Prompt Cache ROI · for API builders
Does prompt caching actually save you money?
Cache economics vary sharply by provider. Anthropic charges a 25% write premium - so caching only saves if your hit rate clears break-even. OpenAI and Google charge no write premium - caching is always free upside.
Pricing verified: 2026-06-05
42 cache-capable models
Break-even analysis
What this calculator does
Does prompt caching actually save you money on your workload — or does it cost you more?
Why use it
- Anthropic charges a 25% write premium — caching can LOSE money below a 22% hit rate
- OpenAI and Google have no write premium — caching is pure upside from hit rate 0.01% upward
- See exact break-even hit rate for your chosen model before enabling cache in production
- Compare cache ROI across all 8 cache-capable models at one click
These are the inputs, outputs, and how you can use this calculator for your AI workloads.
📥 Inputs you provide
- ModelPick a cache-capable model
- Requests per monthTotal monthly call volume
- Input tokens / requestAverage input size
- Output tokens / requestAverage output size
- Cacheable portion of inputStatic-prefix share of input
- Cache hit rateHow often the cache is warm
📤 Outputs you get
- No-cache costMonthly cost with caching off
- With-cache costMonthly cost with caching on
- Monthly savingsDollars saved (or lost) per month
- Break-even hit rateMinimum hit rate to save
🎯 Use your results to
🎯
Find your break-even
See the exact hit rate caching must clear before you flip it on in production
💾
Quantify the savings
Real monthly and annual dollars at your hit rate — not a vendor headline number
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Compare providers
Anthropic vs OpenAI vs Google cache economics side-by-side on the same workload
🔌
Integrate with your agents
MCP available so agentic workflows can pull cache ROI programmatically
👇 Now try the calculator below with your own AI workloads
1
Enter every field
Model determines the cache economics — Anthropic's read discount is 90% but comes with a 25% write premium. OpenAI's 50% read discount has no write premium. Requests/month scales total impact. Input tokens/request matters because cache savings only apply to input; heavy-output workloads see smaller % savings. Output tokens/request scales the uncachable portion of cost.
2
Tune with cacheable/hit sliders
Cacheable % = the static prefix fraction of your prompt (system rules, few-shot examples, retrieved docs). If your system prompt is 800 tokens and query is 200, cacheable = 80%. Hit rate % = how often the cache is still warm when a new request arrives. Depends on TTL (5 min Anthropic, 7 min OpenAI, 1 hour Gemini) vs time-between-calls. High cacheable + low hit rate = cache is expiring before reuse — bump request cadence or use extended TTL.
3
Interpret the 3 cards + break-even bar
Left card = cost without cache. Middle card = cost with cache (based on your sliders). Right card = monthly savings (flips red if cache is costing you money). The break-even bar shows the minimum hit rate needed to save; YOU marker shows where you actually are. Model comparison table at bottom stacks all 8 cache-capable models side-by-side — the winner column often surprises.
4
Next actions
Stack cache savings with routing and batch API for compound savings (often 80%+ total). If your use case has long-lived context (agent loops, stable RAG docs), consider Anthropic's 1-hour extended cache (2× write cost but dramatically more hits). Always verify savings hold at production scale — first-request-after-deploy always misses.
📊 Calculator at a glance
🎛 CALCULATOR
⚙️ Your workload
Start with a preset, then adjust.
What % of your input is reusable prefix (system prompt, docs, few-shot examples)?
% of requests arriving while cache is still warm. Depends on TTL vs time between calls.
📈 RESULTS
No cache
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With cache
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Monthly savings
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Break-even hit rate - your hit rate must exceed this to save
0% (caching always loses)
100% (every request hits)
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💡 Recommendations
📋 Cache ROI across all 42 cache-capable models
Same workload, different models. Green row = biggest savings, gold = your current model.
| Model | Base / cached input | No cache | With cache | Savings |
|---|
📋 What now?
- If you're above break-even — ship caching on the static prefix first (system prompt + tool definitions + few-shot examples). That's ~80% of the savings for ~20% of the effort.
- If you're below break-even on Anthropic — raise the hit rate (consolidate calls, use the 1-hour extended cache) or move that workload to a no-write-premium provider where any hit rate saves.
- Verify before you bank it — measure the real hit rate in production for 1-2 weeks; the first request after each TTL expiry always pays full price, so steady-state numbers differ from the estimate.