Vector & retrieval
Weaviate
Open-source vector database with built-in hybrid keyword-plus-vector search.
- Category
- Vector & retrieval
- Pricing
- Open source
- Runs
- Hosted or self-hosted
- Interface
- API, GraphQL, Client libraries
- Source
- Open source
What Weaviate is
Weaviate ships hybrid search as a first-class feature, combining BM25 keyword scoring with vector similarity and fusing the rankings for you. That matters because embeddings are weak on exact names, codes, and rare terms — the cases users notice most. It can also generate embeddings at ingest through provider modules.
Best for
Corpora containing identifiers, product codes, or proper nouns, where pure vector search visibly misses.
Consider something else if
A larger system to learn and operate than a minimal vector store, with more concepts before your first query.
Weaviate alternatives
The closest options in vector & retrieval, on the axes that actually separate them.
| Tool | Best for | Pricing | Runs |
|---|---|---|---|
| Weaviateopen source | Corpora containing identifiers, product codes, or proper nouns, where pure vector search visibly misses. | Open source | Hosted or self-hosted |
| Qdrantopen source | Filter-heavy retrieval — multi-tenant, permissioned, or time-scoped — where naive filtering degrades results. | 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 Weaviate?
Weaviate ships hybrid search as a first-class feature, combining BM25 keyword scoring with vector similarity and fusing the rankings for you. That matters because embeddings are weak on exact names, codes, and rare terms — the cases users notice most. It can also generate embeddings at ingest through provider modules. It is open source and hosted or self-hosted.
What are the alternatives to Weaviate?
The closest alternatives are Qdrant, 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 Weaviate the right choice?
Corpora containing identifiers, product codes, or proper nouns, where pure vector search visibly misses. The main caveat: A larger system to learn and operate than a minimal vector store, with more concepts before your first query.
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.