Start here for the decision-making essentials: what Weaviate does, who it is for, how it is accessed, and the first-party sources behind this profile.
Pricing1 plans
Flexper month
from $45/month minimum
baseline cluster plus Vector Dimensions and Storage
backups charged on top
Platforms
Cross-platform
API accessNot public
FoundedNot disclosed by source
AvailabilityWeb / remote
LicenseYes
Best suited to
Source-backed fit
Enterprise organizations needing multi-tenant, multi-billion-vector RAG… Teams building retrieval-augmented generation and agentic AI applications Developers wanting hybrid search across code and docs in IDE tools like… Regulated industries requiring SOC 2 and HIPAA-compliant vector infrastructure
AI infrastructure · Tool
Weaviate
Decision-ready
Weaviate is an open-source, deployment-agnostic AI database platform that unifies vector search, RAG, and memory capabilities to help developers build AI-native applications with reduced hallucination and vendor lock-in.
Short answers to the questions buyers and builders commonly ask about Weaviate. Each answer cites the shared ledger below, where every source is listed once.
01What does Weaviate say it can do?
Vector database supporting vector search, keyword search, hybrid search, filtering, multi- · Vector search, keyword search, hybrid search, filtering, multi-tenancy, and retrieval-augm · Pre-built agentic workflows that dynamically interact with data to perform sync, async, an · Query the database in natural language
Weaviate is an open-source, AI-native vector database that stores objects and vectors together and supports vector search, keyword search, hybrid search, filtering, multi-tenancy, and retrieval-augmented generation.
Multi-tenant retrieval-augmented generation (RAG) for AI applications across thousands of · Financial data research and analysis platform processing large-scale documents · Powering AI agents for high-volume insurance claims processing · Memory built for AI agents that remember, learn, and continually improve over time, includ
He needed a RAG retrieval system that could scale across thousands of customers, each with their own isolated datasets, while maintaining high-quality semantic search.
04What pricing information is available for Weaviate?
Flex | from $45/month minimum | baseline cluster cost including Vector Dimensions and Stor · Premium | from $400/month minimum | baseline cluster cost including Vector Dimensions and
The minimum covers the baseline cluster cost and includes charges for Vector Dimensions and Storage: $45/month on Flex
Seamlessly connect data with models from a range of providers · Built-in MCP server providing hybrid search over code and docs to Claude Code, Cursor, and · Composable templates for common use cases plus extensible properties and composable pipeli
Seamlessly connect your data with models from a range of providers
This profile connects the jobs Weaviate is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.
Common use cases
Multi-tenant RAG across thousands of isolated customer indexes
Financial data research and analysis processing millions of vectors
AI-native semantic search applications with hybrid retrieval
Agentic workflows performing sync, async, and multi-step data tasks
Natural-language querying of enterprise databases via the Query Agent
Multi-tenant retrieval-augmented generation (RAG) for AI applications across thousands of
Flex | from $45/month minimum | baseline cluster cost including Vector Dimensions and Stor · Premium | from $400/month minimum | baseline cluster cost including Vector Dimensions and
API
Not publicly listed
Source links
17 recorded
Source-backed
Verified facts
Updated September 16, 2026
Each fact points to a recorded source, making it easy to distinguish verified product information from claims that need checking.
Official website
HTTP 200 verified twice
First-party description
The AI database developers love | Weaviate
Source-supported facts
Weaviate is an open-source, AI-native vector database · Stores objects and vectors together · Supports vector search, keyword search, hybrid search, filtering, multi-tenancy, and RAG
Weaviate is an open-source AI-native vector database
Platform
Cross-platform
Company
Weaviate B.V.
Headquarters
Prinsengracht 769A, 1017 JZ, Amsterdam, NL
View 32 more verified facts
Deployment
Serverless, Weaviate Enterprise Cloud, and Bring Your Own Cloud deployment options
Security
Data fully encrypted in transit and at rest, with granular access controls and multi-tenant isolation
Audit
Weaviate Cloud is SOC 2 audited; Enterprise Cloud on AWS is HIPAA compliant
Pricing
Flex | from $45/month minimum | baseline cluster cost including Vector Dimensions and Storage; backups charged on top
Practical capabilities
What it helps with
8 documented areas
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
Use case
Multi-tenant RAG across thousands of isolated customer indexes
Use case
Financial data research and analysis processing millions of vectors
Use case
AI-native semantic search applications with hybrid retrieval
Use case
Agentic workflows performing sync, async, and multi-step data tasks
Use case
Natural-language querying of enterprise databases via the Query Agent
Use case
Multi-tenant retrieval-augmented generation (RAG) for AI applications across thousands of
Use case
Financial data research and analysis platform processing large-scale documents
Use case
Powering AI agents for high-volume insurance claims processing
Availability
Where it runs and where to get it
Source checked
Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.
Cost / license
Flex | from $45/month minimum | baseline cluster cost including Vector Dimensions and Stor · Premium | from $400/month minimum | baseline cluster cost including Vector Dimensions and YesOpen-source AI-native vector databaseOpen-sourceWeaviate is an open-source AI-native vector databaseTrueWeaviate is an open-source AI-native vector database · True · Open-source
Application types
AI-native vector databaseOpen-source AI-native vector database that stores objects and vectors together
DeploymentServerless, Weaviate Enterprise Cloud, and Bring Your Own Cloud deployment options · Can be self-hosted or run as a managed service in Weaviate Cloud · Self-hosted database, managed service, or Kubernetes package in your VPC · Engram is a managed memory service built on the Weaviate vector database
LicenseWeaviate is an open-source AI-native vector database · True · Open-source
Model supportNot disclosed by source
Data controlData fully encrypted in transit and at rest, with granular access controls and multi-tenan · Weaviate Cloud supports granular role-based access control with new Editor and Viewer role · Operates a responsible disclosure / vulnerability reporting program via HackerOne with a W · Strict resource isolation for multi-tenant deployments · Dedicated security reporting channel at https://weaviate.io/security-report
Learning curveIntermediate
Primary use casesMulti-tenant RAG across thousands of isolated customer indexes, Financial data research and analysis processing millions of vectors, AI-native semantic search applications with hybrid retrieval, Agentic workflows performing sync, async, and multi-step data tasks, Natural-language querying of enterprise databases via the Query Agent, Multi-tenant retrieval-augmented generation (RAG) for AI applications across thousands of, Financial data research and analysis platform processing large-scale documents, Powering AI agents for high-volume insurance claims processing, Memory built for AI agents that remember, learn, and continually improve over time, includ
What to verify before adopting
Evolution and major updates
Weaviate timeline
A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.
1 dated update
Latest first · exact dates
Showing the newest updates and meaningful milestones. Open an entry for its summary and source.
Release note
HFresh: Memory-Efficient Vector Search
Open details
Weaviate introduced HFresh, a disk-based vector index that trades some query latency for lower memory use and larger-scale datasets, using HNSW as the in-memory centroid index and disk-based postings in an LSM store with incremental rebalancing instead of full
Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.
1weaviate.io 8 facts · 1 answer · 1 snapshot source · Official site
These tools share a workflow, capability or audience with Weaviate. They may complement it rather than replace it, and are not presented as integrations or endorsements.
Can be self-hosted or run as a managed service in Weaviate Cloud
Capability
Pre-built agentic workflows that dynamically interact with data to perform sync, async, and multi-step tasks
Capability
Query the database in natural language
Capability
Generate embeddings within Weaviate Cloud without connecting to a third-party provider
Integration
Seamlessly connect data with models from a range of providers
Integration
Built-in MCP server providing hybrid search over code and docs to Claude Code, Cursor, and VS Code
Security
Weaviate Cloud supports granular role-based access control with new Editor and Viewer roles
Capability
Stores objects and vectors together and supports vector search, keyword search, hybrid search, filtering, multi-tenancy, and retrieval-augmented generation
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