API Design for AI Services
Best practices for designing APIs that serve AI workloads, covering streaming responses, versioning, error handling for probabilistic …
Api-Design
API Versioning Strategies for AI Services
How to version AI APIs as models evolve: URL path versioning, header versioning, model version pinning, backward compatibility, and …
Idempotency
What idempotency means, how idempotency keys work for API endpoints, and why safe retry behaviour is critical for AI inference APIs handling …
OpenAPI
What the OpenAPI Specification is, how schema-first API design works, and why code generation from OpenAPI specs improves consistency for AI …
Rate Limiting for LLM and AI Endpoints
How to implement rate limiting for AI API endpoints: token bucket and sliding window algorithms, per-user and per-model limits, token-based …
REST vs GraphQL for AI Application APIs
Comparing REST and GraphQL API designs for AI applications, covering streaming support, query patterns, caching, and practical …
API Design
REST, GraphQL, and gRPC. The principles of versioning, error handling, idempotency, and authentication that make APIs reliable contracts …