Weaviate • Vector Search Engine

Weaviate is an open source vector search engine that stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance and scalability of a cloud-native database, all accessible through GraphQL, REST, and various language clients.



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Low Latency & Large-Scale

Achieve low latencies at any scale in production. Weaviate is a fault-tolerant, highly-available database that you can tune for the best possible QPS/Accuracy trade-off.


Use Weaviate as a stand-alone vector search engine (bring your own vectors) or use one of the many modules (transformers, GPT-3, and more) to vectorize or extend your Weaviate setup.


Cloud-native (Kubernetes and Docker), a wide variety of client libraries, and available through RESTful and GraphQL APIs.

Weaviate in the news

Weaviate in Techcrunch
Weaviate in ZDNet
Weaviate in VentureBeat

A wide variety of out-of-the-box integrations

Besides Weaviate's capabilities to bring your own vectors, you can also choose one of Weaviate's modules with out-of-the-box support for vectorization. You can also choose one of the integrations with one of the well-known neural search frameworks.

OpenAI Embeddings integration with Weaviate logo
Huggingface Transformers integration with Weaviate logo
deepset's Haystack integration with Weaviate logo
DocArray from Jina AI integration with Weaviate logo
WordPress & WooCommerce search plugin from WPSOLR integration with Weaviate logo

Combining developer UX and scalability

GraphQL logo for Weaviate

Query your data using GraphQL

Weaviate has a graph-like data model to easily search through your data using the GraphQL-API. Making Weaviate ideal to store any vectorized data format or graph-embeddings.

Docker logo for Weaviate

Containerized development

Starting your development with Weaviate is one docker-compose up away. Get started in the docs here.

Kubernetes logo in Weaviate

Kubernetes for scale

Need to scale for production? Check out the Weaviate K8s docs here.


Need inspiration?

On the Weaviate Podcast users share how they are using Weaviate in their use cases.

Weaviate Podcast use case logo

Search for comments using Weaviate's semantic search features with Orchest, Weaviate, and Streamlit.

Weaviate Podcast use case logo

Jina AI is a cloud-native neural search framework and in this podcast, Han Xiao the CEO of Jina AI is telling about all the interesting elements at stake here.

Weaviate Podcast use case logo

Karen Beckers, Data Scientist from Squadra Machine Learning Company, gives insightful information about how to use vector search in eCommerce.

Weaviate Podcast use case logo

Alex Cannan, a Machine Learning engineer at Zencastr, talks with Connor Shorten about a really exciting use case of applying search to look through podcast transcription. Topics discussed are the need for fine-tuning, building your own vector database versus Weaviate, data privacy for Deep Learning applications, and many more!

Weaviate Podcast use case logo

Katie is a knowledge management bot, continuously improving, self-learning, and trained by humans. Under the hood, Katie is powered by the Weaviate vector search engine, during this podcast, Katie's Michael Wechner will talk about all things vector search and more!

Weaviate Podcast use case logo

NLP frameworks like Deepset's Haystack are powerful tools to help data scientists and software engineers work with the latest and greatest in natural language processing. In this interview, Malte Pietsch will be talking about Haystack and how they leverage the Weaviate vector search engine as a persistent storage engine for their data and vector representations.

Weaviate Podcast use case logo

Join Connor Shorten and Charles Pierse (Keenious) for the second Weaviate vector search engine Podcast. During the show, they will be discussing how Keenious uses Weaviate and broader, all things NLP!


Who is the company behind Weaviate?

SeMI Technologies is the company behind Weaviate and responsible for the managed services.


Should I use the managed service or open-source?

If you decide to use Weaviate open-source, you have 100% control and rely on community support. The paid services provide direct support, take all management away, and -often crucial if you are an enterprise- the right SLAs to run a database like Weaviate in production.


Is there a startup / foundation / education discount?

Yup! Ask us about it.


Is there a difference between Weaviate open-source and the paid version of Weaviate?


Get started

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