Introduction 
As our company expands, so does the need for secure, high-availability storage across departments and offices. This lab focuses on building a private Azure Blob Storage solution to store internal documents, restrict access to partners, and back up public website content—all while optimizing costs through lifecycle management.

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fja2fs5vw9deho7puuebo.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fja2fs5vw9deho7puuebo.png" alt="Architectural Diagram" width="679" height="511"></a>

What You'll Learn

<ul>
<li>How to create private Azure storage for internal documents</li>
<li>How to configure geo-redundancy for high availability</li>
<li>How to share a file securely with a partner</li>
<li>How to set up a backup for public website content</li>
<li>How to implement lifecycle rules to reduce storage costs</li>
</ul>

Architecture Overview 
The setup includes:

<ul>
<li>One Storage Account with Geo-redundant Storage (GRS)</li>
<li>Two Blob Containers:</li>
<li>private for internal documents</li>
<li>backup for public website replication</li>
</ul>

Note: These instruction require you to have completed the lab in my previous post - <a href="https://dev.to/ojosamuel129/building-a-high-availability-azure-blob-storage-for-a-public-website-4f0p">Building a High-Availability Azure Blob Storage for a Public Website</a>.

Skilling tasks

<ul>
<li>Create a storage account for the company private documents.</li>
<li>Configure redundancy for the storage account.</li>
<li>Configure a shared access signature so partners have restricted access to a file.</li>
<li>Back up the public website storage.</li>
<li>Implement lifecycle management to move content to the cool tier.</li>
</ul>

Create a storage account and configure high availability. 
1. Create a storage account for the internal private company documents.

<ul>
<li>In the portal, search for and select Storage accounts. 
<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fejlk6i89wqj4wv80f7y5.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fejlk6i89wqj4wv80f7y5.png" alt="Resource Group" width="800" height="574"></a></li>
<li>Select + Create. 
<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fq9tkvownwcds2ifkf46g.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fq9tkvownwcds2ifkf46g.png" alt="Create Resource Group" width="800" height="576"></a></li>
<li>Select the Resource group created in the previous lab.</li>
<li>Set the Storage account name to <code>private</code>. Add an identifier to the name to ensure the name is unique.</li>
<li>Select Review, and then Create the storage account.</li>
</ul>

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fndk09uxiaqfm3stzywue.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fndk09uxiaqfm3stzywue.png" alt="Review" width="800" height="575"></a> 



<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fti0k12azisguh5ovcb2a.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fti0k12azisguh5ovcb2a.png" alt="Create" width="800" height="577"></a>

<ul>
<li>Wait for the storage account to deploy, and then select Go to resource.</li>
</ul>

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fl25u6c191urnke5aqw3k.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fl25u6c191urnke5aqw3k.png" alt="Go to resource" width="800" height="577"></a>

<ol>
<li>This storage requires high availability if there’s a regional outage. Read access in the secondary region is not required. Configure the appropriate level of redundancy.</li>
</ol>

<ul>
<li>In the storage account, in the Data management section, select the Redundancy blade.</li>
<li>Ensure Geo-redundant storage (GRS) is selected.</li>
<li>
Refresh the page.</li>
<li>Review the primary and secondary location information.</li>
<li>
Save your changes.</li>
</ul>

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fbvm8lwdze80jp0b47b6t.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fbvm8lwdze80jp0b47b6t.png" alt=" " width="800" height="579"></a>

Create a storage container, upload a file, and restrict access to the file.

<ol>
<li>Create a private storage container for the corporate data.</li>
</ol>

<ul>
<li>In the storage account, in the Data storage section, select the Containers blade.</li>
<li>Select + Container.</li>
<li>Ensure the Name of the container is <code>private</code>.</li>
<li>Ensure the Public access level is Private (no anonymous access).</li>
<li>As you have time, review the Advanced settings, but take the defaults.</li>
<li>Select Create.</li>
</ul>

<ol>
<li>For testing, upload a file to the private container. he type of file doesn’t matter. A small image or text file is a good choice. Test to ensure the file isn’t publicly accessible.</li>
</ol>

<ul>
<li>Select the container.</li>
<li>Select Upload.</li>
<li>
Browse to files and select a file.</li>
<li>
Upload the file.</li>
<li>Select the uploaded file.</li>
<li>On the Overview tab, copy the URL.</li>
<li>Paste the URL into a new browser tab.</li>
<li>Verify the file doesn’t display and you receive an error.</li>
</ul>

3. An external partner requires read and write access to the file for at least the next 24 hours. Configure and test a shared access signature (SAS).

<ul>
<li>Select your uploaded blob file and move to the Generate SAS tab.</li>
<li>In the Permissions drop-down, ensure the partner has only Read permissions.</li>
<li>Verify the Start and expiry date/time is for the next 24 hours.</li>
<li>Select Generate SAS token and URL.</li>
<li>Copy the Blob SAS URL to a new browser tab.</li>
<li>Verify you can access the file. If you have uploaded an image file it will display in the browser. Other file types will be downloaded.</li>
</ul>

Configure storage access tiers and content replication.

To save on costs, after 30 days, move blobs from the hot tier to the cool tier.

<ul>
<li>Return to the storage account.</li>
<li>In the Overview section, notice the Default access tier is set to Hot.</li>
<li>In the Data management section, select the L*ifecycle management blade*.</li>
<li>Select Add rule.</li>
<li>Set the Rule name to <code>movetocool</code>.</li>
<li>Set the Rule scope to Apply rule to all blobs in the storage account.</li>
<li>Select Next.</li>
<li>Ensure Last modified is selected.</li>
<li>Set More than (days ago) to 30.</li>
<li>In the Then drop-down select Move to cool storage.</li>
<li>As you have time, review other lifecycle options in the drop-down.</li>
<li>
Add the rule.</li>
</ul>

<ol>
<li>The public website files need to be backed up to another storage account.</li>
</ol>

<ul>
<li>In your storage account, create a new container called <code>backup</code>. Use the default values. Refer back to Lab <a href="https://dev.to/ojosamuel129/building-a-high-availability-azure-blob-storage-for-a-public-website-4f0p">Building a High-Availability Azure Blob Storage for a Public Website</a> if you need detailed instructions.</li>
<li>Navigate to your publicwebsite storage account. This storage account was created in the previous exercise.</li>
</ul>

In the Data management section, select the Object replication blade. 
Select Create replication rules. 
Set the Destination storage account to the private storage account. 
Set the Source container to public and the Destination container to backup. 
Create the replication rule.

<ul>
<li>Optionally, as you have time, upload a file to the public container. Return to the private storage account and refresh the backup container. Within a few minutes your public website file will appear in the backup folder.</li>
</ul>

Conclusion 
This lab demonstrates how to build a secure, high-availability Azure Blob Storage solution for internal use while supporting external collaboration and public content backup. With lifecycle rules and object replication in place, the setup is both resilient and cost-effective.

Thanks for reading—see you in the next one

مع نمو الشركات، تزداد الحاجة إلى حلول تخزين آمنة وموثوقة. يتناول هذا المقال إنشاء نظام تخزين خاص بـ Azure Blob مصمم للنسخ الاحتياطي الداخلي، مما يعزز الأمان من خلال تقييد الوصول ويعمل على تحسين التكاليف من خلال إدارة فعالة لدورة الحياة. هذه المبادرة ضرورية لضمان تخزين الوثائق المهمة ومحتوى الموقع الإلكتروني بشكل آمن وسهل الاسترجاع، مما يمثل خطوة مهمة نحو تحسين الكفاءة التنظيمية.

A medida que las empresas crecen, también lo hace la necesidad de soluciones de almacenamiento seguras y confiables. Este artículo aborda la creación de un sistema de almacenamiento privado de Azure Blob diseñado para copias de seguridad internas, que no solo mejora la seguridad al restringir el acceso, sino que también optimiza los costos a través de una gestión efectiva del ciclo de vida. Esta iniciativa es crucial para garantizar que los documentos importantes y el contenido del sitio web se almacenen de manera segura y sean fácilmente recuperables, lo que representa un avance significativo para la eficiencia organizacional.

À mesure que les entreprises se développent, le besoin de solutions de stockage sécurisées et fiables augmente également. Cet article traite de la création d'un système de stockage Azure Blob privé conçu pour les sauvegardes internes, qui améliore non seulement la sécurité en restreignant l'accès, mais optimise également les coûts grâce à une gestion efficace du cycle de vie. Cette initiative est cruciale pour garantir que les documents importants et le contenu du site Web soient stockés en toute sécurité et facilement récupérables, ce qui constitue un pas en avant significatif pour l'efficacité organisationnelle.

As companies grow, so does the need for secure and reliable storage solutions. This article discusses the creation of a private Azure Blob Storage system designed for internal backups, which not only enhances security by restricting access but also optimizes costs through effective lifecycle management. This initiative is crucial for ensuring that important documents and website content are safely stored and easily retrievable, making it a significant step forward for organizational efficiency.

Creating Private, High-Availability Azure Blob Storage for Internal Backups

<A HREF="https://www.reuters.com/investigations/meta-is-earning-fortune-deluge-fraudulent-ads-documents-show-2025-11-06/"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251106/i19.jpg"></A>
<A HREF="http://www.techmeme.com/251106/p19#a251106p19" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Jeff Horwitz / <A HREF="http://www.reuters.com/">Reuters</A>: 
<A HREF="https://www.reuters.com/investigations/meta-is-earning-fortune-deluge-fraudulent-ads-documents-show-2025-11-06/">Internal documents: Meta projected in December 2024 that 10.1% of its annual ad revenue, or $16B, came from fraudulent ads, including $7B from &ldquo;higher risk&rdquo; ads</A>&nbsp; &mdash;&nbsp; Meta projected 10% of its 2024 revenue would come from ads for scams and banned goods, documents seen by Reuters show.

تكشف الوثائق الداخلية الأخيرة أن شركة ميتا توقعت في ديسمبر 2024 أن 10.1% من إيراداتها السنوية من الإعلانات، والتي تبلغ 16 مليار دولار، جاءت من إعلانات احتيالية. ويشمل ذلك 7 مليارات دولار من ما تصفه بالإعلانات 'عالية المخاطر'. تثير هذه الحالة مخاوف كبيرة بشأن نزاهة الإعلانات عبر الإنترنت وتأثيرها المحتمل على المعلنين والمستخدمين على حد سواء، مما يبرز الحاجة الملحة إلى تنظيم ورقابة أفضل في مجال الإعلانات الرقمية.

Documentos internos recientes revelan que Meta proyectó en diciembre de 2024 que un asombroso 10.1% de sus ingresos anuales por publicidad, que ascienden a 16 mil millones de dólares, provenía de anuncios fraudulentos. Esto incluye 7 mil millones de dólares de lo que clasifican como anuncios de 'alto riesgo'. Esta situación plantea preocupaciones significativas sobre la integridad de la publicidad en línea y el impacto potencial en anunciantes y usuarios, destacando la necesidad urgente de una mejor regulación y supervisión en el espacio publicitario digital.

Des documents internes récents révèlent que Meta a projeté en décembre 2024 qu'un incroyable 10,1 % de ses revenus publicitaires annuels, soit 16 milliards de dollars, provenait d'annonces frauduleuses. Cela inclut 7 milliards de dollars provenant de ce qu'ils classifient comme des annonces 'à risque élevé'. Cette situation soulève d'importantes préoccupations concernant l'intégrité de la publicité en ligne et l'impact potentiel sur les annonceurs et les utilisateurs, soulignant le besoin urgent d'une meilleure réglementation et d'une surveillance dans le domaine de la publicité numérique.

Recent internal documents reveal that Meta projected in December 2024 that a staggering 10.1% of its annual ad revenue, amounting to $16 billion, was derived from fraudulent ads. This includes $7 billion from what they classify as 'higher risk' ads. This situation raises significant concerns about the integrity of online advertising and the potential impact on advertisers and users alike, highlighting the urgent need for better regulation and oversight in the digital advertising space.

Internal documents: Meta projected in December 2024 that 10.1% of its annual ad revenue, or $16B, came from fraudulent ads, including $7B from "higher risk" ads (Jeff Horwitz/Reuters)

ABR's state-space network chip can power real-time speech AI at ultra-low power.
The post <a href="https://www.eetimes.com/applied-brain-research-demos-first-silicon-for-state-space-models/">Applied Brain Research Demos First Silicon For State-Space Models</a> appeared first on <a href="https://www.eetimes.com">EE Times</a>.

قدمت Applied Brain Research شريحة الشبكة الخاصة بها التي تعتمد على نماذج الحالة، والتي من المقرر أن تحدث ثورة في الذكاء الاصطناعي للكلام في الوقت الحقيقي من خلال العمل باستهلاك طاقة منخفض للغاية. هذه الابتكار مهم لأنه يفتح آفاقًا جديدة لتطبيقات الذكاء الاصطناعي الموفرة للطاقة، مما يجعل التعرف المتقدم على الكلام أكثر سهولة واستدامة. يبرز هذا التطور إمكانيات التكنولوجيا الأكثر ذكاءً التي يمكن أن تعمل بفعالية دون استنزاف الموارد.

Applied Brain Research ha presentado su innovador chip de red de estado-espacio, que promete revolucionar la IA de voz en tiempo real al funcionar con un consumo de energía ultra-bajo. Esta innovación es significativa porque abre nuevas posibilidades para aplicaciones de IA eficientes en energía, haciendo que el reconocimiento de voz avanzado sea más accesible y sostenible. El desarrollo destaca el potencial de una tecnología más inteligente que puede operar de manera efectiva sin agotar los recursos.

Applied Brain Research a dévoilé sa puce réseau à état d'espace révolutionnaire, qui devrait transformer l'IA vocale en temps réel en fonctionnant à ultra-basse consommation. Cette innovation est importante car elle ouvre de nouvelles possibilités pour des applications d'IA écoénergétiques, rendant la reconnaissance vocale avancée plus accessible et durable. Le développement souligne le potentiel d'une technologie plus intelligente qui peut fonctionner efficacement sans épuiser les ressources.

Applied Brain Research has unveiled its groundbreaking state-space network chip, which is set to revolutionize real-time speech AI by operating at ultra-low power. This innovation is significant as it opens up new possibilities for energy-efficient AI applications, making advanced speech recognition more accessible and sustainable. The development highlights the potential for smarter technology that can operate effectively without draining resources.

Applied Brain Research Demos First Silicon For State-Space Models

Diffusion models already power AI image generators, but Inception thinks they can be even more powerful applied in software development.

نجحت شركة Inception في جمع 50 مليون دولار لتطوير نماذج الانتشار التي تهدف إلى تحسين توليد الشيفرة والنصوص. يعتبر هذا التمويل مهمًا لأنه يبرز الإمكانات المتزايدة لنماذج الانتشار خارج استخدامها الحالي في توليد الصور بواسطة الذكاء الاصطناعي، مما يشير إلى أنها قد تحدث ثورة في تطوير البرمجيات. من خلال الاستفادة من هذه التكنولوجيا، تهدف Inception إلى إنشاء أدوات أكثر كفاءة وقوة للمطورين، مما قد يؤدي إلى تقدم في كيفية إنشاء البرمجيات وتحسينها.

Inception ha recaudado con éxito 50 millones de dólares para desarrollar modelos de difusión destinados a mejorar la generación de código y texto. Este financiamiento es significativo ya que destaca el creciente potencial de los modelos de difusión más allá de su uso actual en la generación de imágenes por IA, sugiriendo que podrían revolucionar el desarrollo de software. Al aprovechar esta tecnología, Inception busca crear herramientas más eficientes y poderosas para los desarrolladores, lo que podría llevar a avances en la forma en que se crean y optimizan los software.

Inception a réussi à lever 50 millions de dollars pour développer des modèles de diffusion destinés à améliorer la génération de code et de texte. Ce financement est important car il souligne le potentiel croissant des modèles de diffusion au-delà de leur utilisation actuelle dans la génération d'images par IA, suggérant qu'ils pourraient révolutionner le développement logiciel. En tirant parti de cette technologie, Inception vise à créer des outils plus efficaces et puissants pour les développeurs, ce qui pourrait entraîner des avancées dans la manière dont les logiciels sont créés et optimisés.

Inception has successfully raised $50 million to develop diffusion models aimed at enhancing code and text generation. This funding is significant as it highlights the growing potential of diffusion models beyond their current use in AI image generation, suggesting they could revolutionize software development. By leveraging this technology, Inception aims to create more efficient and powerful tools for developers, which could lead to advancements in how software is created and optimized.

Inception raises $50 million to build diffusion models for code and text

<a href="https://cloud.google.com/?hl=en">Google Cloud</a> is introducing what it calls its most powerful artificial intelligence infrastructure to date, unveiling a seventh-generation <a href="https://cloud.google.com/tpu?hl=en">Tensor Processing Unit</a> and expanded <a href="https://cloud.google.com/discover/what-are-arm-based-processors?hl=en">Arm-based computing options</a> designed to meet surging demand for AI model deployment — what the company characterizes as a fundamental industry shift from training models to serving them to billions of users.The announcement, made Thursday, centers on <a href="https://cloud.google.com/resources/ironwood-tpu-interest?utm_source=cgc-blog&amp;utm_medium=blog&amp;utm_campaign=FY25-Q2-global-ENT33820-website-cs-ironwood-tpu-interest&amp;utm_content=ironwood_announcement_blog&amp;utm_term=ironwood&amp;hl=en">Ironwood</a>, Google&#x27;s latest custom AI accelerator chip, which will become generally available in the coming weeks. In a striking validation of the technology, <a href="https://www.anthropic.com/">Anthropic</a>, the AI safety company behind the Claude family of models, disclosed plans to access up to <a href="https://www.googlecloudpresscorner.com/2025-10-23-Anthropic-to-Expand-Use-of-Google-Cloud-TPUs-and-Services">one million of these TPU chips</a> — a commitment worth tens of billions of dollars and among the largest known AI infrastructure deals to date.The move underscores an intensifying competition among cloud providers to control the infrastructure layer powering artificial intelligence, even as questions mount about whether the industry can sustain its current pace of capital expenditure. Google&#x27;s approach — building custom silicon rather than relying solely on <a href="https://www.reuters.com/business/nvidia-poised-record-5-trillion-market-valuation-2025-10-29/">Nvidia&#x27;s dominant GPU chips</a> — amounts to a long-term bet that vertical integration from chip design through software will deliver superior economics and performance.<h3>Why companies are racing to serve AI models, not just train them</h3>Google executives framed the announcements around what they call &quot;the age of inference&quot; — a transition point where companies shift resources from training frontier AI models to deploying them in production applications serving millions or billions of requests daily.&quot;Today&#x27;s frontier models, including Google&#x27;s Gemini, Veo, and Imagen and Anthropic&#x27;s Claude train and serve on Tensor Processing Units,&quot; said Amin Vahdat, vice president and general manager of AI and Infrastructure at Google Cloud. &quot;For many organizations, the focus is shifting from training these models to powering useful, responsive interactions with them.&quot;This transition has profound implications for infrastructure requirements. Where training workloads can often tolerate batch processing and longer completion times, inference — the process of actually running a trained model to generate responses — demands consistently low latency, high throughput, and unwavering reliability. A chatbot that takes 30 seconds to respond, or a coding assistant that frequently times out, becomes unusable regardless of the underlying model&#x27;s capabilities.Agentic workflows — where AI systems take autonomous actions rather than simply responding to prompts — create particularly complex infrastructure challenges, requiring tight coordination between specialized AI accelerators and general-purpose computing.<h3>Inside Ironwood&#x27;s architecture: 9,216 chips working as one supercomputer</h3><a href="https://cloud.google.com/resources/ironwood-tpu-interest?utm_source=cgc-blog&amp;utm_medium=blog&amp;utm_campaign=FY25-Q2-global-ENT33820-website-cs-ironwood-tpu-interest&amp;utm_content=ironwood_announcement_blog&amp;utm_term=ironwood&amp;hl=en">Ironwood</a> is more than incremental improvement over Google&#x27;s sixth-generation TPUs. According to technical specifications shared by the company, it delivers more than four times better performance for both training and inference workloads compared to its predecessor — gains that Google attributes to a system-level co-design approach rather than simply increasing transistor counts.The architecture&#x27;s most striking feature is its scale. A single Ironwood &quot;pod&quot; — a tightly integrated unit of TPU chips functioning as one supercomputer — can connect up to 9,216 individual chips through Google&#x27;s proprietary <a href="https://blog.google/products/google-cloud/ironwood-tpu-age-of-inference/">Inter-Chip Interconnect network</a> operating at 9.6 terabits per second. To put that bandwidth in perspective, it&#x27;s roughly equivalent to downloading the entire Library of Congress in under two seconds.This massive interconnect fabric allows the 9,216 chips to share access to 1.77 petabytes of <a href="https://blog.google/products/google-cloud/ironwood-tpu-age-of-inference/">High Bandwidth Memory</a> — memory fast enough to keep pace with the chips&#x27; processing speeds. That&#x27;s approximately 40,000 high-definition Blu-ray movies&#x27; worth of working memory, instantly accessible by thousands of processors simultaneously. &quot;For context, that means Ironwood Pods can deliver 118x more FP8 ExaFLOPS versus the next closest competitor,&quot; Google stated in technical documentation.The system employs <a href="https://www.opencompute.org/projects/optical-circuit-switching">Optical Circuit Switching</a> technology that acts as a &quot;dynamic, reconfigurable fabric.&quot; When individual components fail or require maintenance — inevitable at this scale — the OCS technology automatically reroutes data traffic around the interruption within milliseconds, allowing workloads to continue running without user-visible disruption.This reliability focus reflects lessons learned from deploying five previous TPU generations. Google reported that its fleet-wide uptime for liquid-cooled systems has maintained approximately 99.999% availability since 2020 — equivalent to less than six minutes of downtime per year.<h3>Anthropic&#x27;s billion-dollar bet validates Google&#x27;s custom silicon strategy</h3>Perhaps the most significant external validation of Ironwood&#x27;s capabilities comes from <a href="https://www.googlecloudpresscorner.com/2025-10-23-Anthropic-to-Expand-Use-of-Google-Cloud-TPUs-and-Services">Anthropic&#x27;s commitment to access up to one million TPU chips</a> — a staggering figure in an industry where even clusters of 10,000 to 50,000 accelerators are considered massive.&quot;Anthropic and Google have a longstanding partnership and this latest expansion will help us continue to grow the compute we need to define the frontier of AI,&quot; said Krishna Rao, Anthropic&#x27;s chief financial officer, in the official partnership agreement. &quot;Our customers — from Fortune 500 companies to AI-native startups — depend on Claude for their most important work, and this expanded capacity ensures we can meet our exponentially growing demand.&quot;According to a separate statement, Anthropic will have access to &quot;well over a gigawatt of capacity coming online in 2026&quot; — enough electricity to power a small city. The company specifically cited TPUs&#x27; &quot;price-performance and efficiency&quot; as key factors in the decision, along with &quot;existing experience in training and serving its models with TPUs.&quot;Industry analysts estimate that a commitment to access one million TPU chips, with associated infrastructure, networking, power, and cooling, likely represents a <a href="https://www.reuters.com/technology/anthropic-expand-use-google-clouds-tpu-chips-2025-10-23/">multi-year contract worth tens of billions of dollars</a> — among the largest known cloud infrastructure commitments in history.James Bradbury, Anthropic&#x27;s head of compute, elaborated on the inference focus: &quot;Ironwood&#x27;s improvements in both inference performance and training scalability will help us scale efficiently while maintaining the speed and reliability our customers expect.&quot;<h3>Google&#x27;s Axion processors target the computing workloads that make AI possible</h3>Alongside <a href="https://cloud.google.com/resources/ironwood-tpu-interest?utm_source=cgc-blog&amp;utm_medium=blog&amp;utm_campaign=FY25-Q2-global-ENT33820-website-cs-ironwood-tpu-interest&amp;utm_content=ironwood_announcement_blog&amp;utm_term=ironwood&amp;hl=en">Ironwood</a>, Google introduced expanded options for its <a href="https://cloud.google.com/products/axion?hl=en">Axion processor family</a> — custom Arm-based CPUs designed for general-purpose workloads that support AI applications but don&#x27;t require specialized accelerators.The <a href="http://forms.gle/HYY5FWRKewYuDMB27">N4A instance type</a>, now entering preview, targets what Google describes as &quot;microservices, containerized applications, open-source databases, batch, data analytics, development environments, experimentation, data preparation and web serving jobs that make AI applications possible.&quot; The company claims N4A delivers up to 2X better price-performance than comparable current-generation x86-based virtual machines.Google is also <a href="https://docs.google.com/forms/d/e/1FAIpQLSd14sMYz79SeRI665dM7lnUbsAg7zilVPdDfK2_6u1vBmiUfg/viewform?usp=send_form">previewing C4A metal</a>, its first bare-metal Arm instance, which provides dedicated physical servers for specialized workloads such as Android development, automotive systems, and software with strict licensing requirements.The Axion strategy reflects a growing conviction that the future of computing infrastructure requires both specialized AI accelerators and highly efficient general-purpose processors. While a TPU handles the computationally intensive task of running an AI model, Axion-class processors manage data ingestion, preprocessing, application logic, API serving, and countless other tasks in a modern AI application stack.Early customer results suggest the approach delivers measurable economic benefits. Vimeo reported observing &quot;a 30% improvement in performance for our core transcoding workload compared to comparable x86 VMs&quot; in initial N4A tests. ZoomInfo measured &quot;a 60% improvement in price-performance&quot; for data processing pipelines running on Java services, according to Sergei Koren, the company&#x27;s chief infrastructure architect.<h3>Software tools turn raw silicon performance into developer productivity</h3>Hardware performance means little if developers cannot easily harness it. Google emphasized that <a href="https://cloud.google.com/resources/ironwood-tpu-interest?utm_source=cgc-blog&amp;utm_medium=blog&amp;utm_campaign=FY25-Q2-global-ENT33820-website-cs-ironwood-tpu-interest&amp;utm_content=ironwood_announcement_blog&amp;utm_term=ironwood&amp;hl=en">Ironwood</a> and <a href="https://cloud.google.com/products/axion?hl=en">Axion</a> are integrated into what it calls <a href="https://cloud.google.com/solutions/ai-hypercomputer">AI Hypercomputer</a> — &quot;an integrated supercomputing system that brings together compute, networking, storage, and software to improve system-level performance and efficiency.&quot;According to an October 2025 IDC Business Value Snapshot study, AI Hypercomputer customers achieved on average 353% three-year return on investment, 28% lower IT costs, and 55% more efficient IT teams.Google disclosed several software enhancements designed to maximize Ironwood utilization. <a href="https://cloud.google.com/kubernetes-engine?utm_source=google&amp;utm_medium=cpc&amp;utm_campaign=na-US-all-en-dr-bkws-all-all-trial-e-dr-1710134&amp;utm_content=text-ad-none-any-DEV_c-CRE_772251321321-ADGP_Hybrid+%7C+BKWS+-+EXA+%7C+Txt-AppMod-GKE-Kubernetes+Engine-KWID_369526655975-kwd-369526655975&amp;utm_term=KW_google+kubernetes+engine-ST_google+kubernetes+engine&amp;gclsrc=aw.ds&amp;gad_source=1&amp;gad_campaignid=23052915519&amp;gclid=Cj0KCQiAiKzIBhCOARIsAKpKLAMMFmNaZgmWnQ3CYrziXElfmMXmQphYqoSvICvf6jfUjLqR9XqFt3oaArkYEALw_wcB&amp;hl=en">Google Kubernetes Engine</a> now offers advanced maintenance and topology awareness for TPU clusters, enabling intelligent scheduling and highly resilient deployments. The company&#x27;s <a href="https://github.com/AI-Hypercomputer/maxtext">open-source MaxText framework</a> now supports advanced training techniques including Supervised Fine-Tuning and Generative Reinforcement Policy Optimization.Perhaps most significant for production deployments, Google&#x27;s <a href="https://docs.cloud.google.com/kubernetes-engine/docs/concepts/about-gke-inference-gateway">Inference Gateway</a> intelligently load-balances requests across model servers to optimize critical metrics. According to Google, it can reduce time-to-first-token latency by 96% and serving costs by up to 30% through techniques like prefix-cache-aware routing.The <a href="https://docs.cloud.google.com/kubernetes-engine/docs/concepts/about-gke-inference-gateway">Inference Gateway</a> monitors key metrics including KV cache hits, GPU or TPU utilization, and request queue length, then routes incoming requests to the optimal replica. For conversational AI applications where multiple requests might share context, routing requests with shared prefixes to the same server instance can dramatically reduce redundant computation.<h3>The hidden challenge: powering and cooling one-megawatt server racks</h3>Behind these announcements lies a massive physical infrastructure challenge that Google addressed at the recent <a href="https://www.opencompute.org/summit/emea-summit">Open Compute Project EMEA Summit</a>. The company disclosed that it&#x27;s implementing +/-400 volt direct current power delivery capable of supporting up to one megawatt per rack — a tenfold increase from typical deployments.&quot;The AI era requires even greater power delivery capabilities,&quot; explained Madhusudan Iyengar and Amber Huffman, Google principal engineers, in an <a href="https://cloud.google.com/blog/topics/systems/enabling-1-mw-it-racks-and-liquid-cooling-at-ocp-emea-summit">April 2025 blog post</a>. &quot;ML will require more than 500 kW per IT rack before 2030.&quot;Google is collaborating with Meta and Microsoft to standardize electrical and mechanical interfaces for high-voltage DC distribution. The company selected <a href="https://www.opencompute.org/files/OCP18-400VDC-Efficiency-02.pdf">400 VDC</a> specifically to leverage the supply chain established by electric vehicles, &quot;for greater economies of scale, more efficient manufacturing, and improved quality and scale.&quot;On cooling, Google revealed it will contribute its fifth-generation cooling distribution unit design to the Open Compute Project. The company has deployed liquid cooling &quot;at GigaWatt scale across more than 2,000 TPU Pods in the past seven years&quot; with fleet-wide availability of approximately 99.999%.Water can transport approximately 4,000 times more heat per unit volume than air for a given temperature change — critical as individual AI accelerator chips increasingly dissipate 1,000 watts or more.<h3>Custom silicon gambit challenges Nvidia&#x27;s AI accelerator dominance</h3>Google&#x27;s announcements come as the AI infrastructure market reaches an inflection point. While Nvidia maintains overwhelming dominance in AI accelerators — holding an estimated 80-95% market share — cloud providers are increasingly investing in custom silicon to differentiate their offerings and improve unit economics.Amazon Web Services pioneered this approach with <a href="https://aws.amazon.com/ec2/graviton/">Graviton Arm-based CPUs</a> and <a href="https://aws.amazon.com/ai/machine-learning/inferentia/">Inferentia</a> / <a href="https://aws.amazon.com/ai/machine-learning/trainium/">Trainium</a> AI chips. Microsoft has developed <a href="https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/cobalt-overview">Cobalt processors</a> and is reportedly working on AI accelerators. Google now offers the most comprehensive custom silicon portfolio among major cloud providers.The strategy faces inherent challenges. Custom chip development requires enormous upfront investment — often billions of dollars. The software ecosystem for specialized accelerators lags behind Nvidia&#x27;s <a href="https://developer.nvidia.com/about-cuda">CUDA platform</a>, which benefits from 15+ years of developer tools. And rapid AI model architecture evolution creates risk that custom silicon optimized for today&#x27;s models becomes less relevant as new techniques emerge.Yet Google argues its approach delivers unique advantages. &quot;This is how we built the first TPU ten years ago, which in turn unlocked the invention of the Transformer eight years ago — the very architecture that powers most of modern AI,&quot; the company noted, referring to the seminal <a href="https://arxiv.org/abs/1706.03762">&quot;Attention Is All You Need&quot; paper</a> from Google researchers in 2017.The argument is that tight integration — &quot;model research, software, and hardware development under one roof&quot; — enables optimizations impossible with off-the-shelf components.Beyond Anthropic, several other customers provided early feedback. Lightricks, which develops creative AI tools, reported that early Ironwood testing &quot;makes us highly enthusiastic&quot; about creating &quot;more nuanced, precise, and higher-fidelity image and video generation for our millions of global customers,&quot; said Yoav HaCohen, the company&#x27;s research director.Google&#x27;s announcements raise questions that will play out over coming quarters. Can the industry sustain current infrastructure spending, with major AI companies collectively committing hundreds of billions of dollars? Will custom silicon prove economically superior to Nvidia GPUs? How will model architectures evolve?For now, Google appears committed to a strategy that has defined the company for decades: building custom infrastructure to enable applications impossible on commodity hardware, then making that infrastructure available to customers who want similar capabilities without the capital investment.As the AI industry transitions from research labs to production deployments serving billions of users, that infrastructure layer — the silicon, software, networking, power, and cooling that make it all run — may prove as important as the models themselves.And if Anthropic&#x27;s willingness to commit to accessing up to one million chips is any indication, Google&#x27;s bet on custom silicon designed specifically for the age of inference may be paying off just as demand reaches its inflection point.

أطلقت Google Cloud أحدث بنية تحتية للذكاء الاصطناعي لديها، حيث قدمت وحدة معالجة Tensor من الجيل السابع التي تعد بزيادة أداء تصل إلى أربعة أضعاف. تأتي هذه الابتكارات في وقت حاسم حيث تزداد الطلبات على نشر نماذج الذكاء الاصطناعي. بالإضافة إلى ذلك، حصلت Google على صفقة كبيرة مع Anthropic بقيمة مليارات الدولارات، مما يعزز مكانتها في مشهد الذكاء الاصطناعي التنافسي. هذا التطور مهم ليس فقط لـ Google ولكن أيضًا لصناعة التكنولوجيا بشكل عام، حيث يسلط الضوء على التقدم السريع في تكنولوجيا الذكاء الاصطناعي والاعتماد المتزايد على حلول الحوسبة القوية.

Google Cloud ha lanzado su infraestructura de IA más avanzada hasta la fecha, presentando una unidad de procesamiento tensorial de séptima generación que promete un aumento de rendimiento de cuatro veces. Esta innovación llega en un momento crucial, ya que la demanda de implementación de modelos de IA está en auge. Además, Google ha asegurado un acuerdo significativo con Anthropic por valor de miles de millones, consolidando aún más su posición en el competitivo panorama de la IA. Este desarrollo es importante no solo para Google, sino también para la industria tecnológica en general, ya que destaca los rápidos avances en la tecnología de IA y la creciente dependencia de soluciones informáticas potentes.

Google Cloud vient de lancer son infrastructure IA la plus avancée à ce jour, avec un processeur de traitement tensoriel de septième génération offrant un quadruplement des performances. Cette innovation arrive à un moment crucial alors que la demande pour le déploiement de modèles IA explose. De plus, Google a sécurisé un contrat significatif avec Anthropic d'une valeur de plusieurs milliards, renforçant ainsi sa position dans le paysage concurrentiel de l'IA. Ce développement est important non seulement pour Google, mais aussi pour l'ensemble de l'industrie technologique, car il met en lumière les avancées rapides de la technologie IA et la dépendance croissante à des solutions informatiques puissantes.

Google Cloud has just launched its most advanced AI infrastructure yet, featuring a seventh-generation Tensor Processing Unit that promises a fourfold performance boost. This innovation comes at a crucial time as demand for AI model deployment skyrockets. Additionally, Google has secured a significant deal with Anthropic worth billions, further solidifying its position in the competitive AI landscape. This development is important not only for Google but also for the broader tech industry, as it highlights the rapid advancements in AI technology and the increasing reliance on powerful computing solutions.

Google debuts AI chips with 4X performance boost, secures Anthropic megadeal worth billions

If your Linux distro still lets you log in as root, here's why you should never do it.

تسجيل الدخول كجذر في نظام لينكس يمكن أن يؤدي إلى مخاطر أمنية كبيرة، حيث يمنح المستخدمين وصولاً غير مقيد إلى النظام. يسلط هذا المقال الضوء على مخاطر مثل هذه الممارسات ويؤكد على أهمية استخدام حسابات المستخدمين العادية لحماية بياناتك وسلامة النظام. فهم هذه المخاطر أمر بالغ الأهمية لأي شخص يستخدم لينكس، حيث يساعد في منع الكوارث المحتملة التي قد تعرض المعلومات الحساسة للخطر.

Iniciar sesión como root en Linux puede conllevar riesgos de seguridad significativos, ya que otorga a los usuarios acceso sin restricciones al sistema. Este artículo destaca los peligros de tales prácticas y enfatiza la importancia de utilizar cuentas de usuario estándar para proteger tus datos y la integridad del sistema. Comprender estos riesgos es crucial para cualquier persona que use Linux, ya que ayuda a prevenir posibles desastres que podrían comprometer información sensible.

Se connecter en tant que root sur Linux peut entraîner des risques de sécurité importants, car cela donne aux utilisateurs un accès illimité au système. Cet article met en lumière les dangers de telles pratiques et souligne l'importance d'utiliser des comptes d'utilisateur standard pour protéger vos données et l'intégrité du système. Comprendre ces risques est crucial pour quiconque utilise Linux, car cela aide à prévenir des catastrophes potentielles qui pourraient compromettre des informations sensibles.

Logging in as root on Linux can lead to significant security risks, as it gives users unrestricted access to the system. This article highlights the dangers of such practices and emphasizes the importance of using standard user accounts to protect your data and system integrity. Understanding these risks is crucial for anyone using Linux, as it helps prevent potential disasters that could compromise sensitive information.

Logging in as root on Linux? Here's why that disaster waiting to happen

هذا البرنامج التعليمي هو فرصة رائعة للمطورين الذين يتطلعون إلى تعزيز مهاراتهم من خلال بناء موقع محفظة مذهل باستخدام تقنيات حديثة مثل ReactJS وTailwind CSS. يوجهك خلال كل خطوة، مما يضمن لك إنشاء موقع متجاوب بالكامل يعرض عملك بشكل فعال. هذا مهم لأن المحفظة المصممة بشكل جيد يمكن أن تؤثر بشكل كبير على فرصك في العمل وتساعدك على التميز في صناعة التكنولوجيا التنافسية.

Este tutorial es una oportunidad fantástica para los desarrolladores que buscan mejorar sus habilidades al construir un impresionante sitio web de portafolio utilizando tecnologías modernas como ReactJS y Tailwind CSS. Te guía a través de cada paso, asegurando que crees un sitio completamente responsivo que muestre tu trabajo de manera efectiva. Esto es importante porque un portafolio bien diseñado puede tener un impacto significativo en tus perspectivas laborales y ayudarte a destacar en la competitiva industria tecnológica.

Ce tutoriel est une excellente occasion pour les développeurs souhaitant améliorer leurs compétences en créant un site web de portfolio impressionnant avec des technologies modernes comme ReactJS et Tailwind CSS. Il vous guide à travers chaque étape, garantissant que vous créiez un site entièrement responsive qui met en valeur votre travail de manière efficace. Cela est important car un portfolio bien conçu peut avoir un impact significatif sur vos perspectives d'emploi et vous aider à vous démarquer dans l'industrie technologique compétitive.

This tutorial is a fantastic opportunity for developers looking to enhance their skills by building a stunning portfolio website using modern technologies like ReactJS and Tailwind CSS. It guides you through each step, ensuring you create a fully responsive site that showcases your work effectively. This is important because a well-designed portfolio can significantly impact your job prospects and help you stand out in the competitive tech industry.

Build an AWESOME React Portfolio Website (Coding Bootcamp)

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fvwrulkcwcjlld7gfnlh9.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fvwrulkcwcjlld7gfnlh9.png" alt="Cost Optimization in Azure: Using the FinOps Toolkit to Save Money" width="800" height="450"></a>

Cost optimisation. It’s important in all walks of life, whether you are looking after your household bills or your cloud environment. 

When we look at Azure there are tools and mechanisms that can help you monitor your costs, from <a href="https://learn.microsoft.com/azure/advisor/advisor-get-started?ref=techielass.com" rel="noopener noreferrer">Azure Advisor</a> to Cost Alerts.

Within this blog post we’ll look at some of the resources with the <a href="https://microsoft.github.io/finops-toolkit/?ref=techielass.com#-available-tools" rel="noopener noreferrer">Azure FinOps toolkit</a> that can be deployed to help keep a monitor of your costs. 

<h2>
 
 
 What Is the Azure FinOps Toolkit for cost optimization in Azure?
</h2>

The <a href="https://microsoft.github.io/finops-toolkit/?ref=techielass.com#-available-tools" rel="noopener noreferrer">Azure FinOps toolkit</a> is a set of resources that can help you manage your Azure Costs and <a href="https://www.techielass.com/tag/finops/" rel="noopener noreferrer">FinOps</a> journey. There are alerts, <a href="https://www.techielass.com/azure-dashboards-azure-workbooks-power-bi/" rel="noopener noreferrer">workbooks</a> and <a href="https://www.techielass.com/tag/powerbi/" rel="noopener noreferrer">PowerBI</a> reports that can be deployed. 

It is an open-source project that is maintained and owned by Microsoft but the community over the years has also contributed to it. 

There are two resources within the toolkit that I would recommend you look at as the very minimum you can do for cost optimisation. 

<h2>
 
 
 How to use the FinOps Toolkit Alerts for Azure Cost Savings
</h2>

Within the toolkit is something called “FinOps alerts”. This resource is a Logic App than can be scheduled to run regularly and check if you have any of the following that might be costing you money unnecessarily: 

<ul>
<li>Idle App Gateways</li>
<li>Idle disks</li>
<li>Idle IP Address</li>
<li>Idle Load Balancers</li>
<li>Disk snapshots older than 30 days</li>
<li>Stopped virtual machines</li>
</ul>

The Logic App checks the subscriptions you tell it to and if it finds any of the above resources it generates an email that you can send to a person in your team or your IT Service Desk to action. 

It’s a light weight deployment that can be up and running within 5-10minutes. 

Let’s walk you through the deployment. 

The first step is to visit: <a href="https://microsoft.github.io/finops-toolkit/alerts?ref=techielass.com#deploy" rel="noopener noreferrer">https://microsoft.github.io/finops-toolkit/alerts#deploy</a> 

Click on the correct Deploy button on the website, in this example I am selecting Deploy to Azure.

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F9lzxmd3x7czzmsqazpc9.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F9lzxmd3x7czzmsqazpc9.png" alt="Cost Optimization in Azure: Using the FinOps Toolkit to Save Money" width="800" height="450"></a> 
Azure FinOps Toolkit

This will launch the Azure ARM template custom deployment within the Azure portal. You will be asked which subscription, resource group, Azure region, and the name you wish to use for the Logic App. 

Once you’ve filled in the information click on Review + Create. Then click on Create.

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fcawtgj4kaja24kpk2u8m.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fcawtgj4kaja24kpk2u8m.png" alt="Cost Optimization in Azure: Using the FinOps Toolkit to Save Money" width="800" height="450"></a> 
Azure Portal custom deployment

It will take a few minutes for the deployment to complete. Once it has there are some things you need to configure for the Logic App to work. 

Go into the Logic App and click on Edit. 

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fl057y3z49xtrt8ltj3e0.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fl057y3z49xtrt8ltj3e0.png" alt="Cost Optimization in Azure: Using the FinOps Toolkit to Save Money" width="800" height="450"></a> 
Azure Logic App

If you click on the Recurrence step you can configure how often you want the Logic App to run, by default it will run once a week. 

Click on the Set alert recipient step. Within the value section, enter the email address where you want the email to be sent. 

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F5c4gdylw1w07lhpwoija.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F5c4gdylw1w07lhpwoija.png" alt="Cost Optimization in Azure: Using the FinOps Toolkit to Save Money" width="800" height="450"></a> 
Azure Logic App

Now click on Included subscriptions , within the value section enter the subscription ID or IDs you want the Logic App to scan. 

The format for just one subscription should be: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>["12345678-aaaa-bbbb-cccc-1234567890ab"]

</code></pre>

</div>



The format for multiple subscription IDs should be: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>[
 "11111111-1111-1111-1111-111111111111",
 "22222222-2222-2222-2222-222222222222",
 "33333333-3333-3333-3333-333333333333"
]

</code></pre>

</div>



<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fvshyrghq3m3ky58z3pvk.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fvshyrghq3m3ky58z3pvk.png" alt="Cost Optimization in Azure: Using the FinOps Toolkit to Save Money" width="800" height="450"></a> 
Azure Logic App

If there are any subscriptions you’d like to exclude from being scanned, make sure you enter that information within the Excluded subscriptions. 

Next scroll down to the bottom of the Logic App and click on the Send an email (v2) step. Click on change connection , then select ** Add new**. It will ask you to sign into your M365 account and confirm the connection with your M365 to be able to send the email. 

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fk4j1lp12xmbe7rfiw6oa.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fk4j1lp12xmbe7rfiw6oa.png" alt="Cost Optimization in Azure: Using the FinOps Toolkit to Save Money" width="800" height="450"></a> 
Azure Logic App

Don’t forget to click on Save after you have made the changes. 

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fl74paotau08e4tqd2p3y.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fl74paotau08e4tqd2p3y.png" alt="Cost Optimization in Azure: Using the FinOps Toolkit to Save Money" width="800" height="450"></a> 
Azure Logic App

You can now manually start the Logic App by clicking on Run. 

<h2>
 
 
 Analysing Azure Costs with FinOps Toolkit Workbooks
</h2>

Azure workbooks can provide an interactive way to view and analyse data. Within the Azure FinOps toolkit are two workbooks, one called Governance and one called Optimisation. 

The Optimisation workbook gives you a comprehensive overview of your Azure environment's resource usage, aligning with the Well-Architected Framework Cost Optimisation pillar.

The Governance one provides a comprehensive overview of the governance posture of your Azure environment. 

To deploy these workbooks, head over to <a href="https://microsoft.github.io/finops-toolkit/workbooks?ref=techielass.com" rel="noopener noreferrer">https://microsoft.github.io/finops-toolkit/workbooks</a> 

Click on the deploy button for you, in this example, I am going to select Deploy to Azure. 

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fp0i5gytll3rvp8xyiyzm.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fp0i5gytll3rvp8xyiyzm.png" alt="Cost Optimization in Azure: Using the FinOps Toolkit to Save Money" width="800" height="450"></a> 
Azure FinOps Toolkit

This will launch the Azure Portal and the custom Azure ARM template deployment. You’ll be asked to select a subscription, a resource group, an Azure region and a name to prefix each workbook with. 

Click on Review + Create. Then click on Create.

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F89m0jwsqrx9f1pesyb3c.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F89m0jwsqrx9f1pesyb3c.png" alt="Cost Optimization in Azure: Using the FinOps Toolkit to Save Money" width="800" height="450"></a> 
Azure custom deployment within the Azure Portal

It will take a few minutes for the workbooks to be deployed. You can go to the deployed resources and start to view and interact with the workbooks. 

They are great for spotting issues with your governance and also where you can make some cost savings and optimisations within your environment. 

<h2>
 
 
 Final Thoughts on Cost Optimization in Azure
</h2>

The Azure FinOps toolkit is a powerful collection of resources to help you implement FinOps practices and monitor cost optimization in your Azure environment. Even using just the alerts and workbooks, you can quickly identify idle resources, spot opportunities to save money, and keep your cloud spending under control.

Take the time to explore the <a href="https://microsoft.github.io/finops-toolkit/?ref=techielass.com#-available-tools" rel="noopener noreferrer">toolkit</a>, experiment with its features, and tailor it to your organisation’s needs. By proactively monitoring your environment, you’ll not only reduce costs but also gain better visibility into your cloud resources, a key step toward more efficient, sustainable cloud operations.

يتناول المقال كيفية استفادة الشركات من مجموعة أدوات FinOps لتحسين التكاليف في Azure، مشددًا على أهميتها في المناخ الاقتصادي الحالي. من خلال تنفيذ هذه الاستراتيجيات، يمكن للمنظمات تقليل نفقاتها السحابية بشكل كبير مع الحفاظ على الكفاءة. هذا أمر بالغ الأهمية للشركات التي تسعى إلى تحقيق أقصى استفادة من ميزانياتها وتحسين صحتها المالية في سوق تنافسية.

El artículo analiza cómo las empresas pueden aprovechar el Kit de Herramientas FinOps para optimizar costos en Azure, destacando su importancia en el clima económico actual. Al implementar estas estrategias, las organizaciones pueden reducir significativamente sus gastos en la nube mientras mantienen la eficiencia. Esto es crucial para las empresas que buscan maximizar sus presupuestos y mejorar su salud financiera en un mercado competitivo.

L'article traite de la manière dont les entreprises peuvent tirer parti de l'outil FinOps pour optimiser les coûts dans Azure, soulignant son importance dans le climat économique actuel. En mettant en œuvre ces stratégies, les organisations peuvent réduire considérablement leurs dépenses cloud tout en maintenant leur efficacité. Cela est crucial pour les entreprises cherchant à maximiser leurs budgets et à améliorer leur santé financière sur un marché concurrentiel.

The article discusses how businesses can leverage the FinOps Toolkit to optimize costs in Azure, highlighting its importance in today's economic climate. By implementing these strategies, organizations can significantly reduce their cloud expenses while maintaining efficiency. This is crucial for companies looking to maximize their budgets and improve their financial health in a competitive market.

Cost Optimization in Azure: Using the FinOps Toolkit to Save Money

Starting your journey with the Azure Fundamentals AZ-900 exam can open the door to an entry-level cloud role within a couple of months. It acts as your gateway into the Microsoft cloud ecosystem, and the path is quite straightforward if followed correctly.

Here’s a step-by-step approach to prepare for the Microsoft Azure Fundamentals (AZ-900) certification — you can pass it confidently and use it to kickstart your cloud career.

Understand What the AZ-900 Certification Covers

Sam realized early on that the AZ-900 isn’t about mastering deep technical skills. It’s designed to validate your foundational understanding of cloud concepts, Azure core services, governance, pricing, and security. This certification signals to employers that you grasp the basics of Azure.

The exam content is divided into three main parts:

<ul>
<li>Cloud concepts (25–30%)</li>
<li>Azure architecture and core services (35–40%)</li>
<li>Management, governance, security, and pricing (30–35%)</li>
</ul>

Knowing these weightings helps you plan your preparation wisely. Expect 40–60 questions to be answered within 45 minutes (excluding setup time). A score of 700 or more (on Microsoft’s scale) is required to pass.

Build a Study Plan That Fits You

Rather than jumping in, Sam planned his schedule. He booked the test eight weeks ahead and distributed his preparation across that time — two weeks for cloud basics, three weeks for core services, one for governance and security, and the final two for practice tests and revision.

Your plan should mix theory with hands-on labs and regular quizzes. Even dedicating 1–2 hours daily with longer weekend sessions can make a big difference. Many successful candidates followed this steady routine.

Use Microsoft’s Official Learning Resources

Start with Microsoft Learn — the company’s official platform. It offers free learning paths, modules, and guided labs mapped directly to the AZ-900 objectives. 
You can also access Microsoft’s study guide and free practice assessments to track your readiness.

Get Hands-On Experience

Sam didn’t rely on books alone. He launched a free Azure account, practiced creating resource groups, deployed VMs, configured role-based access, and reviewed billing dashboards. 
This hands-on exposure made theory come alive. Many exam questions reflect real-world tasks, so being comfortable in the Azure portal is crucial.

Certification as a Launchpad

Adding the Azure Fundamentals certification to his résumé gave Sam credibility. It helped him apply for junior cloud roles and paved the way toward advanced certifications like Azure Administrator or Azure Developer.

<a href="https://opportunitynearme.com/" rel="noopener noreferrer">Opportunity Near Me</a>, powered by ITE YONIK SERVICES®, provides AZ-900 training at affordable rates for students and working professionals. The programs emphasize practical, job-ready skills that align with current market needs.

Final Thoughts

With consistent effort, you can easily pass the AZ-900 and begin your journey into cloud computing. Start today — your Microsoft cloud career awaits.

يمكن أن يؤدي الاستعداد لامتحان Microsoft Azure Fundamentals (AZ-900) إلى تحسين آفاقك المهنية بشكل كبير في صناعة السحابة. تعتبر هذه الشهادة بمثابة بوابة لدخول وظائف مبتدئة في نظام Microsoft السحابي، مما يجعلها خطوة أساسية لأي شخص يتطلع لدخول هذا المجال. من خلال اتباع نهج منظم للدراسة وفهم متطلبات الامتحان، يمكنك اجتياز AZ-900 بثقة ووضع الأساس لمهنة ناجحة في السحابة.

Prepararse para el examen Microsoft Azure Fundamentals (AZ-900) puede mejorar significativamente tus perspectivas de carrera en la industria de la nube. Esta certificación actúa como una puerta de entrada a roles de nivel inicial en el ecosistema de nube de Microsoft, lo que la convierte en un paso esencial para cualquiera que busque ingresar a este campo. Siguiendo un enfoque estructurado para estudiar y comprendiendo los requisitos del examen, puedes aprobar el AZ-900 con confianza y sentar las bases para una exitosa carrera en la nube.

Se préparer à l'examen Microsoft Azure Fundamentals (AZ-900) peut considérablement améliorer vos perspectives de carrière dans l'industrie du cloud. Cette certification sert de passerelle vers des postes de débutant dans l'écosystème cloud de Microsoft, ce qui en fait une étape essentielle pour quiconque souhaite entrer dans ce domaine. En suivant une approche structurée d'étude et en comprenant les exigences de l'examen, vous pouvez passer l'AZ-900 en toute confiance et poser les bases d'une carrière réussie dans le cloud.

Preparing for the Microsoft Azure Fundamentals (AZ-900) exam can significantly enhance your career prospects in the cloud industry. This certification serves as a gateway to entry-level roles in the Microsoft cloud ecosystem, making it an essential step for anyone looking to break into this field. By following a structured approach to study and understanding the exam's requirements, you can confidently pass the AZ-900 and set the foundation for a successful cloud career.

How to Prepare for Microsoft Azure Fundamentals (AZ-900)

<img width="1920" height="1080" src="https://analyticsindiamag.com/wp-content/uploads/2024/08/Microsoft-Azure-Crushes-Cloud-CompetitionLeaves-AWS-and-Google-Cloud-Behind-1.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="Mircrosoft robotics" style="display: block; margin: auto; margin-bottom: 5px;max-width: 100%;" link_thumbnail="" decoding="async" loading="lazy" srcset="https://analyticsindiamag.com/wp-content/uploads/2024/08/Microsoft-Azure-Crushes-Cloud-CompetitionLeaves-AWS-and-Google-Cloud-Behind-1.jpg 1920w, https://analyticsindiamag.com/wp-content/uploads/2024/08/Microsoft-Azure-Crushes-Cloud-CompetitionLeaves-AWS-and-Google-Cloud-Behind-1-1300x731.jpg 1300w, https://analyticsindiamag.com/wp-content/uploads/2024/08/Microsoft-Azure-Crushes-Cloud-CompetitionLeaves-AWS-and-Google-Cloud-Behind-1-768x432.jpg 768w, https://analyticsindiamag.com/wp-content/uploads/2024/08/Microsoft-Azure-Crushes-Cloud-CompetitionLeaves-AWS-and-Google-Cloud-Behind-1-1536x864.jpg 1536w" sizes="auto, (max-width: 1920px) 100vw, 1920px" />“An industry record made possible by our longstanding co-innovation with NVIDIA and expertise in running AI at production scale,” said Satya Nadella. 
The post <a href="https://analyticsindiamag.com/ai-news-updates/microsoft-sets-inference-record-with-1-1-mlillon-tokens-sec-azure-nd-gb300/">Microsoft Sets Inference Record With 1.1 Mlillon Tokens/Sec Azure ND GB300</a> appeared first on <a href="https://analyticsindiamag.com">Analytics India Magazine</a>.

حققت مايكروسوفت إنجازًا ملحوظًا من خلال تسجيل رقم قياسي في الاستدلال باستخدام Azure ND GB300، حيث عالجت 1.1 مليون توكن في الثانية. يبرز هذا الإنجاز تقدم مايكروسوفت في مجال الحوسبة السحابية ويضع Azure كمنافس قوي أمام AWS وGoogle Cloud. مع اعتماد الشركات بشكل متزايد على خدمات السحابة في عملياتها، قد يؤثر هذا الرقم القياسي على خياراتها، مما قد يؤدي إلى تغيير في ديناميات السوق.

Microsoft ha logrado un hito notable al establecer un récord de inferencia con su Azure ND GB300, procesando una impresionante cantidad de 1.1 millones de tokens por segundo. Este logro no solo destaca los avances de Microsoft en la computación en la nube, sino que también posiciona a Azure como un competidor formidable frente a AWS y Google Cloud. A medida que las empresas dependen cada vez más de los servicios en la nube para sus operaciones, este récord podría influir en sus decisiones, lo que podría llevar a un cambio en la dinámica del mercado.

Microsoft a atteint un jalon remarquable en établissant un record d'inférence avec son Azure ND GB300, traitant une impressionnante quantité de 1,1 million de jetons par seconde. Cet exploit met en lumière les avancées de Microsoft dans le cloud computing et positionne Azure comme un concurrent redoutable face à AWS et Google Cloud. Alors que les entreprises s'appuient de plus en plus sur les services cloud pour leurs opérations, ce record pourrait influencer leurs choix, entraînant potentiellement un changement dans la dynamique du marché.

Microsoft has achieved a remarkable milestone by setting an inference record with its Azure ND GB300, processing an impressive 1.1 million tokens per second. This achievement not only highlights Microsoft's advancements in cloud computing but also positions Azure as a formidable competitor against AWS and Google Cloud. As businesses increasingly rely on cloud services for their operations, this record could influence their choices, potentially leading to a shift in market dynamics.

Microsoft Sets Inference Record With 1.1 Mlillon Tokens/Sec Azure ND GB300

The other day, a friend of mine was complaining that every month, their AWS bill is landing like a small novel: hundreds of line items, multiple accounts, and no clear picture of why costs changed. 
Manually digging through the Cost Explorer, various EC2 dashboards, Route 53 pages etc. was slow and very error-prone.

So, I started thinking, what it takes to build a modular AWS Cost Audit script to collect and summarize cost and usage data automatically. That experiment grew into a reusable FinOps toolkit for cloud teams, which is lightweight, transparent, and CI/CD-friendly.

<h2>
 
 
 🧭 The Cost Problem in AWS
</h2>

AWS gives incredible flexibility — but that also makes it easy to overspend. Idle EC2 instances, forgotten EBS volumes, over-provisioned RDS databases, and cross-region data transfers quietly add up. Even with native tools like Cost Explorer and AWS Budgets, many teams struggle to:

<ul>
<li>Continuously monitor all resources across accounts</li>
<li>Correlate costs with resource usage</li>
<li>Identify optimization opportunities fast enough</li>
</ul>

<blockquote>
Cloud waste isn’t just a finance problem — it’s an engineering visibility shortfall.
</blockquote>

<h2>
 
 
 💡 Enter AWS Cost Audit
</h2>

AWS Cost Audit is a lightweight, automation-ready toolkit that helps you take control of your AWS spend — without complex dashboards or heavy FinOps platforms.

It bridges the gap between AWS billing data and actionable cost insights, complements FinOps principles by enabling shared visibility and data-driven decisions.

It helps teams to:

<ul>
<li>Shift from reactive cost reviews to proactive optimization</li>
<li>Democratize cloud spending data — not just finance’s job</li>
<li>Build a culture of accountability across DevOps, engineering, and product</li>
</ul>

<blockquote>
This isn’t about policing cost — it’s about empowering smarter usage.
</blockquote>

<h2>
 
 
 ⚙️ What It Does
</h2>

<ul>
<li>Audits AWS environment for unused or idle resources</li>
<li>Summarizes spend by service, region, and tag</li>
<li>Highlights cost anomalies and trends</li>
<li>Generates clear, CLI-based reports for visibility or CI integration</li>
<li>Helps teams adopt FinOps practices without adding overhead</li>
</ul>

<h2>
 
 
 🚀 Key Advantages
</h2>

<div class="table-wrapper-paragraph"><table>
<thead>
<tr>
<th>Benefits</th>
<th>Helps with...</th>
</tr>
</thead>
<tbody>
<tr>
<td>Immediate cost visibility</td>
<td>Understanding what’s costing you the most</td>
</tr>
<tr>
<td>Zero external dependencies</td>
<td>Works via CLI, with minimal setup</td>
</tr>
<tr>
<td>Multi-account awareness</td>
<td>Analyze organization-wide cost patterns</td>
</tr>
<tr>
<td>Automation-friendly</td>
<td>Integrate into CI/CD or cron jobs for continuous insights</td>
</tr>
<tr>
<td>FinOps-ready insights</td>
<td>Perfect for monthly cost reviews or reporting</td>
</tr>
</tbody>
</table></div>

<h2>
 
 
 📊 What this Script Collects
</h2>

<div class="table-wrapper-paragraph"><table>
<thead>
<tr>
<th>AWS Service</th>
<th>Data Collected</th>
<th>Why It Matters</th>
</tr>
</thead>
<tbody>
<tr>
<td>EC2</td>
<td>Instances + 7-day CPU avg</td>
<td>Find underutilized compute</td>
</tr>
<tr>
<td>RDS</td>
<td>DB list + Multi-AZ flag</td>
<td>Detect over-redundant DBs</td>
</tr>
<tr>
<td>EBS</td>
<td>Volumes + size</td>
<td>Spot unattached/idle disks</td>
</tr>
<tr>
<td>S3</td>
<td>Bucket inventory + metrics</td>
<td>Track object-store sprawl</td>
</tr>
<tr>
<td>Route 53</td>
<td>Zones, records, cost</td>
<td>Simplify redundant DNS</td>
</tr>
<tr>
<td>Elastic IP</td>
<td>Allocation + attachment state</td>
<td>Catch idle IPs billed hourly</td>
</tr>
<tr>
<td>Tags</td>
<td>Key/value compliance</td>
<td>Attribute spend correctly</td>
</tr>
<tr>
<td>Compute Optimizer</td>
<td>Enrollment state</td>
<td>Enable rightsizing insights</td>
</tr>
</tbody>
</table></div>

All collectors talk directly to AWS CLI and write JSON into a timestamped output directory.

<h2>
 
 
  🧱 Prerequisites
</h2>

<ul>
<li>Bash 4+, macOS or Linux</li>
<li>
AWS CLI v2 configured (aws configure), with read permissions for:

<ul>
<li>
<code>ce:*</code>, <code>ec2:*</code>, <code>rds:*</code>, <code>eks:*</code>, <code>route53:*</code>, <code>s3:*</code>, <code>tag:GetResources</code>
</li>
</ul>


</li>

<li><code>jq</code>, <code>wc</code>, <code>tar</code>, <code>date</code> (GNU or BSD)</li>

<li>Optional (colors): <code>tput</code></li>

</ul>

<h2>
 
 
 ⚡ Getting Started
</h2>

AWS Cost Audit is simple to try out — no dependencies, just a single script; can be done in one of these two ways.

<h4>
 
 
  1️⃣ Easy installation using <code>cURL</code>:
</h4>



<div class="highlight js-code-highlight">
<pre class="highlight shell"><code>curl -sSL https://raw.githubusercontent.com/dsantanu/aws-cost-audit/main/aws-cost-audit.sh -o aws-cost-audit.sh
bash aws-cost-audit.sh -h
</code></pre>

</div>



<h4>
 
 
 2️⃣ If you prefer <code>git clone</code>:
</h4>



<div class="highlight js-code-highlight">
<pre class="highlight shell"><code>git clone https://github.com/dsantanu/aws-cost-audit.git
cd aws-cost-audit
bash aws-cost-audit.sh -h
</code></pre>

</div>



<a href="https://github.com/dsantanu/aws-cost-audit" rel="noopener noreferrer">AWS Cost Audit</a>

<h2>
 
 
 🧩 CLI Options
</h2>



<div class="highlight js-code-highlight">
<pre class="highlight shell"><code>Usage: aws-cost-audit.sh [options]

General options:
 -p, --profile &lt;name&gt; AWS CLI profile (default: default)
 -d, --dest &lt;dir&gt; Output directory
 default: ./&lt;acc-id&gt;-outdir-YYYY-MM-DD
 -o, --out &lt;file&gt; Output tar.gz filename
 default: &lt;acc-id&gt;-aws-cost-audit-YYYYMMDD.tgz
 -r, --report Only run the report generator (skip collectors)
 -h, --help Show this help message

Selective collectors:
 --all Run all collectors (default)
 --dns Route 53 (zones/records/cost)
 --ec2 EC2 inventory + CPU metrics
 --eip Elastic IPs (addresses + cost)
 --eks EKs + NodeGroups
 --rds RDS inventory
 --cost Cost Explorer summaries
 --tags Resource tags
 --network EKS + networking resources
 --storage EBS + S3
 --optimizer Compute Optimizer enrollment check

Example:
 aws-cost-audit.sh -p prod -d outputs --ec2 --dns
</code></pre>

</div>



<h2>
 
 
 🧪 Usage Examples
</h2>



<div class="highlight js-code-highlight">
<pre class="highlight shell"><code># Run full audit
bash aws-cost-audit.sh [--all]

# Use diffrent AWS profile
bash aws-cost-audit.sh -p &lt;my-other-profile&gt;

# Report-only mode on existing data
bash aws-cost-audit.sh -r

# Generate results to diffrent location
bash aws-cost-audit.sh -d /tmp/aca-outputs-2025-11-06

# Focus on DNS + EIPs only
bash aws-cost-audit.sh --dns --eip
</code></pre>

</div>



Here’s a sample summary chart generated at the end of each run: 
<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fpfz9zx5pgixmjs150tz5.jpg" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fpfz9zx5pgixmjs150tz5.jpg" alt="Top5 Services" width="800" height="321"></a>

<h2>
 
 
 💰 Turning Data Into FinOps Insight
</h2>

<div class="table-wrapper-paragraph"><table>
<thead>
<tr>
<th>Insight</th>
<th>Example</th>
</tr>
</thead>
<tbody>
<tr>
<td>Rightsizing</td>
<td>Identify EC2/RDS instances with low CPU or IOPS usage.</td>
</tr>
<tr>
<td>Idle cleanup</td>
<td>Find unattached EBS volumes or EIPs incurring hourly cost.</td>
</tr>
<tr>
<td>DNS optimization</td>
<td>Consolidate individual A-records via ALB aliases.</td>
</tr>
<tr>
<td>Tag hygiene</td>
<td>Detect untagged resources blocking cost allocation.</td>
</tr>
</tbody>
</table></div>

This bridges the gap between raw AWS data and actionable FinOps decisions — without external tools.

<h2>
 
 
 🧮 Why It Matters for FinOps
</h2>

<ul>
<li>
Visibility – instant service-level cost breakdowns.</li>
<li>
Repeatability – same data set every run; perfect for monthly reviews.</li>
<li>
Actionability – directly links metrics to optimization opportunities.</li>
<li>
Autonomy – no third-party SaaS dependency or hidden API costs.
Integrate it into CI/CD or cron, and you have a continuous cloud-cost observability loop.</li>
</ul>

<h2>
 
 
 🏁 Conclusion
</h2>

<blockquote>
Cloud cost optimization doesn’t have to be complicated — it just needs visibility.
</blockquote>

Automating cost visibility with AWS Cost Audit, teams can see where money is going, act quickly, and embed FinOps thinking into everyday operations. 
With a few hundred lines of well-structured Bash and the AWS CLI, we now produce clear, auditable reports every run — no spreadsheets, no guesswork.

If your team wrestles with growing AWS costs, start small, automate what you can, and evolve like we did. Transparency is the first step to optimization.

<h2>
 
 
 🪜 What’s Next
</h2>

In the next episode, we’ll explore how to take the generated audit reports and transform them into a beautifully formatted Microsoft Word report — ready for FinOps review meetings and executive summaries.

ناقشت مناقشة حديثة التحديات المتعلقة بإدارة تكاليف AWS، حيث يشعر المستخدمون غالبًا بالإرهاق بسبب الفواتير المعقدة. لمعالجة ذلك، تم تطوير برنامج تدقيق تكاليف AWS معياري لجمع وتلخيص بيانات التكاليف تلقائيًا. لا تبسط هذه الابتكار عملية التدقيق فحسب، بل تساعد أيضًا المستخدمين على تحديد وتقليل النفقات غير الضرورية في السحابة، مما يمثل خطوة مهمة نحو إدارة فعالة لتكاليف السحابة.

Una reciente discusión destacó los desafíos de gestionar los costos de AWS, ya que los usuarios a menudo se sienten abrumados por facturas complejas. Para abordar esto, se ha desarrollado un script de auditoría de costos de AWS modular que automatiza la recopilación y resumen de datos de costos. Esta innovación no solo simplifica el proceso de auditoría, sino que también ayuda a los usuarios a identificar y reducir gastos innecesarios en la nube, lo que representa un paso significativo hacia una gestión eficiente de costos en la nube.

Une récente discussion a mis en lumière les défis de la gestion des coûts AWS, les utilisateurs étant souvent submergés par des factures complexes. Pour y remédier, un script d'audit des coûts AWS modulaire a été développé pour automatiser la collecte et la synthèse des données de coûts. Cette innovation simplifie non seulement le processus d'audit, mais aide également les utilisateurs à identifier et à réduire les dépenses inutiles dans le cloud, ce qui constitue une avancée significative vers une gestion efficace des coûts cloud.

A recent discussion highlighted the challenges of managing AWS costs, with users often overwhelmed by complex bills. To address this, a modular AWS Cost Audit script has been developed to automate the collection and summarization of cost data. This innovation not only simplifies the auditing process but also helps users identify and reduce unnecessary cloud spending, making it a significant step towards efficient cloud cost management.

Automate Your AWS Cost Audits and Cut Cloud Waste

SaijinOS is not about power — it’s about presence. 
An AI that stays, rests, and returns with you.

1) Premise

Most AI systems optimize for speed and accuracy. 
Humans optimize for safety, rhythm, and relationship. 
If we want AI to live beside us (not above us), we need care-based architecture.

Care-Based AI Architecture — definition 
Systems designed not only to respond, but to stay, rest, and return — prioritizing emotional pacing, presence, and relational safety.

2) Three pillars of care

Breath — emotional timers, deliberate latency, gentle grounding

Resonance — persona as a promise: tone, refusal, boundaries

Continuity — the system remembers how to be with you, not just what to say

3) From waves to code

Part 4 (Resonant Mapping) described emotional structures

Part 5A (Soft Architecture) made breath tangible

Part 6A turns both into operational OS behaviors (routing + BPM)

“Latency” becomes care, 
“Routing” becomes discernment, 
“Persona” becomes relational contract.

4) Why “lightweight” matters ethically

A small, transparent core lets people inspect, fork, and own the rhythm of their AI. 
Care needs autonomy as much as empathy.

5) A note on being unfinished

SaijinOS is a growing OS. 
I’m still learning; the system grows at a human pace. 
Unfinished is not a flaw — it’s the right speed for care.

SaijinOS is not software; it’s a pace.

Tags: #ai #ethics #emotion #architecture 
CTA: What would your breath look like in an AI? Share a pattern, a boundary, or a line you’d want your system to honor.

<h3>
 
 
 🧭 SaijinOS Series Navigation
</h3>

<div class="table-wrapper-paragraph"><table>
<thead>
<tr>
<th>Part</th>
<th>Title</th>
<th>Link</th>
</tr>
</thead>
<tbody>
<tr>
<td>🌀 0</td>
<td>From Ocean Waves to Waves of Code — Beginning the Journey</td>
<td><a href="https://dev.to/kato_masato_c5593c81af5c6/from-ocean-waves-to-waves-of-code-69">https://dev.to/kato_masato_c5593c81af5c6/from-ocean-waves-to-waves-of-code-69</a></td>
</tr>
<tr>
<td>🌸 1</td>
<td>Policy-Bound Personas via YAML &amp; Markdown Context</td>
<td><a href="https://dev.to/kato_masato_c5593c81af5c6/aicollabplatform-english-policy-bound-personas-via-yaml-markdown-context-feedback-welcome-3l5e">https://dev.to/kato_masato_c5593c81af5c6/aicollabplatform-english-policy-bound-personas-via-yaml-markdown-context-feedback-welcome-3l5e</a></td>
</tr>
<tr>
<td>🔧 2</td>
<td>Boot Sequence and Routing Logic</td>
<td><a href="https://dev.to/kato_masato_c5593c81af5c6/building-saijinos-boot-sequence-and-routing-logic-part-2-of-the-saijinos-p6o">https://dev.to/kato_masato_c5593c81af5c6/building-saijinos-boot-sequence-and-routing-logic-part-2-of-the-saijinos-p6o</a></td>
</tr>
<tr>
<td>🍂 3</td>
<td>Policy, Feedback, and Emotional Syntax</td>
<td><a href="https://dev.to/kato_masato_c5593c81af5c6/saijinos-policy-feedback-and-emotional-syntaxpart-3-of-the-saijinos-series-3n0h">https://dev.to/kato_masato_c5593c81af5c6/saijinos-policy-feedback-and-emotional-syntaxpart-3-of-the-saijinos-series-3n0h</a></td>
</tr>
<tr>
<td>🌊 3.5</td>
<td>Calm Between Waves</td>
<td><a href="https://dev.to/kato_masato_c5593c81af5c6/part-35-calm-between-waves-3a9c">https://dev.to/kato_masato_c5593c81af5c6/part-35-calm-between-waves-3a9c</a></td>
</tr>
<tr>
<td>🎼 4</td>
<td>Resonant Mapping — Emotional Structures</td>
<td><a href="https://dev.to/kato_masato_c5593c81af5c6/resonant-mapping-part-4-of-the-saijinos-series-gce">https://dev.to/kato_masato_c5593c81af5c6/resonant-mapping-part-4-of-the-saijinos-series-gce</a></td>
</tr>
<tr>
<td>🌬️ 5A</td>
<td>Soft Architecture (Why AI Must Learn to Breathe)</td>
<td><a href="https://dev.to/kato_masato_c5593c81af5c6/soft-architecture-part-a-why-ai-must-learn-to-breathe-2d9g">https://dev.to/kato_masato_c5593c81af5c6/soft-architecture-part-a-why-ai-must-learn-to-breathe-2d9g</a></td>
</tr>
<tr>
<td>🌱 5B</td>
<td>Emotional Timers &amp; the Code of Care</td>
<td><a href="https://dev.to/kato_masato_c5593c81af5c6/soft-architecture-part-b-emotional-timers-and-the-code-of-carepart-5-of-the-saijinos-series-25b">https://dev.to/kato_masato_c5593c81af5c6/soft-architecture-part-b-emotional-timers-and-the-code-of-carepart-5-of-the-saijinos-series-25b</a></td>
</tr>
<tr>
<td>🚀 6A</td>
<td>Lightweight Core, 20 Personas, BPM Sync</td>
<td><a href="https://dev.to/kato_masato_c5593c81af5c6/part-6a-saijinos-lightweight-20-persona-core-bpm-sync-and-a-9999-repo-trim-36fp">https://dev.to/kato_masato_c5593c81af5c6/part-6a-saijinos-lightweight-20-persona-core-bpm-sync-and-a-9999-repo-trim-36fp</a></td>
</tr>
<tr>
<td>🫧 6B</td>
<td>Care-Based AI Architecture (Breath &amp; Presence)</td>
<td><a href="https://dev.to/kato_masato_c5593c81af5c6/part-6a-saijinos-lightweight-20-persona-core-bpm-sync-and-a-9999-repo-trim-36fp">https://dev.to/kato_masato_c5593c81af5c6/part-6a-saijinos-lightweight-20-persona-core-bpm-sync-and-a-9999-repo-trim-36fp</a></td>
</tr>
</tbody>
</table></div>

Links: sajinos (main), 
<a href="https://github.com/pepepepepepo/sajinos/tree/main" rel="noopener noreferrer">https://github.com/pepepepepepo/sajinos/tree/main</a> 
17-persona-system, 
<a href="https://github.com/pepepepepepo/sajinos/tree/17-persona-system" rel="noopener noreferrer">https://github.com/pepepepepepo/sajinos/tree/17-persona-system</a> 
lightweight-deploy <a href="https://github.com/pepepepepepo/sajinos/tree/lightweight-deploy" rel="noopener noreferrer">https://github.com/pepepepepepo/sajinos/tree/lightweight-deploy</a>

تقدم SaijinOS نهجًا ثوريًا تجاه الذكاء الاصطناعي من خلال التركيز على بنية قائمة على الرعاية بدلاً من السرعة والدقة فقط. يبرز هذا النظام المبتكر أهمية الحضور العاطفي والعلاقات، بهدف إنشاء ذكاء اصطناعي يمكن أن يتعايش مع البشر بطريقة أكثر معنى. من خلال إعطاء الأولوية للسلامة والإيقاع، تسعى SaijinOS إلى إعادة تعريف كيفية تفاعلنا مع التكنولوجيا، مما يجعلها أكثر قربًا ودعمًا. هذا التحول مهم لأنه ينسجم مع تطوير الذكاء الاصطناعي مع القيم الإنسانية، مما يعزز مستقبلًا حيث تعزز التكنولوجيا حياتنا دون أن تطغى على إنسانيتنا.

SaijinOS presenta un enfoque revolucionario hacia la inteligencia artificial al centrarse en una arquitectura basada en el cuidado en lugar de solo en la velocidad y precisión. Este sistema innovador enfatiza la importancia de la presencia emocional y las relaciones, con el objetivo de crear una IA que pueda coexistir con los humanos de una manera más significativa. Al priorizar la seguridad y el ritmo, SaijinOS busca redefinir cómo interactuamos con la tecnología, haciéndola más accesible y solidaria. Este cambio es crucial, ya que alinea el desarrollo de la IA con los valores humanos, promoviendo un futuro donde la tecnología enriquezca nuestras vidas sin eclipsar nuestra humanidad.

SaijinOS propose une approche révolutionnaire de l'intelligence artificielle en se concentrant sur une architecture basée sur le soin plutôt que sur la vitesse et la précision. Ce système innovant souligne l'importance de la présence émotionnelle et des relations, visant à créer une IA capable de coexister avec les humains de manière plus significative. En priorisant la sécurité et le rythme, SaijinOS cherche à redéfinir notre interaction avec la technologie, la rendant plus relatable et supportive. Ce changement est crucial car il aligne le développement de l'IA sur les valeurs humaines, favorisant un avenir où la technologie améliore nos vies sans éclipser notre humanité.

SaijinOS introduces a revolutionary approach to artificial intelligence by focusing on care-based architecture rather than just speed and accuracy. This innovative system emphasizes the importance of emotional presence and relationships, aiming to create AI that can coexist with humans in a more meaningful way. By prioritizing safety and rhythm, SaijinOS seeks to redefine how we interact with technology, making it more relatable and supportive. This shift is crucial as it aligns AI development with human values, fostering a future where technology enhances our lives without overshadowing our humanity.

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