Why Microsoft Still Needs Nvidia and AMD Even After Building Its Own AI Chips

When Satya Nadella confirmed that Microsoft will keep buying AI chips from Nvidia and AMD even after launching its own silicon, many people were surprised. If Microsoft now has its own chips, why does it still need others? The answer reveals a lot about how the AI hardware ecosystem really works in 2026.

In this blog, we’ll break down why Microsoft is investing heavily in its own chips while still staying one of the biggest customers of Nvidia GPUs and AMD accelerators, and what that means for the future of cloud AI, developers, and startups.

In recent years, Microsoft has unveiled its own in-house AI accelerators, designed specifically to power workloads on Azure and services like Copilot. These chips are tuned to run large language models, recommendation systems, and other demanding AI tasks more efficiently.

The goal is simple: optimize cost, performance, and control. By designing its own silicon, Microsoft can better integrate hardware with its software stack, from Windows to Azure OpenAI Service. This is similar to what Google did with TPUs and what Amazon did with its Trainium and Inferentia chips.

So Why Still Buy Nvidia and AMD?

Even with its own silicon, Nadella has been very clear: Microsoft will not stop buying GPUs from Nvidia and AMD. There are several strong reasons for this, and understanding them helps explain the reality behind today’s AI boom.

1. Demand for AI Compute Is Exploding

The world’s demand for AI compute is growing faster than any single company can handle alone. Training and running models like GPT-style systems, image generators, and video models requires huge amounts of hardware. Enterprises are spinning up Azure AI clusters, startups are deploying new AI apps, and even individuals are experimenting with LLMs.

Nadella’s message is essentially: even if Microsoft builds its own chips, it still can’t get enough GPU power. Nvidia H100 and newer architectures, along with AMD Instinct accelerators, will keep filling that gap. Building internal chips is not a replacement; it’s an addition.

2. Nvidia’s Software Ecosystem Is a Moat

Buying hardware is not only about silicon; it’s about the software ecosystem around it. Nvidia has spent years building CUDA, TensorRT, and a massive set of libraries and tools that developers deeply rely on.

Most AI research labs, ML engineers, and data scientists are already optimized for Nvidia GPUs. Rewriting and re-optimizing everything for a new chip architecture is expensive and slow. So even if Microsoft offers its own accelerators, many customers will still prefer tried-and-tested Nvidia-based clusters.

This is why Nadella can confidently say Microsoft will keep buying from Nvidia: customers demand it. If Azure wants to stay one of the top AI clouds, it must support what developers already know and trust.

3. Multi-Vendor Strategy Reduces Risk

There’s also a strategic reason: never depend on a single supplier. The GPU shortage of the early 2020s taught big tech a serious lesson. If all your AI capacity depends on one vendor and they hit supply issues or price spikes, your entire roadmap is at risk.

By keeping strong relationships with Nvidia, AMD, and also building its own chips, Microsoft spreads its risk. This multi-vendor strategy helps Microsoft negotiate better prices, ensure stable supply, and avoid being locked into any single ecosystem.

4. Different Chips for Different Jobs

Not all AI workloads are the same. Some are training-heavy, some are inference-heavy, some need ultra-low latency, and some need maximum throughput. It’s unlikely that a single chip design will be best at everything.

Microsoft’s own chips might be finely tuned for specific internal workloads, like Copilot in Office or search. Meanwhile, Nvidia GPUs may still be better for large-scale training runs or for customers with custom research workflows. AMD accelerators can sit somewhere in between or target specific performance-per-dollar segments.

In short, it’s not a question of “either/or”; it’s a question of building a portfolio of AI hardware options.

What This Means for the AI Industry

Nadella’s stance sends a clear signal to the market: even the biggest tech giants can’t go fully solo on AI hardware. Specialized chips are becoming a competitive advantage, but partnerships still matter.

For context, consider how other players are moving. Google builds TPUs but still offers Nvidia GPUs on Google Cloud. Amazon Web Services pushes Trainium and Inferentia yet continues to invest in Nvidia-based instances. Meta is developing its own accelerators but remains a large GPU buyer.

Similarly, Microsoft is hedging its bets in hardware: build in-house silicon, but stay close to Nvidia and AMD. This blended approach keeps Azure competitive for every kind of AI customer—from enterprises migrating legacy ML workloads to startups building the next generation of AI-native apps.

How Developers and Startups Benefit

For developers, founders, and startups, Microsoft’s hybrid chip strategy is good news. It means:

1. More choice of hardware: On Azure, you can run models on Nvidia GPUs, AMD accelerators, or Microsoft’s own chips, depending on cost, performance, and compatibility.

2. Better pricing over time: Competition between vendors (Nvidia, AMD, and Microsoft’s silicon teams) should gradually push AI compute costs down, making it easier to experiment and scale.

3. Stable supply for large projects: If one chip family is backordered, Azure can allocate capacity from another. This is critical for teams training long-running models or building products that can’t afford downtime.

4. Faster innovation: When multiple vendors are competing to power Azure’s AI, you get quicker upgrades, new architectures, and better AI infrastructure features.

Strategic Reasons Behind Microsoft’s Own Silicon

While Microsoft won’t stop buying from Nvidia and AMD, it also won’t stop building its own silicon. Here’s why that internal effort still matters so much:

Cost control: As AI usage explodes across Office, GitHub, Teams, and Azure, the cloud bill for GPUs can reach billions. Owning part of the stack lets Microsoft manage long-term economics better.

Customization: Microsoft can bake in features that tightly integrate with its software stack, including Windows Server, Azure AI Studio, and internal frameworks. This can improve latency, energy efficiency, and security.

Strategic leverage: Having its own chips gives Microsoft more negotiating power with external vendors and more freedom to plan its hardware roadmap without being fully dependent on outside timelines.

The Bigger Picture: AI Chips as the New Cloud War

We’re entering a phase where AI chips are the new battleground in the cloud wars. Google has TPUs, Amazon has Trainium, Meta is building its own accelerators, and now Microsoft is all in too. But none of them are fully walking away from Nvidia or AMD.

Instead, they are combining custom silicon with best-in-class GPUs from external partners. Nadella’s statement that Microsoft will keep buying chips even after launching its own is just an honest reflection of how massive and fast-growing the AI compute market really is.

Final Thoughts

The takeaway is simple but powerful: having your own AI chip doesn’t mean you stop needing others. For Microsoft, building its own silicon is about optimization and control, while continuing to buy from Nvidia and AMD is about scale, ecosystem, and flexibility.

If you’re a developer, founder, or tech enthusiast, expect a future where cloud providers quietly mix and match different AI chips under the hood, while you simply choose the performance and price tier that fits your project. Behind that simplicity is a complex supply chain—and in the age of AI, no one wins alone.