Semantic Cache Simulator
Vector-based query reuse
Tokens Saved
0
Cache Waiting for Query...
Why Builders Use It
90% Faster
Cached results serve in ~20ms vs 2000ms for LLMs.
$0 Cost
Vector lookups are practically free. LLM output tokens are expensive.
Consistency
Ensure specific questions always get the approved, high-quality answer.
The Distance Rule
Semantic caching uses Cosine Similarity. Unlike a Google search (Exact Match), if your query is vectorially "close" to a stored one, the cache returns a hit.