AMSTERDAM, July 30, 2024 (GLOBE NEWSWIRE) -- AI-native vector database company Weaviate announced today that it is releasing a developer “workbench” of tools and apps along with flexible tiered storage to meet the needs of organizations putting AI into production.
Inspired by Weaviate’s vibrant open source community, Weaviate’s new developer offerings accelerate AI application development and provide end-to-end solutions for some of the most common AI use cases, helping organizations make the leap from AI prototypes to production.
They include:
- Recommender app: Provides a fully managed, low-code solution for rapid development of scalable, personalized recommendation systems. Recommender offers configurable endpoints for item-to-item, item-to-user, and user-to-user recommendation scenarios and supports images, text, audio, and other forms of multimodal data. Sign up to be part of the private beta.
- Query tool: Enables developers to query data in Weaviate Cloud using a GraphQL interface. Available now through Weaviate Cloud Console.
- Collections tool: Allows users to create and manage collections in Weaviate Cloud without writing any code. Available now through the Weaviate Cloud Console.
- Explorer tool: Lets users search and validate object data through a graphical user interface (GUI). Coming soon to Weaviate Cloud Console.
To fuel development of new apps, Weaviate has debuted a Labs division dedicated to testing daring ideas and turning the best into Weaviate products. Among its first projects, Weaviate Labs is developing an app to help teams quickly deploy production-ready Generative Feedback Loops for AI agents and take the next step beyond RAG.
To meet the needs of diverse AI use cases, Weaviate’s new storage tiers and tenant offloading capabilities allow users to optimize for speed, cost, or performance. Low-latency applications closely tied to revenue, such as e-commerce and recommendation engines, can continue to be optimized for performance, while applications with higher latency tolerances, such as chatbots, can scale cost-efficiently.
“We’ve seen AI applications move into production at scale. Now the AI-native stack needs to evolve so organizations can build AI applications faster and deploy them at lower cost. We’re entering the ‘Production Era,’ where we start to see real impact from AI,” said Bob van Luijt, CEO and co-founder of Weaviate. “Listening to our community, it’s clear that to take the next step, developers need an AI-native framework with flexible storage tiers, modular GUI tools to interact with their data, and a pipeline of new concepts to spark their creativity.”
The storage options are:
- Hot - for the highest performance read/write data access in real-time
- Warm - to balance accessibility and cost of data that is readily available but used less frequently
- Cold - for cost-effective long-term storage of data with infrequent use and slower access.
At present, most vector databases that power AI applications treat all data as ‘Hot,’ offering rapid access but at the highest price. This means AI applications can be costly to scale from prototype to production. Other vector databases only offer Warm and Cold data tiers, which makes them incompatible with real-time use cases like e-commerce. By offering a trifecta of Hot, Warm, and Cold tiers, Weaviate enables AI application developers to balance cost, performance, and speed depending on the workload and use case.
"Weaviate’s scalable multi-tenant architecture has been crucial in maintaining fast and reliable AI-driven customer service and engagement experiences for our thousands of users on Botsonic," said Samanyou Garg, CEO of Writesonic. "Their vector search capabilities enable our users to build highly accurate AI chatbots trained on their own data. We look forward to leveraging their new flexible storage tiers to efficiently allocate resources for each tenant."
Weaviate’s new Query and Collections tools are now available to Weaviate Cloud users through the Weaviate Cloud Console. The new storage tiers are available for all users of Weaviate Enterprise Cloud and Weaviate Database (Open Source).
About Weaviate
Weaviate is an open-source AI-native vector database that makes it easier for developers to build and scale AI applications. With powerful hybrid search out of the box, seamless connection to machine learning models, and a purpose-built architecture that scales to billions of vectors and millions of tenants—Weaviate is a foundation for modern, AI-native software development. Customers and open-source users, including Instabase, NetApp, and Red Hat, power search and generative AI applications with Weaviate while maintaining control over their own data. The company was founded in 2019 and is funded by Battery Ventures, Cortical Ventures, Index Ventures, ING Ventures, NEA, and Zetta Venture Partners. For more information, visit Weaviate.io.
Media Contact
Chris Ulbrich
weaviate@firebrand.marketing
415 848 9175
