Organizations running mission‑critical workloads operate under stricter standards because system failures can often affect people and business operations at scale. They must ensure control, resilience, and operational autonomy such that innovation does not compromise governance. They need agility that also maintains continuity and preserves standards compliance, so they can get the most out of AI,…
Enterprises today are embracing open-weight models for their transparency, flexibility, and ability to run across a broad range of deployment architectures. As the number of open models grows, the bar for reliability, instruction-following quality, multimodal reasoning, and long-context performance continues to rise. Today, we’re excited to announce that Mistral Large 3 is now available in Azure, bringing one of the…
Ten years ago, Microsoft and Red Hat began a partnership grounded in open source and enterprise cloud innovation. This year, we celebrate a decade of collaboration. Our journey together has helped customers accelerate hybrid cloud transformation, empower developers to innovate, and strengthen the open source community to drive modern application innovation. Accelerate modernization with Azure…
Enabling the next wave of cloud transformation with Azure Networking The cloud landscape is evolving at an unprecedented pace, driven by the exponential growth of AI workloads and the need for seamless, secure, and high-performance connectivity. Azure Network services stand at the forefront of this transformation, delivering the hyperscale infrastructure, intelligent services, and resilient architecture…
We’re at a real inflection point in the AI landscape, a threshold where models move from useful assistants to genuine collaborators. Models that understand the objective, factor in constraints, and execute complex multi-tool workflows. Models that not only support processes, but help restructure them for reliability, scale, and operational efficiency. Anthropic’s newest model, Claude Opus…
Organizations face a challenging trade-off when adapting AI models to their specific business needs: settle for generic models that produce average results, or tackle the complexity and expense of advanced model customization. Traditional approaches force a choice between poor performance with smaller models or the high costs of deploying larger model variants and managing complex…