In-process HNSW vector index: how it works and when to use it
In-process HNSW vector index, keeps a custom, persistent HNSW graph in the same process as the store, so a lookup and its scoring never cross a network hop, sub-millisecond search. Here's how Crowkis does it and why it matters for cost and safety.
The cheapest token is the one you never spend twice. In-process HNSW vector index is how Crowkis keeps a custom, persistent HNSW graph in the same process as the store, so a lookup and its scoring never cross a network hop, sub-millisecond search.
How it works
Crowkis keeps a custom, persistent HNSW graph in the same process as the store, so a lookup and its scoring never cross a network hop, sub-millisecond search. It runs inside one Redis-compatible engine, so it composes with semantic caching, agent memory, and the other intelligence layers instead of being a separate service you wire together.
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. In-process HNSW vector index is part of what makes that reuse safe rather than reckless, the difference between a cache you trust in production and one you audit after every incident. Drop it in over RESP, gRPC, REST, or MCP, no rewrite required.
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