API Patterns for AI Systems

AI systems have API requirements that traditional applications do not. Model inference is computationally expensive and latency-variable. Responses may be streamed rather than returned in a single payload. Confidence scores accompany outputs. Rate limits are tighter because each call consumes compute resources. Designing APIs for AI systems requires patterns that account for these characteristics.

1. Streaming Responses AI model outputs — especially from large language models — generate tokens sequentially. Server-Sent Events (SSE) or WebSockets allow the consumer to begin processing output before the full response is generated. The perceived latency drops dramatically. A 10-second generation that streams from the first token feels like a 200ms response to the user.
2. Asynchronous Processing For expensive operations — document processing, batch inference, fine-tuning — the synchronous request-response pattern is inappropriate. Submit the job, return a job ID, and provide a status endpoint or callback webhook. The consumer polls for completion or receives a notification. This pattern prevents timeout cascades in the client and allows the backend to queue and prioritize work.
3. Confidence and Metadata AI outputs should include confidence scores, model version, processing time, and token usage alongside the result. This metadata allows the consumer to make informed decisions — routing low-confidence outputs to human review, tracking cost per request, and detecting model version drift. The metadata is as valuable as the result.