Anthropic to scale Claude on Azure
Anthropic to adopt NVIDIA architecture
NVIDIA and Microsoft to invest in Anthropic

Today Microsoft, NVIDIA and Anthropic announced new strategic partnerships. Anthropic is scaling its rapidly-growing Claude AI model on Microsoft Azure, powered by NVIDIA, which will broaden access to Claude and provide Azure enterprise customers with expanded model choice and new capabilities. Anthropic has committed to purchase $30 billion of Azure compute capacity and to contract additional compute capacity up to one gigawatt.  

For the first time, NVIDIA and Anthropic are establishing a deep technology partnership to support Anthropic’s future growth. Anthropic and NVIDIA will collaborate on design and engineering, with the goal of optimizing Anthropic models for the best possible performance, efficiency, and TCO, and optimizing future NVIDIA architectures for Anthropic workloads. Anthropic’s compute commitment will initially be up to one gigawatt of compute capacity with NVIDIA Grace Blackwell and Vera Rubin systems. 

Microsoft and Anthropic are also expanding their existing partnership to provide broader access to Claude for businesses. Customers of Microsoft Foundry will be able to access Anthropic’s frontier Claude models including Claude Sonnet 4.5, Claude Opus 4.1, and Claude Haiku 4.5. This partnership will make Claude the only frontier model available on all three of the world’s most prominent cloud services. Azure customers will gain expanded choice in models and access to Claude-specific capabilities.  

Microsoft has also committed to continuing access for Claude across Microsoft’s Copilot family, including GitHub Copilot, Microsoft 365 Copilot, and Copilot Studio. 

As part of the partnership, NVIDIA and Microsoft are committing to invest up to $10 billion and up to $5 billion respectively in Anthropic.  

Anthropic co-founder and CEO Dario Amodei, Microsoft Chairman and CEO Satya Nadella, and NVIDIA founder and CEO Jensen Huang gathered to discuss the new partnerships:

The post Microsoft, NVIDIA and Anthropic announce strategic partnerships appeared first on The Official Microsoft Blog.

Performance testing is critical for delivering reliable, scalable applications. We have been working on AI-driven innovations in Azure Load Testing that will change how you author and analyze load tests.

AI-Assisted Authoring of JMeter Scripts

Writing high-quality load test scripts has traditionally required deep expertise. From setting correlations and think times to properly parameterizing inputs, it requires significant time and effort. This manual effort slows teams down, especially when they must recreate real-world scenarios under tight deadlines.

With our new AI-assisted authoring, that changes.

Now you can simply record your application journey, and Azure Load Testing will do the heavy lifting:

• Record your scenarios using the browser extension
• AI automatically suggests correlations to handle dynamic values
• Intelligent parameterization for more realistic test data
• Smart request labelling to help you organize flows cleanly
• Recommended think times to match actual user behavior

Once refined, a production-ready JMeter script is generated automatically. You can run this script immediately on Azure Load Testing with the scale and reliability you expect.

You can create complex, realistic performance tests created in a fraction of the time, even if you’re not a JMeter expert.

AI-Powered Actionable Insights

Performance tests don’t stop at execution. Real value comes from understanding what happened and knowing what to do next.

We have supercharged our insights experience with AI.

• Insights for Failed Test Runs: When a test fails, the first question is always: why?
  Now, Azure Load Testing uses AI to automatically analyze test run logs, detect the root cause, and provide clear guidance on what went wrong and how to fix it.
• Baseline Comparison Insights: Compare any test run against your defined baseline to immediately see what degraded, what improved, and which requests diverged from expected performance. It also helps understand the root cause for performance degradation.
• Focused Recommendations for Failed Test Criteria: If any of your pass/fail criteria fail, AI surfaces targeted recommendations so you can take corrective action quickly.

You get meaningful insights, even when things don’t go as planned. No more staring at graphs trying to figure out what to do next.

The Future of Load Testing Is Intelligent

With AI assisting script creation and analyzing test outcomes end-to-end, Azure Load Testing now helps teams:

• Run real world performance tests faster
• Troubleshoot with confidence
• Reduce manual debugging

The authoring capability will be available in the next couple of weeks. Meanwhile, you can try out AI-powered insights for your load test run to quickly analyze your results.

Please share your feedback here.

Happy Load Testing!