Generative AI Prompt Cache Efficiency Calculator

Quantify how cache hit rate, token volume, and marginal inference cost translate into avoided generation spend and faster response times for generative AI workloads.

Number of prompts routed through the inference service in the analysis window.
Sum of prompt + completion tokens that would be produced without caching.
Share of requests served by an exact or semantic cache match.
Fully loaded marginal cost for generating 1,000 tokens on the target model tier.
Defaults to 0 ms. Enter the typical end-to-end latency reduction achieved for cache hits.

Indicative estimator; validate results against production telemetry and financial systems before committing budgets.

Examples

  • 250,000 requests, 850 tokens each, 42% hit rate, $2.40 per 1,000 tokens, 220 ms latency savings ⇒ $214,200 saved and 6.42 latency-hours avoided
  • 90,000 requests, 1,200 tokens, 55% hit rate, $1.80 per 1,000 tokens ⇒ $106,920 saved

FAQ

Can I model semantic cache hit rates that vary by segment?

Yes. Run the calculator per user segment or workload profile with its observed hit rate and token size, then aggregate the savings in your reporting workbook.

How should I estimate the marginal token cost?

Blend provider API pricing, reserved capacity commitments, and infrastructure amortisation to determine an all-in cost per 1,000 tokens so the savings reflect actual cash impact.

What latency should I enter?

Use the measured end-to-end latency delta between cached and uncached responses, including model generation, post-processing, and network delivery. Leave it blank to focus purely on cost savings.

Does the tool account for cache storage costs?

Storage or embedding costs are not deducted automatically. Subtract ongoing cache infrastructure spend from the savings figure to compute net benefit.

Additional Information

  • Avoided tokens equal total generation volume multiplied by the cache hit fraction.
  • Cost savings reflect the difference between uncached spend and spend after removing cache hits.
  • Latency savings convert milliseconds per cache hit into aggregate hours saved per analysis window.

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