Prompt Caching in Production: When It Pays Off, When It Doesn't
Prompt caching cuts costs 50-90% on the right workloads. The shapes that benefit, the configurations that work, and the gotchas.
Prompt caching cuts costs 50-90% on the right workloads. The substantial 2024-2026 evolution of prompt caching across providers — Anthropic prompt caching, OpenAI cached completions, AWS Bedrock prompt caching — produced substantial economics for substantial workload shapes. This post walks through what actually benefits, the configurations that work, and the gotchas that bite teams.
What prompt caching does#
Prompt caching stores the LLM’s intermediate computation (key-value cache) for substantial prompt prefixes. When a subsequent request has the same prefix, the LLM substantially reuses the cached computation rather than recomputing from scratch.
Cost reduction. Cached tokens typically cost 10-25% of regular tokens. Substantial savings when prefixes are large and reused.
Latency reduction. Substantial latency improvement from not recomputing.
Substantial provider variation. Anthropic, OpenAI, Bedrock implement substantially differently.
Where caching substantially wins#
Several substantial workload shapes:
Long system prompts reused across requests. When all requests share a long system prompt (instructions, formatting, context), caching that prefix saves substantially.
RAG with shared context. When multiple turns of conversation share retrieved context, caching that context saves substantially.
Tool definitions reused. Large tool definitions repeated across requests — caching saves substantially.
Few-shot examples. Long few-shot example blocks reused across requests.
Document-anchored conversations. Documents in context reused across substantial multi-turn conversations.
Customer-specific personas. Long persona definitions reused for substantial customer interactions.
Where caching doesn’t help#
Several substantial scenarios where caching doesn’t pay off:
Highly variable prompts. When each prompt is substantially unique, no prefix to cache.
Very short prompts. Substantial overhead of caching not justified for small prompts.
One-shot use cases. When prompts aren’t reused within cache TTL, caching has substantial cost without substantial benefit.
Trailing variation. When the variable part is in the middle or beginning, the cacheable prefix is short.
The substantial provider differences#
Substantial differences across providers:
Anthropic prompt caching:
- 5-minute default TTL; 1-hour extended option
- Explicit cache_control blocks in prompt
- Substantial 90% savings on cached tokens
- Substantial control over what gets cached
OpenAI cached completions:
- Automatic caching of prompt prefixes
- Substantial savings on cached portion
- Less explicit control than Anthropic
Bedrock prompt caching:
- Available for substantial models on Bedrock
- Mechanism varies by provider
- Substantial AWS pricing integration
The substantial configuration#
For substantial Anthropic prompt caching:
Mark cacheable blocks with cache_control. Substantial design choice: cache the system prompt, cache the RAG context, cache the tool definitions.
Order matters. Cache breakpoints work as prefix matches. Anything before a cache point is implicitly cached.
Multiple cache breakpoints for substantial layered caching — base instructions, then customer context, then conversation history.
TTL choice. 5-minute default suffices for substantial conversational workloads; 1-hour for substantial longer-lived contexts.
The substantial economics#
Substantial savings example:
Workload: 50K input tokens with 5K shared prefix; 100 requests/hour.
Without caching: 100 × 50K = 5M tokens × $3/M = $15/hour input.
With caching of 5K prefix: First request costs 50K × $3/M = $0.15. Subsequent 99 requests: 5K cached at $0.30/M + 45K full at $3/M = ~$13.65 total. Savings: ~$1.35/hour, or 9%.
With longer shared context (45K of 50K cached): Substantial savings at ~$13.50/hour reduction, or ~90%.
The savings substantially depend on prefix length and reuse pattern.
The substantial gotchas#
Several substantial gotchas:
Cache invalidation by minor variations. Any change in the cached prefix — even whitespace — invalidates the cache. Substantial discipline matters.
TTL expiration. Cached content expires; substantial workload patterns matter.
Cache misses appear as full-cost requests. Monitoring matters substantially — high cache miss rate substantially undermines savings.
Substantial cost of cache writes. Some providers charge for cache creation. Math depends on reuse frequency.
Token counting changes. Cached tokens may count differently in usage tracking.
Concurrent request behavior. Behavior varies when multiple concurrent requests would write the same cache.
The decision framework#
For most teams in 2026:
Adopt caching when you have substantial shared-prefix workloads with substantial reuse.
Don’t adopt caching for workloads without substantial shared structure.
Design prompts for caching. Substantial productivity from designing prompts with caching in mind — stable prefix, variable suffix.
Monitor cache hit rate. Track this metric; substantial savings depend on it.
Pick provider based on workload fit. Different caching implementations favor different workloads.
What we typically see at clients#
Common patterns:
No caching deployed. Most enterprises haven’t yet adopted prompt caching. Substantial unfunded opportunity.
Caching deployed without design. Default caching enabled without designing prompts for it. Substantial savings less than possible.
Substantial sophisticated caching deployments at cost-conscious teams — substantial savings achieved.
Where pdpspectra fits#
Our AI integration practice builds production LLM systems with substantial prompt caching and cost optimization.
Related reading: the LLM cost optimization post, the LLM routing post, and the sub-100ms inference post.
Prompt caching is substantial cost lever when designed for. Talk to our team about your AI cost optimization.