AI Model APIs & Infrastructure

AI model API and infrastructure platforms give developers programmatic access to generative models and the operational controls needed to run them in applications. This category covers model gateways, hosted inference APIs, routing layers and related infrastructure; it does not describe code editors or no-code app builders.

Evaluate the production path

Compare model and modality coverage, API compatibility, streaming, structured output, tool calling, SDKs and regional availability. Then test rate limits, latency, retries, fallback behavior, uptime reporting, logs and cost controls. A wide catalog is useful only if model versions and provider behavior remain clear.

Security, data and cost

Review retention, training use, encryption, access keys, workspace roles and compliance options. Pricing can combine model tokens with platform fees, caching, storage or data transfer. Benchmark a realistic workload and plan how to avoid provider lock-in.

Common questions

Is an API aggregator the same as a model host?

No. An aggregator may route to external providers, while a host runs inference itself; some platforms do both.

What matters beyond price?

Reliability, predictable model identity, data handling, latency and migration options are equally important in production.