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DBMS > Weaviate

Weaviate System Properties

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Editorial information provided by DB-Engines
DescriptionAn AI-native realtime vector database engine that integrates scalable machine learning models.
Primary database modelVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Rank#143  Overall
#5  Vector DBMS
Technical documentationweaviate.io/­developers/­weaviate
DeveloperWeaviate B.V.
Initial release2019
Current release1.19, May 2023
License infoCommercial or Open SourceOpen Source infocommercial license available with Weaviate Enterprise
Cloud-based only infoOnly available as a cloud serviceno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGo
Data schemeyes, maps to GraphQL interface
Typing infopredefined data types such as float or dateyes infostring, int, float, geo point, date, cross reference, fuzzy references
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.no
Secondary indexesyes infoall data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed
SQL infoSupport of SQLGraphQL is used as query language
APIs and other access methodsGraphQL query language
Supported programming languagesJavaScript / TypeScript
Server-side scripts infoStored proceduresno
Partitioning methods infoMethods for storing different data on different nodesSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Foreign keys infoReferential integrityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano
Concurrency infoSupport for concurrent manipulation of datayes
Durability infoSupport for making data persistentyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes
User concepts infoAccess controlAPI 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 togethersimplifies 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

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More resources
DB-Engines blog posts

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

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Recent citations in the news

Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector ...
24 January 2024, AWS Blog

Weaviate Partners with Snowflake to Bring Secure GenAI to Snowpark Container Services
8 February 2024, Datanami

Foley Represents Cortical Ventures in $50M Series B Round for Weaviate
17 December 2023, Foley & Lardner LLP

Getting Started with Weaviate: A Beginner's Guide to Search with Vector Databases
18 July 2023, Towards Data Science

Meet Verba 1.0: Run State-of-the-Art RAG Locally with Ollama Integration and Open Source Models
20 May 2024, MarkTechPost

provided by Google News

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