Vector & Storage
Storage engines for embeddings. Beyond raw search speed, look at metadata filtering, hybrid search and what happens to recall when the collection grows past what fits in memory.
4 projects
ClickHouseObservabilityA column-oriented engine for append-heavy, high-cardinality data, which is the shape agent telemetry takes: one row per model call, filtered later by model, cost or error. Langfuse moved its traces here from Postgres in December 2024 and ClickHouse acquired the project in January 2026, so a self-hosted observability stack increasingly means running this underneath. It is the wrong choice for anything transactional — updates and deletes rewrite whole column parts rather than edit rows — so treat it as where agent runs land, not as the database your application writes state to.
MilvusVector & StorageSeparates storage from compute so indexing and querying scale independently — the architecture you want at billions of vectors. That capability comes with operational weight; below a few million vectors, something simpler will serve you better.
QdrantVector & StorageWritten in Rust and built so that metadata filters apply during search rather than after it — the difference that keeps results correct when an agent queries a narrow slice of a large collection. Runs from a single container for local development up to a distributed cluster.
ChromaVector & StorageThe lowest-friction way to get retrieval working: no server to stand up, no cluster to size. That makes it excellent for prototypes and small production loads, and the point at which you outgrow it is a real consideration to plan for rather than discover.