What is an API-Based AI?

Instead of downloading model weights and provisioning GPUs, an application sends a request — a prompt, some context, maybe tool definitions — to a provider's endpoint and gets a completion back. The provider (Anthropic, OpenAI, and others) handles the compute, scaling, and model updates; the caller just handles the request and response format and pays per token or per call. This model dominates production AI products because it removes enormous operational burden: no GPU fleet to manage, automatic access to model improvements, and usage-based cost that scales with actual demand rather than fixed infrastructure spend. The tradeoff is dependency on the provider's uptime, pricing, and rate limits, and less control over exactly how the model was trained or what data it saw. Most production systems build a routing or fallback layer across multiple API-based providers so a single vendor outage or rate-limit spike doesn't take the whole product down — treating the model itself as a swappable, monitored dependency rather than a fixed assumption.