Weaviate

What is Weaviate?

Weaviate is an open source vector database that stores your data objects together with their vector embeddings, so you can search by meaning, by keyword, or both at once. You define a collection, load objects — Weaviate can generate the embeddings for you through built-in model modules, or accept ones you supply — and query by similarity, with metadata filters and BM25 keyword scoring in the same request. It’s written in Go and speaks REST, gRPC, and GraphQL.

What is Weaviate best for?

Teams building RAG, semantic search, and recommendation features that want vector similarity and keyword (BM25) search handled natively in one engine, rather than bolting keyword search onto a vector-only store. It’s a strong fit when you’d rather have the database vectorize your data for you — its module system connects to OpenAI, Cohere, Hugging Face and others — and when you want the same engine whether you self-host it or run it on managed Weaviate Cloud. In a RAG stack Weaviate retrieves the relevant chunks and can call your generative model to produce the answer, including one you serve yourself with vLLM.

What can Weaviate do?

• Store objects with their embeddings and run vector similarity, BM25 keyword, and native hybrid search in a single query
• Auto-vectorize data at import through pluggable modules for OpenAI, Cohere, Hugging Face, and other providers — or bring your own vectors
• Generate answers in place with RAG/generative modules and sharpen results with built-in reranking
• Filter on structured properties, combine multiple named vectors, and run multimodal (text and image) search
• Isolate customers at scale with multi-tenancy, and lock down access with role-based access control (RBAC)
• Scale out with horizontal replication and sharding, and cut memory use with vector compression (quantization)
• Call it over REST, gRPC, or GraphQL with official clients for Python, JavaScript/TypeScript, Go, Java, and C#/.NET

Is Weaviate free?

Yes — Weaviate’s core is free and fully open source under the permissive BSD-3-Clause license, so you can self-host it at no cost and only pay for your own server. Weaviate Cloud is the optional managed layer: an always-free tier gives you one small cluster (up to 100,000 objects) for prototyping, the pay-as-you-go Flex plan starts at $45/month with a 99.5% uptime SLA, and Premium starts at $400/month with dedicated deployment options and enterprise support. Built-in embeddings and the Query Agent are billed separately on usage.

Where does Weaviate fall short?

• Higher memory footprint. Weaviate tends to use more RAM than leaner engines like Qdrant or pgvector at the same scale, so you lean on vector compression and careful sizing to keep costs in check.
• Its GraphQL query API is powerful but verbose, and the v3-to-v4 Python client was a breaking rewrite — teams that upgraded paid a real migration cost that a simpler REST/gRPC-only client would have avoided.
• It’s schema-first: you define a collection’s structure before loading data, which is more upfront setup than a “just throw vectors in” store like Chroma when all you want is a quick prototype.

What does Weaviate replace?

Weaviate is an open source, self-hostable alternative to Pinecone, the managed serverless vector database. It does the same store-embeddings-and-search-by-meaning job, but you run it on your own infrastructure under BSD-3-Clause instead of paying Pinecone’s usage-based, per-query pricing — and unlike Pinecone it can vectorize your data itself. It’s also commonly compared to other open source vector databases like Qdrant, Chroma, Milvus, and pgvector.

FAQ

Is Weaviate open source? Yes — the core database is fully open source under the BSD-3-Clause license, one of the most permissive there is. The code is public on GitHub and free to self-host, audit, and modify, with no source-available restrictions.

Can I self-host Weaviate for free? Yes. Self-hosting is free under BSD-3-Clause; you only pay for the server it runs on. Weaviate Cloud — including an always-free tier for one small cluster — is the optional managed alternative.

Is Weaviate a good Pinecone alternative? For teams that want native hybrid search and built-in vectorization without per-query pricing, yes. If you’d rather have a fully managed, zero-infrastructure service and don’t want to size or tune servers, Pinecone may be the easier path.

What do I need to run Weaviate? For a quick start, just Docker — a Compose file gets you a working instance with the REST, gRPC, and GraphQL API. For production, size RAM to your vector count (vector search is memory-heavy), enable compression, and add replication and sharding to scale out.