Pinecone
Managed vector database for AI applications
Weaviate
Scalable vector database for semantic search and AI applications
Weaviate delivers fast, cloud‑native vector search with hybrid filtering, built‑in RAG, and flexible vectorization, supporting production‑grade scaling, multi‑tenancy, and role‑based access.
Overview
Overview
Weaviate is a cloud‑native vector database designed for developers, data scientists, and enterprises building AI‑powered applications. It stores objects alongside their vector embeddings, enabling semantic search at scale while also supporting traditional keyword filtering.
Core Capabilities
The platform offers automatic vectorization using integrated models from OpenAI, Cohere, HuggingFace, and others, as well as the ability to import pre‑computed embeddings. Its query engine combines vector similarity, BM25 keyword search, image search, and advanced filtering in a single API call. Built‑in Retrieval‑Augmented Generation (RAG) and reranking let you create sophisticated Q&A, chatbot, and summarization pipelines without external tooling. Clients are available for Python, JavaScript/TypeScript, Java, and Go, and the database can be accessed via REST, gRPC, or GraphQL.
Deployment & Operations
Weaviate can be run locally with Docker, orchestrated with Kubernetes, or consumed as a managed cloud service. Production‑ready features include horizontal scaling, multi‑tenancy, replication, and fine‑grained RBAC. Vector compression and quantization reduce memory usage, lowering operational costs while maintaining high query performance.
Highlights
Millisecond‑scale semantic search over billions of vectors
Integrated vectorizers (OpenAI, Cohere, HuggingFace) plus self‑provided embeddings
Hybrid query combining vector similarity, BM25 keyword, and image search in one call
Built‑in RAG and reranking for generative AI pipelines
Pros
- High performance search with Go‑based engine
- Flexible deployment options (Docker, Kubernetes, managed cloud)
- Comprehensive security with RBAC and multi‑tenancy
- Cost‑saving vector compression and quantization
Considerations
- Operational complexity for large‑scale clusters
- Learning curve for hybrid query syntax
- Limited client library support for C# (coming soon)
- Dependence on external embedding services for automatic vectorization
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Fit guide
Great for
- Enterprises building production‑grade semantic search services
- Developers needing rapid prototyping of RAG applications
- Teams requiring multi‑tenant isolation and role‑based access
- Projects that combine text and image similarity in a single index
Not ideal when
- Simple key‑value lookups without vector needs
- Environments lacking container orchestration expertise
- Use cases demanding real‑time streaming ingestion at massive scale without pre‑built scaling
- Applications that cannot rely on external embedding providers and need only raw vectors
How teams use it
Retrieval‑Augmented Generation for Q&A
Provides up‑to‑date answers by retrieving relevant documents and feeding them to LLMs directly from the database.
Semantic image search in e‑commerce
Enables shoppers to find visually similar products using combined text and image vectors.
Personalized recommendation engine
Generates real‑time item suggestions by matching user embeddings with product vectors.
Chatbot with context‑aware retrieval
Delivers coherent conversational responses by fetching relevant knowledge snippets via hybrid search.
Tech snapshot
Go97%
Assembly1%
Python1%
Shell1%
C1%
Makefile1%
Frequently asked questions
How can I deploy Weaviate locally?
Use the provided Docker‑compose file or run the official Docker image; the quick‑start guide walks you through starting the server and an optional embedding model.
Can I import my own pre‑computed embeddings?
Yes, configure the collection with self‑provided vectors and upload the embeddings directly.
Which programming languages have official client libraries?
Weaviate offers first‑party clients for Python, JavaScript/TypeScript, Java, and Go; a C# client is slated for release.
What scaling options are available for production?
Weaviate supports horizontal scaling via Kubernetes, replication across nodes, and multi‑tenant deployments, plus a managed cloud offering.
How does security and access control work?
Fine‑grained RBAC lets you define roles and permissions per collection, and multi‑tenant isolation ensures data separation between tenants.
Project at a glance
Active- Stars
- 15,449
- Watchers
- 15,449
- Forks
- 1,180
LicenseBSD-3-Clause
Repo age9 years old
Last commit3 hours ago
Primary languageGo
Last synced 2 hours ago