Deployment & Infrastructure

Inference Endpoint

An API endpoint that accepts model inputs and returns predictions, serving as the interface between agent applications and underlying LLM compute.

Definition

An inference endpoint is the HTTP/HTTPS API surface through which AI agent applications communicate with LLM compute. It standardizes the request/response protocol: clients send structured payloads (messages, parameters, streaming preferences) and receive generated text, structured outputs, or streaming token chunks. Inference endpoints abstract away the details of GPU allocation, model loading, and batch processing, presenting a simple RPC-style interface to application developers.

Engineering Context

Inference endpoints follow OpenAI-compatible API conventions in most modern serving systems, enabling drop-in replacement between models and providers. Key attributes: authentication, request validation, rate limiting, streaming support (Server-Sent Events), and error response standards. In production, endpoints sit behind load balancers and expose health check endpoints for orchestration systems. Circuit breakers and fallback routing (e.g., primary model fails → fallback to smaller model or cached response) prevent cascading failures. Monitor endpoint-level metrics: request rate, error rate, P50/P95/P99 latency, and queue depth.

Related Terms

Model Serving On-Premise LLM Latency Throughput Large Language Model

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