Semantic caching for LLM applications: when similarity reuse is safe

Know how semantic caching differs from exact caching

Exact caching requires the same cache key or stable provider prefix. Semantic caching compares meaning using embeddings or another similarity representation. It can capture paraphrases, but similar language does not always imply that the same answer is valid for the user, account or current data.

Define where reuse is allowed

Separate stable informational tasks from personalized, transactional or rapidly changing requests. Include tenant, permissions, locale and freshness in the cache policy where relevant. Sensitive or high-stakes workflows may require much stricter reuse rules or no semantic cache at all.

Evaluate hit quality as well as hit rate

Measure accepted cache hits, false matches, stale responses, latency and avoided provider usage. A high hit rate is not a success when incorrect reuse creates support work or user harm. Test with representative queries before enabling broad production reuse.

Measurement checklist

1. Choose a representative completed task, not an artificial one-line prompt.
2. Record the selected model, provider input, cached input, output, retries and final result.
3. Change one optimization mechanism at a time so the cause remains visible.
4. Verify required identifiers, tool calls, code changes or business fields.
5. Keep passthrough available when the reduced request does not pass.

Varion commercial evaluation

Varion keeps proprietary product implementation details private. Evaluate Token Optimisation on representative traffic and judge it by the measured commercial result. New verified users receive 100,000 processed input tokens and 50 local test runs.