DBMS > Weaviate

Weaviate System Properties

Please select another system to compare it with Weaviate.

Our visitors often compare Weaviate with Milvus, Qdrant and Elasticsearch.

Editorial information provided by DB-Engines

Name

Description

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

Vector DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	1.86
Rank	#123	Overall
	#11	Vector DBMS

Website

github.com/weaviate/weaviate
weaviate.io

Technical documentation

weaviate.io/developers/weaviate

Developer

Weaviate B.V.

Initial release

2019

Current release

1.19, May 2023

License

Commercial or Open Source

Open Source

commercial license available with Weaviate Enterprise

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Data scheme

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

string, int, float, geo point, date, cross reference, fuzzy references

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

GraphQL is used as query language

APIs and other access methods

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native, and fast.
Is it an essential AI-native infrastructure component, used to store and searching embedding vectors and their corresponding objects. AI-native vector database capabilities include:

High performance – typically performs a 10-NN neighbor search out of millions of objects in single-digit milliseconds.
Extensible, built-in machine learning (ML) modules – Modules enable use of popular services and model hubs such as OpenAI, Cohere or HuggingFace and much more to vectorize data and perform generative AI operations for any data type or use case.
Rich vector search – Supports a variety of ML searches with the added benefit of being able to search vectors AND the source objects from which the vectors were generated.
Production ready - Built with scaling, replication, multi-tenancy, and security in mind.

Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service
Stores vectors AND source objects together - simplifies development by eliminating the need to host and integrate separate databases for source objects and their vectors.
Extensible modules architecture - intergrates with a variety of machine learning models from OpenAI, Cohere, HuggingFace, Google, and others to provide native vectorization and generative AI (results transformation) capabilities.

Typical application scenarios

As a database supporting the development of generative AI and semantic search applications processing any text, image, video, audio, code or other structured or unstructured information.

It is used for applications that automate or facilitate a wide range of use cases, such as first-line sales and customer service (chatbots), copywriting, language translation, named entity recognition, paraphrasing, text summarization, on-line research, and much more.

Key customers

All companies that have data.

Market metrics

As of mid 2023:

Over 2 million open source downloads
3500+ Weaviate Slack community members - https://weaviate.io/slack
6800 stars on Github - https://github.com/weaviate/weaviate

Licensing and pricing models

Weaviate is open-source, and free to use.
Weaviate is also available as a fully managed cloud vector database service, with prices starting at $25 per month - details are here: https://weaviate.io/pricing

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Weaviate, an ANN Database with CRUD support
2 February 2021, Etienne Dilocker, SeMI Technologies (sponsor)

show all

Recent citations in the news

Weaviate unveils three AI vector database agents
6 March 2025, Blocks and Files

Learn how Oracle tops Weaviate for vector search scalability, availability
18 March 2025, Oracle

Video: How Weaviate Is Revolutionizing Search With AI
5 March 2025, Channel Insider

Weaviate adds agents to its tech stack to ease gen AI app development
11 March 2025, InfoWorld

Weaviate Researchers Introduce Function Calling for LLMs: Eliminating SQL Dependency to Improve Database Querying Accuracy and Efficiency
7 February 2025, MarkTechPost

provided by Google News

Share this page