LiteLLM: The AI Gateway Handbook: Building Production-Ready LLM Infrastructure
English | May 29, 2026 | ASIN: B0H3BYDQ85 | 429 pages | Epub | 1.37 MB
**Build production-grade LLM infrastructure with the industry's leading open-source AI gateway.**Every production AI system faces the same challenge: managing multiple LLM providers - OpenAI, Anthropic, Google, AWS Bedrock, Azure - each with different SDKs, authentication methods, rate limits, and pricing models. **LiteLLM** solves this by providing a unified interface to call 100+ LLMs through a single, OpenAI-compatible API.This comprehensive handbook takes you from your first API call to running LiteLLM at enterprise scale across Kubernetes clusters. Written for AI engineers, platform engineers, and DevOps teams, it covers the full spectrum of LiteLLM capabilities with real, runnable code examples.**What you'll learn:**- **Unified API Integration** - Call OpenAI, Anthropic Claude, Gemini, Bedrock, and Azure through one interface- **Multi-Tenant Management** - Virtual keys, team budgets, scoped access, and RBAC- **Intelligent Routing** - Weighted load balancing, fallback chains, health checks, and circuit breakers- **Cost Control** - Per-key spend tracking, budget alerts, and team-level cost allocation- **Production Reliability** - Rate limiting, request throttling, retry strategies, and error resilience- **Observability** - Langfuse integration, OpenTelemetry tracing, Prometheus metrics- **Safety & Compliance** - Guardrails, PII detection, content moderation, audit logging- **Advanced Patterns** - Streaming, async processing, A2A agent gateway, MCP tool integration- **Deployment at Scale** - Docker, Kubernetes with Helm, Terraform IaC, performance tuning**By the end of this book, you'll be able to:**- Deploy LiteLLM as a centralized AI gateway for your organization- Route traffic intelligently across multiple providers and models- Track and control costs with per-team budgets and alerts- Implement guardrails and safety checks on all LLM traffic- Scale to millions of requests with Kubernetes and auto-scaling- Integrate with Langfuse, OpenTelemetry, and Prometheus for full observability**Who this book is for:**AI/ML engineers building production LLM applications, platform engineers managing AI infrastructure, DevOps teams deploying LLM services, and engineering leaders evaluating AI gateway solutions.**Prerequisites:**Basic Python knowledge and familiarity with REST APIs. No prior LiteLLM experience required.
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