Chroma vs Qdrant
Two of the top vector databases, side by side: score, setup, license, activity and what each review found.
Chroma
Embedding database with a four-function API for Python and JavaScript
Qdrant
Rust vector database with payload filtering, REST and gRPC
| What we compare | Chroma | Qdrant |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 62, popular | 72, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 37, patchy | 82, healthy |
| Easy to run | 50, easy | 33, some setup |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 29.5k | 35k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | May 2026 | Oct 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
Chroma
Chroma is an embedding database with a four-function API (create collection, add, query, get) that tokenizes, embeds and indexes documents itself or accepts your own vectors, with metadata and document filters. It runs in-memory or persisted from the Python or JavaScript client, or as a server via chroma run; the repo ships a Dockerfile and compose file. Chroma Cloud is the hosted serverless version.
Who it is for: developers prototyping RAG who want the simplest API
Strengths
- Four-function API: create collection, add, query, get
- Handles tokenization, embedding and indexing; own vectors optional
- Python and JavaScript clients; chroma run for client-server mode
- Weekly tagged releases on Mondays with hotfixes in between
Weaknesses
- README is thin: no port, resource or auth guidance
- Hosted Chroma Cloud is the headline; self-hosting detail lives in docs
- Row-based API marked coming soon
- No multi-user auth described in the README
- no GPU
- Docker + Compose
- Models: built-in embedding or user-supplied vectors
Qdrant
Qdrant is a Rust vector database exposing REST (OpenAPI 3.0) and gRPC on port 6333 for storing points (vectors plus JSON payload) and searching with dense, sparse and multivector (ColBERT) embeddings, rich payload filters and hybrid fusion (RRF, DBSF). It adds quantization, on-disk storage, sharding and replication, multitenancy, GPU-accelerated indexing and a web UI. Qdrant Edge runs the same engine embedded in-process.
Who it is for: developers wanting a filter-heavy vector store with gRPC
Strengths
- Dense, sparse and multivector (ColBERT) search with RRF and DBSF fusion
- Quantization cuts RAM up to 97 percent; on-disk storage and io_uring
- REST with OpenAPI 3.0 spec plus gRPC; six official clients
- Sharding and replication with zero-downtime collection resize
Weaknesses
- Default docker run has no auth and binds all interfaces
- GPU acceleration covers indexing only; search runs on CPU
- Qdrant Edge embedded mode is Python and Rust only
- Sharding and tenant isolation require upfront design
- GPU optional
- Docker
- Models: any embedding model; dense, sparse and late-interaction (ColBERT) vectors
- port 6333