Hyperscaler

Hyperscalers are the landlords of the internet and increasingly of AI. They operate data centers at such scale that they can offer compute, storage, databases, and AI services far more cheaply than most organizations could on their own. Amazon, Microsoft, and Google are the dominant three; Oracle and Meta are growing presences. Their cloud revenues directly fund much of the AI infrastructure buildout happening today.

For builders, hyperscalers are simultaneously infrastructure providers, potential competitors, and distribution channels. You might run your app on AWS, call Azure OpenAI endpoints for model access, and sell through the AWS Marketplace. The dependency creates risk: pricing changes, service availability, and hyperscaler-built competing products can all affect your business significantly.

The AI era has deepened hyperscaler importance. Training large models requires the kind of GPU clusters only they can provide. Inference at scale similarly runs on their infrastructure. Startups that take cloud credits from hyperscalers during early stages often face vendor lock-in implications later: migrating workloads between clouds is technically and commercially complex. This is why multi-cloud strategies and open-weight models that can run on any infrastructure have strategic appeal for AI companies trying to maintain flexibility.