AI economics
Learn when prompt caching helps, how OpenAI, Anthropic, and Gemini caching differ, and how to design prompts, RAG context, and agent workflows for cache hits.
Comparison
| Option | Best for | Strengths | Tradeoffs | Use when |
|---|---|---|---|---|
| Automatic caching | Apps where the provider detects repeated prompt prefixes without application-managed cache objects | Minimal code change and easy to adopt. | Less explicit control over cache lifetime and hit behavior. | You have stable prompt prefixes and want lower latency or cached-token pricing with low engineering overhead. |
| Explicit caching | Long documents, reusable corpora, policies, and workloads where the app can declare cached content | More predictable reuse when supported by the provider. | Requires lifecycle management, invalidation, and provider-specific implementation. | You repeatedly ask questions against the same large context. |
| Application-level caching | Stable final answers, deterministic transformations, summaries, and offline processing | Works across providers and can eliminate model calls entirely. | Harder when answers depend on fresh data, user permissions, or changing model behavior. | The same input should produce the same output or can be safely reused. |
Most cache wins come from keeping the repeated part of the prompt stable. Put system instructions, tool schemas, examples, and reusable context before highly variable user content when provider rules reward prefix reuse.
Caching matters most when repeated input is large: long codebase context, enterprise policy docs, tool definitions, multi-agent instructions, or repeated evaluation prompts.
Cache misses often come from tiny prompt changes that look harmless: dynamic dates, reordered tools, unstable JSON, different model IDs, or inserting user-specific metadata before the stable prefix.
Decision Rules
Use prompt caching when repeated context is large and stable.
Use application caching when the whole answer can be safely reused.
Do not redesign prompts only for caching if answer quality drops.
Measure cache hit rate, latency, cost, and quality together.
Related Guides
Connect caching to token cost and monthly product economics.
OpenEstimate how cached input changes cost at scale.
OpenEstimate monthly cost with cached and uncached input.
OpenUse async batch processing when latency is not required.
OpenUnderstand why repeated long context becomes expensive.
OpenChinese Archive
Topic Hubs
FAQ
Prompt caching lets a provider reuse computation for repeated prompt content, usually reducing latency and input-token cost when the repeated section is large enough and matches provider rules.
No. It helps when prompts reuse large stable content and hit the cache. It helps less when every request has different prefixes or small inputs.
Use RAG to retrieve the right evidence from a large corpus. Use prompt caching when the same large context or instructions are sent repeatedly.
Source Links
The strongest decision is always local to your workflow. Save the vendor links, define a representative task, record the exact prompt or command, and compare the final evidence instead of the marketing claim.
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