Weaviate
What is Weaviate?
Weaviate is an open-source vector database for AI engineers that combines vector search, BM25 keyword search, and hybrid search to power retrieval-heavy applications. It includes retrieval augmented generation, agentic AI, and native multi-tenancy, and integrates with LangChain, LlamaIndex, OpenAI, and Anthropic. Customers include Instabase, Kapa, and Neople. Plans run Free Trial, Flex at $45/month, Premium at $400/month, and Weaviate Agents at $30/organization.
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At a glance
- Weaviate is best for AI engineers who need fast retrieval, RAG, and scalable vector search.
- Free Trial Free 14-day trial; Flex $45 /mo; Premium $400 /mo; Weaviate Agents $30 / organization
- 14 days, no credit card
- Yes — Weaviate provides developer documentation for its open-source vector database and model provider integrations.
What does Weaviate do?
Weaviate handles AI application data retrieval by combining vector search, BM25 keyword search, and hybrid search so teams can surface relevant results without extra overhead. Its open-source database supports out-of-the-box RAG, advanced filtering, vectorizer modules, and agentic workflows that query, transform, and personalize data. Developers can connect models and frameworks as the ecosystem changes, then use the same platform to build search, retrieval, and multi-step AI experiences. At scale, Weaviate is used by over 50,000 AI builders and has over 20M open source downloads, with thousands of customers running production workloads. Case studies show 42M vectors in production, millions of customer questions, and 90% faster search. It can be self-hosted or run as a managed service, and the platform shows secure deployment, backups, multi-tenancy, and tenant isolation. Customers named on the site include Instabase, Morningstar, Kapa, and Neople.
Why use Weaviate?
- Open-source and self-hostable, so teams can keep control over deployment and infrastructure choices.
- Hybrid search combines vector and keyword retrieval, which helps teams improve relevance without stitching systems together.
- Built-in agent workflows let teams move from search to transformation and personalization in one platform.
- Managed cloud and dedicated cloud options reduce operational overhead while preserving a path to stricter compliance needs.
- Multi-tenancy, tenant isolation, backups, and RBAC support production use cases that need tighter data controls.
Who is Weaviate for?
- AI engineers who need a database layer for retrieval-heavy applications.
- Platform teams who want self-hosted or managed deployment options.
- Data engineers who need to connect diverse sources into AI workflows.
- Product teams who need search and personalization inside AI apps.
- Enterprise builders who need multi-tenancy, backups, and access controls.
What are Weaviate's key features?
AI-powered search
Run hybrid search across vectors and keywords with GraphQL or REST, helping teams return relevant results faster at production scale.
Retrieval augmented generation
Build RAG pipelines with LangChain, LlamaIndex, or Haystack, so LLM apps can ground answers in indexed data instead of prompts alone.
Agentic AI
Connect agents through CrewAI, Agno, or n8n to let applications query data, trigger actions, and automate multi-step workflows.
Billion-scale architecture
Support large production workloads, including 42M vectors in production and over 20M open source downloads, for teams that need proven scale.
Enterprise-ready deployment
Choose shared or dedicated cloud on AWS, Google Cloud Platform, or Microsoft Azure, with RBAC, uptime targets, and external monitoring.
Native multi-tenancy
Isolate tenants inside one database with multi-tenancy and tenant isolation, which matters for SaaS products serving separate customer datasets.
Vector index compression
Use compression by default plus configurable compression techniques to reduce storage and speed up retrieval without changing application queries.
Seamless model integration
Plug into OpenAI, Anthropic, Cohere, Hugging Face, and Mistral through model provider integrations for embedding and generation workflows.
What does Weaviate integrate with?
- GraphQL
- REST
- AWS
- Snowflake
- Databricks
- Google Cloud Platform
- Amazon Web Services
- Microsoft Azure
- Firecrawl
- Context Data
- Contextual AI
- Aryn
- Airbyte
- Cardinal
- Confluent
- Astronomer
- Unstructured
- Replicate
- Modal
- Composio
- Semantic Kernel
- Haystack
- LlamaIndex
- LangChain
- DSPy
- KubeAI
- Anyscale
- FriendliAI
- Mistral
What are Weaviate's use cases?
AI engineers build RAG apps
AI engineers use Weaviate to power retrieval-heavy applications, combining Retrieval augmented generation with Built-in hybrid search to surface the right context before the model answers. They can also use smooth model integration to swap providers without rebuilding the database layer.
Product search for apps
Product teams use Weaviate to add search and personalization inside AI apps, relying on AI-powered search and Advanced filtering to return relevant results fast. With Personalization Agent, they can tailor recommendations and discovery flows to what each user is most likely to want.
Enterprise deployment with controls
Enterprise builders use Weaviate to run AI workloads with Native multi-tenancy and Tenant isolation, keeping customer data separated while scaling. Enterprise-ready deployment and Configurable backups help them meet reliability and access-control requirements without stitching together separate infrastructure.
Platform teams ship managed clusters
Platform teams use Weaviate to choose between self-hosted and managed deployment paths, then standardize on Billion-scale architecture for production growth. Weaviate Shared Cloud and Weaviate Dedicated Cloud give them flexible rollout options as usage and compliance needs change.
How does Weaviate work?
- Connect your first data source through REST or GraphQL, then create a Collection to define the schema for your AI workload and prepare it for retrieval.
- Load embeddings with your preferred Model Providers, or use Vectorizer modules to generate them automatically so your content is ready for semantic search.
- Turn on Built-in hybrid search and Advanced filtering in Query to test relevance, then refine results in Explorer before exposing them to your app.
- Add Retrieval augmented generation or Agentic AI with Database Agents, wiring Query Agent or Transformation Agent into your workflow for grounded answers and automated actions.
- Deploy on Weaviate Shared Cloud, Weaviate Dedicated Cloud, or self-hosted infrastructure, then use Configurable backups, Native multi-tenancy, and Tenant isolation to keep production stable.
How much does Weaviate cost?
Free Trial
Free 14-day trial- Free 14-day trial
- Sandbox cluster
- Baseline security
- Various compression techniques.
- Compression by default.
- Support
Flex
$45 / month- Pay-as-you-go, monthly instant, no-commit entry point.
- Shared cloud cluster that delivers Weaviate's full core DB toolkit (hybrid search, replication, dynamic index, compression, multi-tenancy).
- Baseline security with RBAC.
- Various compression techniques.
- Highly available clusters, 99.5% uptime.
- Compression by default.
- Email support, next-business-day Severity 1 response.
Premium
$400 / month- Prepaid contract with predictable spend.
- Choice of shared or dedicated deployment for high performance & compliance.
- Trusted reliability - up to 99.95% uptime.
- Various compression techniques.
- Metrics endpoint for external monitoring.
- Global coverage for dedicated deployment on AWS, GCP & Azure.
- Enterprise support: as low as 1-hour Severity 1 response, Technical Account Team and access to training and experts.
Weaviate Agents
$30 / organization- Free to try
- Monthly plan
- 4000 requests included
- Onboarding packages
- Enterprise support
- Free on-demand learning
- Live training
Frequently asked questions
What is Weaviate?
Weaviate is an open-source vector database for AI engineers that combines vector search, BM25 keyword search, and hybrid search to power retrieval-heavy applications. It includes retrieval augmented generation, agentic AI, and native multi-tenancy, and integrates with LangChain, LlamaIndex, OpenAI, and Anthropic. Customers include Instabase, Kapa, and Neople. Plans run Free Trial, Flex at $45/month, Premium at $400/month, and Weaviate Agents at $30/organization.
How much does Weaviate cost? Is it free?
Weaviate has a free plan, with paid tiers including Flex at $45 / month, Premium at $400 / month, Weaviate Agents at $30 / organization. A 14-day free trial is available.
What is Weaviate used for? Who is it for?
Weaviate is used for AI-powered search, Retrieval augmented generation, and Agentic AI. It's built for AI engineers, Platform teams, and Data engineers.
Does Weaviate have an API and what does it integrate with?
Weaviate provides developer documentation for its open-source vector database and model provider integrations. It integrates with GraphQL, REST, AWS, Google, Snowflake, and 25 more.
Editor's read
Check whether your workload needs shared or dedicated deployment before committing. Premium includes that choice, while Flex is shared cloud only; if compliance or performance requires dedicated infrastructure, the lower tier will not cover it.
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