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Build secure, scalable, production-ready AI gateway infrastructure with NGINX, Kubernetes, vLLM, Ollama, MCP, and modern observability tools.NGINX AI Gateway in Practice ...
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Build secure, scalable, production-ready AI gateway infrastructure with NGINX, Kubernetes, vLLM, Ollama, MCP, and modern observability tools.NGINX AI Gateway in Practice is a hands-on guide to designing, deploying, securing, monitoring, and operating modern AI gateway platforms for LLM applications, self-hosted inference, AI agents, and Kubernetes-based model serving.You will learn how to use NGINX as a controlled entry point for AI traffic, route requests across cloud and self-hosted models, deploy NGINX Gateway Fabric with Kubernetes Gateway API, implement inference-aware routing with the Gateway API Inference Extension, and serve models with vLLM, Ollama, and SGLang.The book also covers production-grade security with TLS, mTLS, JWT authentication, rate limiting, tenant isolation, prompt-injection defenses, MCP security, and data-leakage prevention. You will build observability pipelines with Prometheus, Grafana, and OpenTelemetry, measure AI-specific performance metrics such as TTFT and TPOT, monitor GPU utilization, and manage deployments through CI/CD and GitOps.Inside, you will learn how to:Build NGINX-based AI gateways for OpenAI-compatible APIs and multiple providersDeploy Kubernetes-native inference gateways with NGINX Gateway FabricImplement InferencePool, Endpoint Picker, and intelligent model routingServe self-hosted models with vLLM, Ollama, and SGLangSecure AI APIs with TLS, mTLS, JWT, quotas, and Zero Trust controlsProtect AI agents and MCP tools with least-privilege authorizationMonitor gateways, models, GPUs, tokens, TTFT, TPOT, and distributed tracesDesign high availability, failover, autoscaling, disaster recovery, and multi-cluster architecturesAutomate delivery with CI/CD, GitOps, testing, versioning, and rollbackBuild a complete enterprise-grade AI gateway platform in the full-stack capstone projectWritten for DevOps engineers, platform engineers, SREs, Kubernetes administrators, AI infrastructure engineers, MLOps professionals, security engineers, backend developers, and enterprise architects, this book focuses on real implementation, production troubleshooting, validation, failure recovery, and operational best practices.By the end of the book, you will have the knowledge and practical foundation to build and operate secure, observable, scalable AI gateway infrastructure for modern LLM applications, private AI platforms, AI agents, and enterprise inference systems.
