The companies with the most control over chip supply on the planet still rent across three cloud providers. That is the fact that should reset how a platform team thinks about AI infrastructure. If a frontier lab with custom silicon deals and over a million of its own accelerators cannot single-source compute, the 200-person team running model-serving in production has no business betting on one provider either.
Read the numbers from the lab itself. Anthropic states plainly that it runs Claude across three silicon families and three clouds at the same time: "We train and run Claude on a range of AI hardware — AWS Trainium, Google TPUs, and NVIDIA GPUs… Claude remains the only frontier AI model available to customers on all three of the world's largest cloud platforms: AWS (Bedrock), Google Cloud (Vertex AI), and Microsoft Azure (Foundry)." That is from Anthropic's own partnership announcement. They do not frame it as insurance. They frame it as matching workloads to the chips best suited for them, which buys better performance and more resilience.
The Money Says This Is the Baseline, Not a Side Bet
Hedging is small. What Anthropic is doing is not small.
On the AWS side, the commitment runs over $100 billion and up to 5 gigawatts across a ten-year span. More than a million Trainium2 chips are already training and serving Claude through Project Rainier, and AWS is named the primary training and cloud provider. That spans Graviton CPUs and the Trainium2-through-Trainium4 custom silicon line.
