Performance and benchmarks

HotKV’s performance numbers live as interactive charts on the homepage, where you can filter by server and pipeline depth rather than read a static table; this page summarizes what is there and links to the full detail.

Throughput benchmarks

The Performance section of the homepage benchmarks HotKV head-to-head against Redis 8.6.2 and Valkey 9.0.3 using memtier_benchmark, with identical parameters, across 6 cloud servers (AWS, Hetzner, and four Google Cloud shapes including an Arm instance), 14 commands, and 2 pipeline depths. It reports commands won, peak throughput, and the percentage difference against each competitor, filterable per server. Full methodology and raw results are available on request from sales@hotkv.com.

Token savings benchmark

We measured how much HotKV’s exact-match and semantic caching save on a realistic LLM workload, rather than estimating it. The token savings section of the homepage has the measured numbers; the full methodology, including where the result does and does not generalize, is in the blog post We measured it: HotKV cut GPT and Claude token use by 60%.

In short: 1,500 customer-support questions from the Banking77 dataset were sent to gpt-6-sol and claude-sonnet-5, twice each, once straight to the model and once with HotKV’s exact and semantic cache in front, with the provider’s own prompt caching left on in both runs. Across both models, HotKV cut the tokens sent to the models by 60% on top of whatever the providers’ own caching was already saving, and cost fell by more than half. The blog post also covers where this does not hold: a workload with little or no repeated traffic saves nothing, and a flatter, less realistic traffic mix in the same test only reached a 41% cache hit rate instead of the measured 60%.

Next steps

• AI features guide to put the prompt and semantic cache to work in your own application.
• Introduction for an overview of what else ships in every plan.