Advanced AI Infrastructure

AI Gateway Architecture & Multi-LLM Routing

A hands-on engineering course on the AI gateway layer as it is actually built in 2025-26: the unified schema that lets one client call any provider, routing by latency, cost, or task, reliability patterns, caching, virtual keys and budgets, guardrails, and OpenTelemetry tracing. Built against real open-source gateways (LiteLLM, TensorZero, Envoy AI Gateway) and open-weight endpoints (Ollama, vLLM, Groq). For teams routing production traffic across more than one model.

28 Lessons
4.5h Duration
Text + Code Format
Free Cost
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Prerequisites
  • Comfortable Python (async/await, type hints, HTTP clients)
  • Experience calling at least one LLM API directly (OpenAI, Anthropic, or similar)
  • Basic Docker and REST API familiarity

What you'll learn

  • Build production gateway configs with LiteLLM, routing across OpenAI, Anthropic, and self-hosted open-weight models on Ollama and vLLM
  • Design routing strategies: static priority, latency- and cost-aware dynamic routing, and semantic routing that picks a model per task
  • Engineer reliability into the gateway: retries with backoff, circuit breakers, fallback chains, and rate limiting
  • Cut cost and latency with exact-match and semantic response caching, and provider-native prompt caching
  • Enforce governance at the gateway: virtual keys, per-team budgets and RBAC, PII redaction, and audit logging
  • Instrument every model call with OpenTelemetry GenAI tracing, and deploy the gateway on Docker and Kubernetes

Course Curriculum

10 modules · 28 lessons · 4.5 hours total

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