How to Calculate Generative AI Prompt Cache Efficiency
Prompt caching turns repeated or near-duplicate requests into rapid cache hits instead of re-running an expensive large language model completion. Engineering teams deploy lexical caches, semantic vector caches, or hybrid policies to protect GPU capacity and stay within tight latency envelopes. This walkthrough formalises how to translate cache telemetry—hit rate, request mix, and marginal token cost—into defensible efficiency metrics that hold up in quarterly business reviews and cloud cost audits.
We will map definitions, variables, and formulas before stepping through a repeatable workflow that aligns closely with the GenAI latency budgeting guide and the infrastructure scenarios covered by the LLM inference infrastructure cost calculator. Together with the embedded prompt cache efficiency calculator, you can produce reconciled savings statements that engineering, finance, and product leaders trust.
What prompt cache efficiency measures
Prompt cache efficiency expresses how much redundant model generation is avoided because repeated requests are served from a cache. The metric combines avoided token generation, gross-to-net cost savings, and, when available, latency reductions for cache hits. Efficiency is typically reported as cost savings per analysis window or as a percentage of the spend that would have occurred without caching. Organisations also track efficiency against service-level objectives to confirm caching does not erode response quality.
Treat the calculation as a deterministic reconciliation problem: start with the total tokens that would have been generated if every request were uncached, subtract the portion avoided by cache hits, and value the delta using your marginal cost per thousand tokens. This mirrors the reasoning applied when allocating inference budgets in the LLM inference carbon intensity walkthrough, but the emphasis here is on financial impact and user-facing latency.
Variables, units, and data sources
Assemble the following variables for the analysis window (typically one week or one month) and record the measurement system so auditors can reproduce the values.
- Nreq – Total prompt requests served (dimensionless). Pull from API gateway logs or observability counters.
- Tavg – Average total tokens per request, combining prompt and completion tokens (tokens). Use provider billing export or streaming telemetry.
- H% – Cache hit rate expressed as a percentage of requests (percent). Derive from cache statistics, differentiating lexical versus semantic policies if both exist.
- C1k – Marginal cost per 1,000 generated tokens (USD per 1,000 tokens). Blend API fees, reserved capacity amortisation, and orchestration overhead.
- Lhit – Optional end-to-end latency reduction achieved on cache hits (milliseconds). Measure using request traces sampled at the edge.
Augment these with qualitative metadata: cache invalidation rules, maximum TTL, and whether semantic caches fall back to lower-fidelity embeddings under pressure. This context becomes critical when analysts compare efficiency changes alongside synthetic data quality work tracked in the synthetic data coverage score guide.
Formulas for cache-driven avoidance
The core efficiency calculation follows a straightforward chain of multiplications. Express every intermediate in consistent units—tokens for volume, USD for cost, and hours for aggregate latency savings.
Total potential tokens Ttotal = Nreq × Tavg
Cache hit fraction fhit = H% ÷ 100
Avoided tokens Tavoided = Ttotal × fhit
Executed tokens Texec = Ttotal − Tavoided
Gross cost without cache Cgross = (Ttotal ÷ 1000) × C1k
Net cost with cache Cnet = (Texec ÷ 1000) × C1k
Cost savings ΔC = Cgross − Cnet
Latency hours saved (optional) Lhrs = (Nreq × fhit × Lhit) ÷ 3,600,000
The formulas assume marginal cost per token is constant. If you operate multiple model tiers, run the calculation separately for each tier and aggregate the savings. This preserves transparency when reconciling to invoices or amortised GPU leases.
Step-by-step workflow
1. Reconstruct the request baseline
Export request counts and token usage from your provider or from the inference orchestrator. Filter out synthetic load tests and failed retries so the baseline reflects billable or user-visible traffic only. Validate totals against billing statements to ensure parity.
2. Segment cache hit performance
Break down hit rate by endpoint, tenant, or use case. Semantic caches often outperform lexical caches on conversational agents but underperform for retrieval-augmented summarisation. Segment-level analysis reveals optimisation targets, especially when combined with rate-limit insights from the API rate limit planner.
3. Calibrate marginal token cost
Blend on-demand API fees, committed-use discounts, accelerator depreciation, and orchestration overhead (such as vector search infrastructure) into a single marginal cost per thousand tokens. Finance teams should review the figure so the resulting savings can flow into their driver-based models.
4. Quantify savings and latency
Apply the formulas to derive avoided tokens, gross versus net spend, and optional latency hours saved. Maintain two-decimal precision for currency outputs and round token counts to whole numbers for readability.
5. Report and iterate
Present the results alongside cache invalidation policies, TTL adjustments, and product release notes. Highlight how much incremental traffic can be supported before exceeding latency or cost guardrails. Schedule quarterly recalculations to capture shifts in model pricing or cache composition.
Validation, governance, and risk controls
Reconcile token totals against provider invoices or self-hosted telemetry. Investigate discrepancies larger than one percent—they often stem from mismatched tokenisation libraries or missing system prompts in logging. Compare reported hit rates with cache size and eviction patterns to ensure metrics are not inflated by short-lived keys.
Governance teams should review whether cached responses maintain compliance, especially when handling personally identifiable information or regulated financial advice. Document bypass rules for sensitive contexts and include them in change management workflows.
Limits and extensions
The calculation assumes cache hits return identical responses without additional post-processing cost. If you enrich cached outputs with real-time data fetches or re-ranking, include that marginal cost in Cnet. The latency estimate also treats every hit as saving the same duration; in practice, token streaming may reduce perceived latency even on misses, so interpret Lhrs as an upper bound.
For hybrid caching strategies that blend lexical and semantic approaches, calculate efficiency separately for each and report a weighted average. You can also extend the framework to incorporate storage costs or amortised embedding refreshes by subtracting those expenses from the savings figure.
Embed: Prompt cache efficiency calculator
Input request counts, token averages, cache hit rates, marginal token cost, and optional latency savings to produce avoided tokens, reconciled spend, and aggregate latency reductions.