Vector & retrieval
Qdrant
Open-source vector database in Rust, with strong payload filtering alongside similarity search.
- Category
- Vector & retrieval
- Pricing
- Open source
- Runs
- Hosted or self-hosted
- Interface
- API, Client libraries
- Source
- Open source
What Qdrant is
Qdrant is a Rust vector database whose distinguishing strength is filtered search: it applies metadata predicates during the vector search rather than before or after, which keeps results correct and fast when most of your corpus is excluded by a filter. It runs as a single container locally and has a managed cloud tier.
Best for
Filter-heavy retrieval — multi-tenant, permissioned, or time-scoped — where naive filtering degrades results.
Consider something else if
Self-hosting is another stateful service to run, back up, and scale.
Qdrant alternatives
The closest options in vector & retrieval, on the axes that actually separate them.
| Tool | Best for | Pricing | Runs |
|---|---|---|---|
| Qdrantopen source | Filter-heavy retrieval — multi-tenant, permissioned, or time-scoped — where naive filtering degrades results. | Open source | Hosted or self-hosted |
| Weaviateopen source | Corpora containing identifiers, product codes, or proper nouns, where pure vector search visibly misses. | Open source | Hosted or self-hosted |
| Pinecone | Teams who want retrieval to be somebody else's operational problem and are content to pay for that. | Usage-based | Hosted |
| pgvectoropen source | Anyone already running Postgres with up to a few million vectors, which is a larger share of real applications than the category implies. | Open source | Self-hosted |
| Chromaopen source | Prototyping retrieval, notebooks, and small applications where a service is overkill. | Open source | Hosted or self-hosted |
| turbopuffer | Large corpora where only a fraction is queried regularly, and per-tenant indexes that are mostly idle. | Usage-based | Hosted |
Choosing within vector & retrieval
Whether Postgres is already enough
Ask this first and take the answer seriously. At small and medium scale the extension removes a service, a sync problem, and a second consistency model, at very little cost in capability.
Metadata filtering
Real queries are almost never pure similarity — they are similarity within a tenant, a date range, or a permission scope. How well a store combines filters with vector search is the difference that shows up in production.
Questions
What is Qdrant?
Qdrant is a Rust vector database whose distinguishing strength is filtered search: it applies metadata predicates during the vector search rather than before or after, which keeps results correct and fast when most of your corpus is excluded by a filter. It runs as a single container locally and has a managed cloud tier. It is open source and hosted or self-hosted.
What are the alternatives to Qdrant?
The closest alternatives are Weaviate, Pinecone, pgvector, Chroma, turbopuffer. They sit in the same category — vector & retrieval — and differ mainly on hosting model, pricing shape, and how much they abstract away.
Is Qdrant the right choice?
Filter-heavy retrieval — multi-tenant, permissioned, or time-scoped — where naive filtering degrades results. The main caveat: Self-hosting is another stateful service to run, back up, and scale.
Whatever you build on, the model is the line item that scales. See what each one costs per million tokens, or price your own workload.