AI Response Caching: How to Reduce Latency and Cut LLM Costs by 80%
TL;DR
LLM inference is expensive and slow. Caching is the highest-leverage optimization available — but most teams implement it wrong. There are four distinct caching layers for AI products: exact-match, semantic, prompt prefix (KV cache), and result caching. Understanding which layer to use, and when, is what separates AI products that scale economically from ones that burn compute on every request.
Why Caching Is the Most Important AI Optimization
Before optimizing prompts, compressing context, or routing to smaller models, implement caching. In most AI products, a significant fraction of requests are functionally identical or semantically equivalent. Serving those from cache eliminates the inference cost and reduces latency to near-zero.
Cost reduction potential
In products with repetitive queries — search, FAQ, summarization of the same documents — cache hit rates of 40–70% are common. At 70% hit rate, you reduce inference costs by 70%, since cached responses cost nothing to generate. At scale, this is the difference between a sustainable margin and an unsustainable one.
Latency improvement
LLM inference for a typical request takes 1–5 seconds. A cache hit returns in milliseconds. For user-facing features where responsiveness determines adoption, this is often more impactful than any other optimization. Users forgive slow AI; they abandon it.
Consistency
Caching also improves output consistency. Non-deterministic LLM responses mean two identical requests can produce different outputs. For many product use cases — legal summaries, medical information, structured data extraction — consistency is a product requirement, and caching enforces it.
Rate limit management
Most LLM APIs enforce rate limits. High-traffic products hit these limits at peak load. Caching reduces the number of API calls, flattening the load curve and making your rate limit headroom go further.
The Four Caching Layers
Exact-match caching
Identical inputs produce the same desired output
Hash the full prompt (system + user message) and store responses in a key-value store (Redis, Memcached). On each request, check the cache before hitting the LLM. Works perfectly for FAQ systems, search queries that repeat, and structured data extraction on fixed templates.
Semantic caching
Queries with different wording have equivalent intent
Embed the incoming query and compare it against a vector index of previously answered queries. If similarity exceeds a threshold (typically 0.95+), return the cached response. Works for natural language queries where users ask the same question in different ways. Requires a vector database and embedding model — adds infrastructure cost but dramatically improves hit rates for conversational products.
Prompt prefix caching (KV cache)
Many requests share a long system prompt or document context
Most major LLM APIs (Anthropic, OpenAI) now support prompt caching — they cache the key-value state of a shared prompt prefix and charge a fraction of normal input token cost on cache hits. If your product sends a 10,000-token document with every request, prefix caching can reduce input costs by 80–90%. Enable it by structuring prompts to put stable content (system prompt, documents) before variable content (user query).
Result caching
Output of a pipeline step is reused across multiple downstream requests
For multi-step AI pipelines (retrieval → summarization → formatting), cache intermediate outputs, not just final responses. If you summarize a document once, cache the summary and reuse it for all queries on that document. This layer requires designing your pipeline with cacheable boundaries — a systems design decision that should happen early.
Cache Design Decisions
TTL (time-to-live) strategy
How long should cached responses live? For static content (FAQ, document summaries), long TTLs (days/weeks) make sense. For market data, news summaries, or anything time-sensitive, shorter TTLs (minutes/hours) prevent stale responses. Wrong TTL settings are the most common caching mistake — stale AI responses erode trust.
Cache invalidation
When the underlying model changes (new version, different system prompt), cached responses may no longer represent what the new model would return. Build explicit cache invalidation tied to model version or prompt hash, so you don't serve old model outputs after a prompt update.
Semantic similarity threshold
For semantic caching, the similarity threshold determines cache hit rate vs accuracy trade-off. Too low: return irrelevant cached responses. Too high: near-zero hit rate. Start at 0.97 and tune downward by evaluating sample queries against cached responses manually. Each product has a different optimal threshold.
Cache storage cost vs compute cost
Semantic caching requires a vector database, which has its own storage and query cost. For low-volume products, the infrastructure overhead may exceed the LLM cost savings. Benchmark your specific use case before committing to the infrastructure investment.
Build AI Products That Scale Economically
LLM cost optimization, caching strategy, and infrastructure decision-making are covered in the AI PM Masterclass. Taught by a Salesforce Sr. Director PM.
Prompt Caching: The Provider-Level Optimization
How prompt caching works
When you send a long prompt, the LLM computes key-value (KV) states for every token. These computations are expensive. Prompt caching stores those KV states on the provider side. The next request that shares the same prefix doesn't recompute them — it loads them from cache. The savings are substantial: Anthropic charges 90% less for prompt cache hits on Claude.
Structuring prompts for cache efficiency
To benefit from prompt caching, the cacheable portion of your prompt (system instructions, document context, few-shot examples) must appear at the beginning of the prompt, before the variable portion (user query). This is the opposite of how many PMs intuitively structure prompts. Audit your current prompt structure with this in mind.
When prompt caching has the biggest impact
Products with long, stable system prompts (customer service bots, document Q&A, code assistants with large codebases in context) see the largest savings. If your system prompt is 500 tokens, prompt caching saves little. If it's 50,000 tokens, prompt caching can cut your cost per request by 70% or more.
Measuring Cache Effectiveness
Cache hit rate
The percentage of requests served from cache. Track this by caching layer — exact-match hit rate, semantic hit rate, and prompt cache hit rate independently. A combined hit rate below 20% suggests your caching strategy is mismatched to your query distribution; above 60% is excellent for most products.
Cost per request (cached vs uncached)
Calculate average cost per request before and after caching implementation. This is your clearest ROI signal. For products with high cache hit rates, you should see 40–80% cost reduction. Track this monthly — query patterns change, and so do cache hit rates.
Latency p50 and p99
Cache hits should dramatically improve p50 latency. But watch p99 — if cache misses are getting slower (due to cold starts, larger prompts), your tail latency may worsen even as your average improves. Good caching improves both, but the implementation details matter.
Stale response rate
For any cache with TTLs, monitor the rate of requests served stale responses after the underlying data changed. High stale rates indicate your TTL is too long relative to your data update frequency. Add a staleness signal to your monitoring so you can catch this before users do.
Master AI Cost Optimization in the AI PM Masterclass
Caching, cost modeling, and infrastructure decisions are part of the technical PM curriculum. Taught by a Salesforce Sr. Director PM.