weaviate
Open-source vector database for storing objects and vectors with structured filtering
GraphCanon updated 2w · GitHub synced 2w · 37 views this month
Decision brief
Weaviate is an open-source vector database with strong support for hybrid searches and scalable cloud-native deployments.
Good fit when
- When you need to integrate both vector search capabilities and traditional SQL-like structured queries into your application.
- If you are building a system that requires flexible deployment options, such as Docker, Kubernetes, AWS, GCP, or managed Weaviate Cloud services.
Avoid when
- If your project requires a proprietary license; Weaviate's open-source nature may not align with restrictive licensing needs.
- When you need immediate access to specific vector embedding models that are not natively supported by Weaviate, without the flexibility of integrating additional models via Docker or Kubernetes.
- Requirements:
- Requires Docker; Deployment on Docker requires a Docker environment.; For cloud deployments, compatible environments such as AWS, GCP require associated accounts and configurations.
Observed Jul 15, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- 12 low (12 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Weaviate. Display-only - separate from trust and ranking.
- Company
- Weaviate·GitHub org profile·1mo
- Funding
- $50,000,000 (2023-04)·GraphCanon curated seed (public press)·1mo
- Commercial model
- Open core·GraphCanon curated seed·1mo
Install
go get github.com/weaviate/weaviateHow it fits your stack(23)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Alternative
Integrates
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Weaviate is an open-source vector database designed for hybrid search operations that integrate both vector search and traditional query conditions. It supports scalable cloud-native architectures and offers deployment flexibility across Docker, Kubernetes, AWS, GCP, and a managed Cloud option.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 2, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 2, 2026
- Languages
- go, python
Source: github.language+pyproject.toml · Aug 2, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 2, 2026)
Install the Python client (or use another [client library](#client-libraries-and-apis)):Source link
Tags
README
Installation
Weaviate offers multiple installation and deployment options:
See the installation docs for more deployment options, such as AWS and GCP.
Getting started
You can easily start Weaviate and a local vector embedding model with Docker.
Create a docker-compose.yml file:
services:
weaviate:
image: cr.weaviate.io/semitechnologies/weaviate:1.36.0
ports:
- "8080:8080"
- "50051:50051"
environment:
ENABLE_MODULES: text2vec-model2vec
MODEL2VEC_INFERENCE_API: http://text2vec-model2vec:8080
# A lightweight embedding model that will generate vectors from objects during import
text2vec-model2vec:
image: cr.weaviate.io/semitechnologies/model2vec-inference:minishlab-potion-base-32M
Start Weaviate and the embedding service with:
docker compose up -d
Install the Python client (or use another client library):
pip install -U weaviate-client
The following Python example shows how easy it is to populate a Weaviate database with data, create vector embeddings and perform semantic search:
import weaviate
from weaviate.classes.config import Configure, DataType, Property
---
## License
BSD 3-Clause License. See [LICENSE](./LICENSE) for details.
For agents
This page has a .md twin and JSON over the API.