Weaviate Review: Open-Source Vector Search
TL;DR: Weaviate is one of the most feature-rich open-source vector databases going. The GraphQL interface is genuinely powerful, the module system lets you bolt on what you need, and the managed cloud takes the operations headache off your plate. It is reportedly a touch slower than Pinecone, but you get a lot more room to move.
If your business is building anything with AI right now, sooner or later you run into a plumbing problem nobody warned you about: where do you store the numbers? Modern AI features, the search box that understands what you mean rather than the exact words you typed, the chatbot that pulls answers from your own documents, all of it runs on "vectors", long lists of numbers that capture meaning. You need somewhere to keep them and search them fast. That is what a vector database does, and Weaviate is one of the names that keeps coming up.
The pitch is straightforward. Weaviate is open source, so you can run it on your own servers for free and see exactly how it works, or pay someone else to run it for you. It is built to be flexible. You can plug in different AI models, search across text and images, and shape your data the way you want it.
The trade-off, as with most flexible tools, is that flexibility asks something of you in return. There is more to learn and more to set up than with a lock-it-and-leave-it product. For Australian teams weighing up where to put their AI data, the question is whether that control is worth the extra effort. For a lot of teams, it is.
Here is how it stacks up.
What Is Weaviate?
Weaviate is an open-source vector database:
- Vector + semantic search, native understanding
- GraphQL interface, query with a familiar syntax
- Modular design, plug in vectorisers, generators, rankers
- Multi-modal, text, image, and (via the CLIP and ImageBind modules) other modalities such as audio
- Self-hosted or managed, flexibility in deployment
- Schema-first, define data structures explicitly
Price: Open source (free) | Cloud reportedly from around $45/mo on the current Flex tier (older listings quoted $25/mo before the October 2025 pricing change, check the official pricing update) | Enterprise custom
GraphQL Interface
The GraphQL interface is what sets Weaviate apart from most other vector databases:
{
Get {
Article(
nearText: { concepts: ["AI automation"] }
limit: 5
) {
title
summary
_additional { certainty }
}
}
}If your team already uses GraphQL, this will feel like home. If you have only ever worked with REST APIs, expect to spend a bit of time getting your head around it.
Module Ecosystem
Weaviate's modules are where you add capabilities:
| Module | Purpose |
|---|---|
| text2vec-openai | OpenAI embeddings |
| text2vec-cohere | Cohere embeddings |
| qna-openai | question answering |
| generative-openai | RAG generation |
| reranker-cohere | result re-ranking |
| multi2vec-clip | image vectors |
Pros and Cons
| Pros | Cons |
|---|---|
| Rich feature set | GraphQL learning curve |
| Truly open source | Reportedly a little slower than Pinecone |
| Excellent module system | Schema management adds complexity |
| Multi-modal support | Self-hosted needs DevOps |
| Affordable managed option | Documentation gaps |
Verdict
Score: 8.5/10
Weaviate is the vector database for teams that need room to move. The module system, the GraphQL interface, and the multi-modal support all earn their keep. If you want open source with options, pick Weaviate. If you would rather hand over the operations and keep things simple, Pinecone is the easier call.
*Published June 19, 2026. Note: this review reflects an earlier Weaviate build (originally tested against v1.28); the project has since moved on considerably, so check the current release notes for the latest version.*