Production & LLMOps · TRACK 03/05
Cost & Latency Optimization
Making it cheaper and faster without making it dumber.
// THE TRACK
01 · START HERESemantic CachingUnderstand how semantic caches match similar questions to stored answers, and the false-hit risks that come with them.BEGINNERThe KV CacheUnderstand the cache that makes token generation fast — and why it's the real reason long contexts cost GPU memory and money.ADVANCEDCutting Token CostsLearn practical ways to shrink your token bill — compression, output caps, context trimming — without degrading answers.INTERMEDIATEPrompt vs Semantic CachingStop confusing the two LLM caches: learn what each one stores, what each one saves, and how production stacks combine both.INTERMEDIATETime to First TokenLearn the difference between TTFT, tokens per second, and total latency — and which one your users actually feel.INTERMEDIATEReducing LatencyWalk away with a toolbox of latency techniques — streaming, parallelism, smaller models — and the quality tradeoffs of each.INTERMEDIATEPrefix CachingYou'll understand how prefix caching reuses the model's KV computation for identical prompt beginnings, and how it differs from provider prompt caching.INTERMEDIATERequest CoalescingYou'll understand how request coalescing (single-flight) serves one in-flight LLM call to many identical concurrent requests, cutting cost and load spikes.INTERMEDIATEGPU Cold StartsYou'll understand why the first request to a self-hosted or serverless LLM is slow, what a cold start actually involves, and how warm pools trade cost for speed.INTERMEDIATECloudflare AI GatewayAfter reading, you'll understand what Cloudflare AI Gateway is, how it sits at the edge in front of your LLM calls, and how its caching, rate limiting, and fallback save cost and improve reliability.INTERMEDIATEGPTCacheAfter reading, you'll understand what GPTCache is, how a semantic cache reuses answers to similar prompts, and how it trades a similarity threshold against accuracy to cut cost and latency.INTERMEDIATELiteLLMAfter reading, you'll understand what LiteLLM is, how it normalizes 100+ models behind one OpenAI-format API, and how its budgets and fallbacks cut cost and risk.INTERMEDIATE