Vector Databases · weaviate

weaviate

Weaviate is an open-source vector database written in Go that stores both objects and vector embeddings, enabling semantic search at scale. It combines vector similarity search with keyword filtering, RAG, and reranking in a single query interface, supporting deployment via Docker, Kubernetes, and managed cloud services.

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Source: GitHub — github.com/weaviate/weaviate

16.5k

GitHub stars

1.3k

Forks

Primary language

BSD-3-Clause

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	weaviate/weaviate
Owner	weaviate
Primary language	Go
License	BSD-3-Clause — OSI-approved
Stars	16.5k
Forks	1.3k
Open issues	578
Latest release	v1.38.2 (2026-06-25)
Last updated	2026-07-07
Source	https://github.com/weaviate/weaviate

What weaviate is

Weaviate implements approximate nearest neighbor search (HNSW) with hybrid search capabilities, multi-tenancy, RBAC, and horizontal scaling. It exposes REST, gRPC, and GraphQL APIs; supports integrated vectorizers (OpenAI, Cohere, HuggingFace, Google) or custom vectors; and includes vector compression and TTL features for production workloads.

Quickstart

Get the weaviate source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/weaviate/weaviate.git2cd weaviate3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Semantic Search & RAG Systems

Build retrieval-augmented generation pipelines and Q&A systems that combine vector similarity with keyword filtering, generative search, and reranking in a single query.

Image Search & Content Classification

Implement image search and multimodal classification by storing image embeddings alongside metadata, leveraging hybrid search for precision and recall balance.

Recommendation Engines & Chatbots

Power user recommendation and conversational AI systems with fast semantic search over millions of vectors, built-in multi-tenancy for user isolation, and RBAC for access control.

Implementation considerations

Vectorization strategy: Decide between integrated model providers (OpenAI, Cohere, etc.) or pre-computed embeddings; each has cost and latency implications.
Deployment model: Local Docker for prototyping, Kubernetes for scale, or Weaviate Cloud for managed operations; production requires failover and replication planning.
Hybrid search tuning: Combine BM25 keyword search with vector similarity; requires experimentation with weights and filtering for your domain.
Multi-tenancy and RBAC: Enable per-tenant isolation and role-based access at design time if handling multiple applications or customer data.
Vector compression: Evaluate quantization trade-offs between memory footprint and query accuracy for your SLA.

When to avoid it — and what to weigh

Transactional ACID Guarantees Required — If your application demands strict ACID compliance and complex multi-row transactions, Weaviate's vector-centric model may not be the right fit; consider a relational database.
Small-Scale or Non-Vector Use Cases — For simple key-value lookups or traditional SQL queries without semantic search, overhead and architectural mismatch make Weaviate unnecessary.
Closed-Source / Proprietary Requirements — BSD-3-Clause permissive license requires source attribution; proprietary derivative licensing requires separate commercial agreement (unknown terms from provided data).
Minimal DevOps Capability — Self-hosted deployments require container orchestration, replication setup, and cluster management; lack of internal DevOps expertise favors managed Weaviate Cloud.

License & commercial use

BSD-3-Clause (BSD 3-Clause 'New' or 'Revised' License) – a permissive OSI-approved license. Requires attribution; allows commercial use, modification, and distribution.

BSD-3-Clause is a permissive license permitting commercial use without restriction. However, verify with legal counsel whether any commercial support, indemnification, or warranty claims require a separate commercial agreement. The provided data does not detail commercial licensing terms or SLAs.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Data at rest and in transit security posture not detailed in provided data. RBAC and multi-tenancy features are mentioned but implementation specifics require review. No mention of encryption, audit logging, or vulnerability disclosure process. Requires security assessment before handling sensitive data.

Alternatives to consider

Pinecone

Managed vector database with similar semantic search and RAG capabilities; trade-off: proprietary, higher operational simplicity, vendor lock-in.

Milvus

Open-source vector database (AGPL); similar feature set (ANN, hybrid search, multi-tenancy); trade-off: AGPL licensing may restrict commercial derivatives.

Qdrant

Open-source vector database (AGPL); lighter-weight alternative with similar search capabilities; trade-off: smaller ecosystem, AGPL licensing.

Software development agency

Build on weaviate with DEV.co software developers

Start with Weaviate's local Docker quickstart or connect with our team to plan your vector database architecture at scale.

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weaviate FAQ

Can I use Weaviate with my own pre-computed vectors?

Yes. Weaviate supports both automatic vectorization via integrated model providers and direct import of pre-computed embeddings.

Is Weaviate suitable for production?

Yes. It is built for mission-critical applications with native support for horizontal scaling, multi-tenancy, replication, RBAC, and vector compression.

What programming languages does Weaviate support?

Client libraries for Python, JavaScript/TypeScript, Java, Go, and C#/.NET; REST, gRPC, and GraphQL APIs also available.

Do I need to manage infrastructure myself?

No. Weaviate offers Docker/Kubernetes self-hosted options or managed Weaviate Cloud. Choice depends on your DevOps capability and compliance requirements.

Software development & web development with DEV.co

Adopting weaviate is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate vector databases software in production.

Ready to Build Semantic Search?

Start with Weaviate's local Docker quickstart or connect with our team to plan your vector database architecture at scale.

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