Weaviate

Overview

Weaviate is an open-source vector search engine that enables developers to build AI-powered applications. It supports semantic search, hybrid search, and integrates with various ML models. With a flexible schema and GraphQL API, it simplifies storing and querying vectorized data.

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Key Features

• Vector search
• Hybrid search
• GraphQL API
• Integration with ML models
• Flexible schema

Pros

• Open-source
• Easy integration with ML models
• Supports hybrid search
• Fast vector indexing

Cons

• Learning curve for new users
• Requires understanding of vectors
• Limited documentation for complex use cases

Who Is This Tool Best For?

Developers building AI applications needing vector search capabilities.

Weaviate: the common questions

What is Weaviate?

A database built for vectors: it stores embeddings and searches them by similarity, with hybrid search that combines vector results with keyword matching.

Is Weaviate right for you?

Developers building retrieval into an application — search, recommendations, or grounding a model in documents — who need the storage and query layer rather than a product on top.

Is Weaviate worth paying for?

The database is open source and free to run yourself. The paid products are the managed cloud and enterprise features, so the cost appears when you stop operating it.

Why not keep vectors in an existing database?

You can, at small scale. A dedicated engine matters when the index grows, when queries mix semantic and keyword search, and when filtering has to happen inside the vector query rather than after it.

Use cases

Semantic search over your own data

Finding documents by meaning rather than by shared words is the base capability everything else is built on.

Grounding a model in documents

Retrieval before generation is how an assistant answers from your material rather than from training.

Combining filters with similarity

Filtering by tenant or date inside the vector query is the part that is awkward in a general database.

Alternatives

• A general database with a vector extension — One system to operate, and weaker at scale.
• A managed retrieval service — No infrastructure and less control over ranking.
• An in-memory index in your application — Fine for thousands of vectors and not for millions.

Our AI tools directory is where each of them is catalogued.

Pricing

Weaviate is usable without paying, scored 4.5 in our listing. Open source and free to self-host, with a managed cloud and enterprise features carrying the paid plans, so the cost appears when you stop operating it yourself. Details are on the vendor site. Ask the Weaviate site before you budget against this.

Before you adopt it

Plan the embedding model first

The vectors are only meaningful alongside the model that produced them. Changing the model means re-embedding everything.

Measure recall, not speed

A fast index that misses the right document is worse than a slow one that finds it. Test retrieval quality before tuning performance.