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LLM Cost & Capacity Calc – Prompt Cache Savings
x402
LLM Cost & Capacity Calc – Prompt Cache Savings is a paid API for AI agents from llm-cost-calc.x402tookit.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-29).
Calculates prompt-caching cost savings for a shared prefix: costs with and without caching, break-even request count, and percent saved.
Price / call$0.02
Success—
StatusUnknown
How this service works
Prompt-cache savings for a shared prefix: cost with and without caching, break-even requests and percent saved.
Output
Returns a JSON object with: total cost with caching, total cost without caching, dollar savings, savings as a percentage, break-even number of requests required for caching to become profitable, cache read and write multipliers used, a human-readable summary, and a verdict on whether caching is advisable.
Example use cases
Evaluating caching for a RAG pipeline
“My RAG system prefixes every query with a 15000-token document chunk, and I send about 500 requests per day at $2 per million input tokens. Using default cache-read (0.1x) and cache-write (1.25x) multipliers, how much would I save with prompt caching and how many requests does it take to break even?”
Comparing caching tiers for a chatbot
“We run a customer support chatbot with a 5000-token shared system prompt across 1000 daily sessions, paying $1.50 per million input tokens. Can you work out whether prompt caching is worth enabling — show me the cost with and without caching and the percent saved?”
Sizing cache budget for a new LLM product
“I'm launching an AI product where every user request shares a 20000-token prefix. If I expect 50 requests reusing that prefix, my input price is $5 per million tokens, and cache reads cost 0.1x with writes at 1.25x, what's my total cost with caching versus without and what percentage am I saving?”
Technical details
Endpoint
GEThttps://llm-cost-calc.x402tookit.com/cache-savings?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need a quick, parameterized calculation of whether LLM prompt caching is cost-effective for a specific workload — particularly when you have a shared prefix (system prompt, RAG document, few-shot examples) reused across many requests and want to quantify break-even points and savings before committing to a caching strategy. Prefer this over manual spreadsheet math or generic cost calculators when you need to factor in provider-specific read/write multipliers.
Side effects
- Missing required query params (prefixTokens, requests, inputPricePerMTok) returns a 400 error
- requests value of 0 or negative may cause division errors or nonsensical break-even output
- Extremely small prefix token counts may yield negative or zero savings depending on multipliers
- Invalid multiplier values (e.g. readMultiplier >= 1) may produce unexpected non-savings results
- Network or upstream service unavailability returns a 5xx error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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