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DirectoryAI & ML PlatformsVector Databases & RAGWeaviate

Weaviate

About Weaviate

Weaviate provides an open-source vector database platform for building AI applications. The platform combines vector search, retrieval-augmented generation, and memory capabilities. It stores, indexes, and searches high-dimensional vectors at scale. The database can be self-hosted or run as a managed service. It includes built-in machine learning model connections and hybrid search.

Products

  • Weaviate Shared Cloud: A fully-managed version of the AI database in Weaviate Cloud.
  • Weaviate Dedicated Cloud: An AI database fully managed in a dedicated instance in Weaviate Cloud.
  • Query Agent: Translates natural language questions into database queries.
  • Engram: A managed memory and context service built to help agents remember.
  • Embeddings: Generates vector embeddings within Weaviate Cloud without a third-party provider.
  • Explorer: Searches and validates object data without writing code.

Used for

  • Performing hybrid, semantic, and keyword search with fine-tuned parameters.
  • Getting answers from data using natural language prompts.
  • Combining retrieved context with a model to generate answers.
  • Personalizing recommendations, content, and responses for individual users.
  • Importing data from any source and configuring a collection schema.

Who it is for

  • The product is for developers building AI applications.

From Weaviate's own website, read 2026-10-24. How we build listings.

How to evaluate Vector Databases & RAG

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Weaviate. It comes from our Vector Database & AI Search buyer guide.

25%
Retrieval Quality & Index Options
ANN algorithms offered (HNSW, IVF/IVF-PQ, DiskANN, GPU/CAGRA), achievable recall@k at your latency budget, quantization for memory savings, and whether the index supports real-time inserts or needs rebuilds
20%
Hybrid Search & Filtering at Scale
Native vector+keyword (BM25 / sparse) fusion, reranking, and — critically — in-graph metadata pre-filtering that holds recall under selective filters rather than degrading to brute force or empty result sets
20%
Scale, Performance & Freshness
Vector ceiling per node and horizontally, p95/p99 latency and QPS under your real filters, multi-tenancy and namespaces, multi-region, and how fast new or updated embeddings become queryable (insert-to-searchable lag)
15%
Operating Model & Lock-In
Managed SaaS vs. self-hosted vs. bring-your-own-cloud, license terms (Apache 2.0 / BSD vs. proprietary), data-export and migration path, and how much sync/ETL you take on if vectors live apart from the source data
10%
Security, Governance & Residency
RBAC and API-key scoping, encryption in transit and at rest, SOC 2 / ISO 27001 / GDPR posture, data residency and VPC/air-gapped options, and inherited governance (e.g. Unity Catalog, platform IAM) where applicable
10%
Developer Experience & Ecosystem
Client SDKs and API ergonomics, LangChain / LlamaIndex / framework integrations, built-in or hosted embedding and reranking, docs quality, and operational maturity (backups, observability, upgrades)

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Quick Facts

weaviate.io Pricing page
CategoryAI & ML Platforms
SubcategoryVector Databases & RAG
DeploymentSelf-hosted, Managed service (cloud), Kubernetes package in VPC

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Keep reading

Buyer Guide
Vector Database & AI Search
Job 02
Govern the AI already in use

Staff already use AI tools nobody approved, and most SaaS products now ship AI features by default.

Job 01
Decide where AI goes in the business

The board wants an AI plan, and every function wants to go first.