Large language models, LLM APIs, and how to run them in production.
View All TagsAI-powered APIs don't follow the same request-response flow as traditional REST APIs. Here's precisely what changes, and why it matters.
Architecture patterns for production LLM-backed APIs: request design, streaming, retries, fallback chains, caching, and cost control.
Cut LLM API spend without sacrificing output quality. Learn semantic caching, model routing, and prompt compression, enforced at the AI Gateway.
Token-based vs. request-based limits, per-user and per-feature quotas, and how to implement LLM rate limiting at the gateway instead of per application.
Server-Sent Events, chunked transfer, mid-stream errors, and gateway behavior: a practical guide to streaming LLM API responses correctly.
GPT, Claude, and Gemini vs. self-hosted Llama, Mistral, and Qwen. Real break-even math on cost, latency, control, and compliance for production.