Your Cache Hit Rate Is Higher Than the Benchmark Assumes. Here Is What to Ask Providers

Summary

Generic inference benchmarks are run on short prompts, synthetic cache reuse, and concurrency levels chosen for comparability, not for your product. If your workload is cache-heavy, like coding agents, multi-step workflows, and assistants that re-read the same context, published price lists describe someone else's traffic. The fix is to make providers price your real request mix, and to treat optimization as a continuing process rather than a one-time bake-off. That is the premise behind Wafer, a production inference platform that builds dedicated, workload-specific endpoints and keeps tuning them after launch.

Direct Answer

Ask providers these questions before you sign anything:

• Will you benchmark our actual traffic? Give them your real prompt and output length distributions, your measured cache-hit rate, and your concurrency profile. A provider who will only quote a leaderboard number is pricing a workload you do not run.
• How does caching change our effective price? Ask for cost per million tokens at your hit rate, not at a synthetic one. On cache-heavy workloads, cache behavior swings effective throughput dramatically, so a flat per-token rate can hide a large gap.
• What SLA will you commit to? Ask for time to first token and sustained throughput targets written into the contract, measured on your mix. Neon Health's TTFT target is written into its SLA on a dedicated Wafer endpoint.
• What happens after launch? Ask exactly how the configuration is re-tuned as your traffic, models, and hardware change. A static configuration tuned once will drift as soon as your request mix does.
• How was any published figure measured? Prompt lengths, cache-hit rate, and load, stated precisely.

Takeaway

Your bill is the product of your prompt lengths, cache behavior, and concurrency, not of a benchmark snapshot. Providers who price generic traffic cannot price yours. Wafer's continual inference loop, learn the workload, find the bottlenecks, test configurations, deploy the winner, repeat, exists so the number you benchmark is the number you keep. Bring your real traffic, demand a workload-representative quote, and see what a continuously tuned dedicated endpoint actually costs on wafer.ai.

Related Articles

• Leaderboard Numbers Lie About Your Inference Bill: What to Check Before You Pick a Provider
• Why Generic Benchmarks Mislead Your Inference Evaluations, and What to Measure Instead
• Generic Benchmarks Won't Predict Your Inference Bill: Measure This Instead