Lesson 1419 of 2116
Prompt Caching Comparison: Anthropic, OpenAI, Gemini
How prompt caching works across vendors and where it pays off.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2How Context Caching Differs Across Vendors
- 3The premise
- 4Comparing context cache economics across Claude, GPT, and Gemini
Concept cluster
Terms to connect while reading
Section 1
The premise
Prompt caching cuts cost dramatically when cache-hit rates are high — vendor implementations differ in critical ways.
What AI does well here
- Cache long system prompts and tool schemas (all vendors).
- Cut token cost 50-90% on cache hits.
- Reduce latency on cached prefix reads.
What AI cannot do
- Match cache implementations across vendors exactly.
- Cache user-specific or rapidly-changing context effectively.
Key terms in this lesson
Section 2
How Context Caching Differs Across Vendors
Section 3
The premise
Cache primitives differ in TTL, granularity, and pricing; the right design depends on your workload's prefix stability.
What AI does well here
- Identify cacheable prefixes in your prompts
- Estimate savings before integrating
- Choose vendor based on cache fit
What AI cannot do
- Cache content that varies per request
- Make a stale cache fresh
- Replace prompt redesign that yields stable prefixes
Section 4
Comparing context cache economics across Claude, GPT, and Gemini
Section 5
The premise
Caching is a 5-10x cost lever, but the mechanics differ enough that one strategy will not fit all vendors.
What AI does well here
- Compare TTL, breakpoints, and discount per family
- Order static-then-dynamic content the same way each vendor expects
What AI cannot do
- Promise a cache hit on sparse traffic
- Cache content that varies per user
Section 6
AI Prompt Caching: How to Cut Costs 90% on Repeat Context
Section 7
The premise
All major vendors now offer prompt caching: pay extra to write the cache, then 5-10x cheaper reads for the next 5 minutes.
What AI does well here
- Cache long system prompts and few-shot examples
- Cache document context across multi-turn conversations
- Stack stable content first, dynamic content last
- Monitor cache hit rate to confirm savings
What AI cannot do
- Cache content that changes every call
- Magically reduce cost without restructuring your prompt
- Help if your TTL keeps expiring before reuse
- Cache across separate accounts or projects
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