Benchmarks
Latency, throughput and memory results from our own test runs. 8 articles.
Subscribe with RSSWe put our tiny embedding model up against OpenAI and NVIDIA. It didn't blink.
crowsight is a small, offline embedding model that ships inside Crowkis. We didn't trust it on faith, so we made it compete with the biggest embedding APIs on the one job a semantic cache actually needs. Here's what happened.
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Does the vector index go cold under churn? We tried to break it
A v0.2.1 bug let the HNSW index go cold under heavy write-and-flush churn, semantic search silently stopped finding neighbours. Here's the soak test that reproduces it and proves v0.2.2 fixed it.
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A million vectors on a laptop: the honest vector-search numbers
Crowkis is a cache with a vector index, not a vector database, but it should still hold up at scale. We indexed 100K and 1M vectors and measured build time, search latency, and recall. Including where dedicated vector DBs still win.