Weaviate

Weaviate

The AI-native database for a new generation of software
0 reviews
16 followers

Maker Shoutouts

Testimonials from top launches

Trending
Findr
Karina Choudhary
used this to buildFindr: remember everythingFindr
(463 points)
Lightning fast and personalised support for every query. They've helped us brainstorm some architecture level things.
Zefi.ai
Alexandros Fokianos
used this to buildZefi 1.0Zefi.ai
(839 points)
Weaviate has let us manage our data and has supported us since the very beginning. Best team ever and amazing product
Quantera.ai
Parth Shah
used this to buildQuantera.aiQuantera.ai
(463 points)
To store all our vector embeddings, now a staple for us to build forward. Their automatic 'load balancing' on which vectors are recently used is a game changer for system optimization
Lamatic.ai
Aman Sharma
used this to buildLamatic.aiLamatic.ai
(479 points)
At Lamatic, we’re excited to use Weaviate as our vector database solution! Weaviate’s powerful vector search and flexible deployment options allow us to manage complex data with ease and precision. Its high-performance, AI-first design enables us to deliver faster, more relevant results to our users, helping us take our product to the next level.
Findr
Nishkarsh Srivastava
used this to buildFindr 2.0Findr
(793 points)
We use weaviate as a vector database that lets us create vector stores on the fly and find the best possible information for our users
Depth
Shehbaj Dhillon
used this to buildDepthDepth
(118 points)
We use Weaviate as a our vector database. Our main feed feature is powered by the vector embeddings Weaviate generates.
Unbody
amir
used this to buildUnbody
(73 points)
Unbody is built on top of Weaviate, making Unbody content API run 100% on a vector database. Weaviate modular architecture as well as user-friendly GraphQl API has played a vital role in our product.