Weaviate
Open-source vector database with built-in ML modules
Weaviate is an open-source vector database with built-in vectorization modules. It auto-generates embeddings on ingest using OpenAI, Cohere, or HuggingFace models -- no separate embedding pipeline needed.
Editor rating is our overall editorial judgment of the tool, out of 5. It is not a test score and not a user-review average. Ratings have not been re-scored since the tool was listed here. The reasons are in Best for and Pros & cons below.
Requires some technical comfort with API keys, command line, or coding. If you're new to AI, start with a chat-based tool first.
Best for
Pros & cons
✓ Pros
✗ What this won't do
Use cases
Multi-modal search engines
Self-hosted RAG systems
E-commerce product search
Knowledge base retrieval
Create a free cluster
Sign in to the Weaviate Cloud console and create a free cluster. It takes 1 to 3 minutes, and a checkmark appears when it is ready.
Connect from Python
Copy the REST Endpoint URL and Administrator API Key into WEAVIATE_URL and WEAVIATE_API_KEY. Install the client with pip install -U "weaviate-client[agents]".
Add data and search
Create a collection called Movie with the Weaviate vectorizer and add three films. Search with near_text for "sci-fi", and The Matrix comes back first.
Close the connection
Call client.close() when you finish, or use a with block, to free up resources. Keep your keys in environment variables, not in the script.
Compare alternatives
| Tool | Category | Editor rating | Pricing |
|---|---|---|---|
| Weaviate (current) | Vector DB | 4.5 | Free (open-source) / cloud from $45 mo |
| Pinecone | Vector DB | 4.6 | Free tier / from $20 mo |
| Qdrant | Vector DB | 4.5 | Free (open-source) / cloud from $25 mo |
| LangChain | Agent Framework | 4.6 | Free (open-source) / LangSmith from $39 mo |