{"product_id":"llm-inference-engineering-handbook-crush-api-costs-cut-latency-and-build-reliable-production-systems-real-benchmarks-python-code-and-complete-code-repository-for-engineers-at-scale","title":"LLM Inference Engineering Handbook: Crush API Costs, Cut Latency and Build Reliable Production Systems — Real Benchmarks, Python Code and Complete Code Repository for Engineers at Scale","description":"\u003cdiv\u003e \u003cdiv\u003e \u003cdiv\u003e \u003ch4\u003e\u003cspan\u003eYour LLM system works. Your API bill doesn't.\u003c\/span\u003e\u003c\/h4\u003e\n\u003cp\u003e\u003cspan\u003eYou've built something that runs. Users are happy. But last month's invoice landed like a punch: thousands of dollars in API costs, response times that spike without warning, and a CFO asking questions you don't have clean answers to.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eYou're not doing anything wrong. You're just running a system that was never optimized for production reality.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThis book fixes that.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eBased on real benchmarks from production systems running 10,000+ queries per day, LLM Inference Engineering Handbook documents the exact techniques that \u003c\/span\u003e\u003cspan\u003ereduce API costs by 73% and cut average response time by 57% — without touching quality\u003c\/span\u003e\u003cspan\u003e.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cu\u003eEvery number in this book was measured, not estimated.\u003c\/u\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eYou'll build a complete optimization stack from scratch:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003e\u003cp\u003e\u003cspan\u003eCost profiling\u003c\/span\u003e\u003cspan\u003e — find exactly where your money goes before optimizing anything\u003c\/span\u003e\u003c\/p\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cp\u003e\u003cspan\u003ePrompt compression\u003c\/span\u003e\u003cspan\u003e — remove 30-40% of redundant tokens without losing semantic meaning\u003c\/span\u003e\u003c\/p\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cp\u003e\u003cspan\u003eMulti-layer caching\u003c\/span\u003e\u003cspan\u003e — eliminate 40-70% of API calls with exact match and semantic cache combined\u003c\/span\u003e\u003c\/p\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cp\u003e\u003cspan\u003eModel routing\u003c\/span\u003e\u003cspan\u003e — send simple queries to fast, cheap models and complex queries to powerful ones, automatically\u003c\/span\u003e\u003c\/p\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cp\u003e\u003cspan\u003eAsync and batching\u003c\/span\u003e\u003cspan\u003e — increase throughput 4x without changing your logic\u003c\/span\u003e\u003c\/p\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cp\u003e\u003cspan\u003eLatency engineering\u003c\/span\u003e\u003cspan\u003e — understand TTFT, p99, and why your worst 10% of users define your product's reputation\u003c\/span\u003e\u003c\/p\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cp\u003e\u003cspan\u003eRAG cost optimization\u003c\/span\u003e\u003cspan\u003e — stop sending 5,000 tokens of context when 800 will do\u003c\/span\u003e\u003c\/p\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cp\u003e\u003cspan\u003eReliability patterns\u003c\/span\u003e\u003cspan\u003e — retry loops, circuit breakers, and fallback chains that prevent the $4,000 outage bill\u003c\/span\u003e\u003c\/p\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cp\u003e\u003cspan\u003eObservability stack\u003c\/span\u003e\u003cspan\u003e — monitor cost, latency, and quality drift before users notice\u003c\/span\u003e\u003c\/p\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cp\u003e\u003cspan\u003eProduction playbooks\u003c\/span\u003e\u003cspan\u003e — step-by-step response guides for cost spikes, latency degradation, and quality regression\u003c\/span\u003e\u003c\/p\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003eEvery chapter ships with production-ready Python code and a complete implementation in the companion code repository. \u003c\/span\u003e\u003cspan\u003eNot pseudocode. Not simplified examples. Code you can run today.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThis is not a book about what LLMs are.\u003c\/span\u003e\u003cspan\u003e It's a book for engineers who already know — and need systems that work at scale without burning budget.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cu\u003eIf your LLM system is in production and the economics aren't working yet, this is the book that changes that.\u003c\/u\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch4\u003e\u003cspan\u003eThe first technique in Chapter 1 takes 20 minutes to implement. Most engineers see measurable results the same day.\u003c\/span\u003e\u003c\/h4\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e","brand":"Books Prime","offers":[{"title":"Default Title","offer_id":45916805365951,"sku":"2655be4a-f347-4364-ab62-a51c7683fe01","price":56.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0761\/7550\/7647\/files\/ed0435d9ba38f38c66c701a8dde5baa6.jpg?v=1785091923","url":"https:\/\/books-prime.com\/products\/llm-inference-engineering-handbook-crush-api-costs-cut-latency-and-build-reliable-production-systems-real-benchmarks-python-code-and-complete-code-repository-for-engineers-at-scale","provider":"Books Prime","version":"1.0","type":"link"}