Curated by THEOUTPOST
On Wed, 4 Dec, 12:08 AM UTC
2 Sources
[1]
Database company Weaviate speeds up AI development with flexible vector embeddings service - SiliconANGLE
Database company Weaviate speeds up AI development with flexible vector embeddings service Dutch artificial intelligence database startup Weaviate B.V. is looking to streamline the data vectorization process with a new feature that automatically transforms unstructured information into vector embeddings. Announced today and available now, Weaviate Embeddings is an open-source tool with pay-as-you-go pricing that promises to accelerate the process of preparing unstructured data for AI applications. The Dutch startup is best known for its open-source vector database, which is geared to AI development. It's designed to cater to AI's enormous appetite for unstructured data, which is the essential oil that powers generative AI chatbots such as ChatGPT. Weaviate stores unstructured information as vector embeddings, which are mathematical structures that represent everything from documents to purchase logs, to images and audio files. By storing the data as vectors, it's much easier for AI models to understand and process it. That's all very well, but users face a mountain to climb when it comes to preparing their datasets to be transformed into vector embeddings. In addition, there's a need to transform the prompts users enter to query that data into embeddings as well. Traditionally, developers use embedding services to perform this essential task of data vectorization, but these often become a bottleneck. The problem is that they impose restrictive rate limits on users, slowing down their applications. They also rely on remote application programming interface calls, further hurting performance, and they use proprietary models to lock developers into their ecosystems. Weaviate Embeddings provides developers with an alternative that's based on open-source models hosted in the Weaviate Cloud. It eliminates the need to connect to a third-party embedding provider, while ensuring developers maintain full control of all of their embeddings. Moreover, they will be able to switch between different embedding models without having to manually reindex their data, the startup said. The new service runs on graphics processing units and brings AI models closer to where the vector data is stored, ensuring low latency. And unlike others, Weaviate says it doesn't impose rate limits or caps on users. Moreover, the pay-as-you-go pricing keeps things simple. Weaviate Embeddings is available now in preview on Weaviate Cloud, but for now users are limited to just one embeddings model, Snowflake Inc.'s Arctic-Embed. However, the company assured users it will add support for many more models in the future, starting early next year. Chief Executive Bob van Luijt said the goal is to help developers bring their AI models closer to the data they rely on. "Weaviate Embeddings makes it simple to build and manage AI-native applications," he said. "For those who prefer a custom approach, our open-source database supports any way they want to work. It's all about giving developers the freedom to choose what's best for them." The launch of Weaviate Embeddings is the latest in a string of innovations by the Dutch company. Earlier this year, it debuted an AI Workbench for developers consisting of a prebuilt recommender agent and various tools for queries, collections and data exploration. It also provides a selection of hot, warm and cold data storage tiers, so developers can better balance the costs of their AI applications with performance.
[2]
Database company Weviate speeds up AI development with flexible vector embeddings service - SiliconANGLE
Database company Weviate speeds up AI development with flexible vector embeddings service Dutch artificial intelligence database startup Weviate B.V. is looking to streamline the data vectorization process with a new feature that automatically transforms unstructured information into vector embeddings. Announced today and available now, Weviate Embeddings is an open-source tool with pay-as-you-go pricing that promises to accelerate the process of preparing unstructured data for AI applications. The Dutch startup is best known for its open-source vector database, which is geared to AI development. It's designed to cater to AI's enormous appetite for unstructured data, which is the essential oil that powers generative AI chatbots such as ChatGPT. Weviate stores unstructured information as vector embeddings, which are mathematical structures that represent everything from documents to purchase logs, to images and audio files. By storing the data as vectors, it's much easier for AI models to understand and process it. That's all very well, but users face a mountain to climb when it comes to preparing their datasets to be transformed into vector embeddings. In addition, there's a need to transform the prompts users enter to query that data into embeddings as well. Traditionally, developers use embedding services to perform this essential task of data vectorization, but these often become a bottleneck. The problem is that they impose restrictive rate limits on users, slowing down their applications. They also rely on remote application programming interface calls, further hurting performance, and they use proprietary models to lock developers into their ecosystems. Weaviate Embeddings provides developers with an alternative that's based on open-source models hosted in the Weviate Cloud. It eliminates the need to connect to a third-party embedding provider, while ensuring developers maintain full control of all of their embeddings. Moreover, they will be able to switch between different embedding models without having to manually reindex their data, the startup said. The new service runs on graphics processing units and brings AI models closer to where the vector data is stored, ensuring low latency. And unlike others, Weviate says it doesn't impose rate limits or caps on users. Moreover, the pay-as-you-go pricing keeps things simple. Weaviate Embeddings is available now in preview on Weviate Cloud, but for now users are limited to just one embeddings model, Snowflake Inc.'s Arctic-Embed. However, the company assured users it will add support for many more models in the future, starting early next year. Weviate Chief Executive Bob van Luijt said the goal is to help developers bring their AI models closer to the data they rely on. "Weviate Embeddings makes it simple to build and manage AI-native applications," he insisted. "For those who prefer a custom approach, our open-source database supports any way they want to work. It's all about giving developers the freedom to choose what's best for them." The launch of Weviate Embeddings is the latest in a string of innovations by the Dutch company. Earlier this year, it debuted an AI Workbench for developers consisting of a prebuilt recommender agent and various tools for queries, collections and data exploration. It also provides a selection of hot, warm and cold data storage tiers, so developers can better balance the costs of their AI applications with performance.
Share
Share
Copy Link
Dutch AI database startup Weaviate introduces Weaviate Embeddings, an open-source tool designed to streamline data vectorization for AI applications, offering developers more flexibility and control over their AI development process.
Dutch artificial intelligence database startup Weaviate B.V. has unveiled a new service aimed at revolutionizing the data preparation process for AI applications. The company's latest offering, Weaviate Embeddings, is an open-source tool designed to automatically transform unstructured information into vector embeddings, a crucial step in AI development 12.
Vector embeddings play a vital role in AI, representing various forms of data such as documents, purchase logs, images, and audio files in a format that AI models can easily process. However, developers often face significant hurdles when preparing datasets for vectorization and transforming user prompts into embeddings 12.
Traditionally, embedding services used for data vectorization have posed several challenges:
Weaviate Embeddings addresses these issues by offering:
The service operates on GPUs, bringing AI models closer to vector data storage for reduced latency. Unlike competitors, Weaviate Embeddings doesn't impose rate limits or caps on users 12.
Currently available in preview on Weaviate Cloud, the service supports Snowflake Inc.'s Arctic-Embed model, with plans to expand to additional models in early 2025. Weaviate Embeddings operates on a pay-as-you-go pricing model, simplifying cost management for users 12.
Bob van Luijt, Weaviate's CEO, emphasized the company's goal of simplifying AI-native application development while providing developers with freedom of choice. "Weaviate Embeddings makes it simple to build and manage AI-native applications," he stated, highlighting the flexibility offered by their open-source database 12.
The launch of Weaviate Embeddings is part of a series of innovations from the Dutch company. Earlier developments include:
As AI continues to evolve, Weaviate's latest offering represents a significant step towards more efficient and flexible AI development processes, potentially accelerating innovation in the field.
Vector databases are emerging as crucial tools in AI development, offering efficient storage and retrieval of high-dimensional data. Their impact spans various industries, from e-commerce to healthcare, revolutionizing how we handle complex information.
3 Sources
3 Sources
OneHouse, a data lakehouse company, has launched vector embeddings support to help organizations manage and reduce costs associated with AI model training. This new feature aims to streamline the process of creating and storing vector embeddings at scale.
2 Sources
2 Sources
Zilliz, the company behind the open-source Milvus vector database, has announced significant updates to its Zilliz Cloud offering, aiming to reduce costs and complexity for enterprise AI deployments while improving performance.
2 Sources
2 Sources
Pinecone introduces innovative features to its vector database, including inference capabilities and cascading retrieval, aiming to improve AI application development and accuracy. The update combines dense and sparse vector retrieval with reranking technologies.
2 Sources
2 Sources
Vectorize AI Inc. debuts its platform for optimizing retrieval-augmented generation (RAG) data preparation, backed by $3.6 million in seed funding led by True Ventures. The startup aims to streamline the process of transforming unstructured data for AI applications.
2 Sources
2 Sources
The Outpost is a comprehensive collection of curated artificial intelligence software tools that cater to the needs of small business owners, bloggers, artists, musicians, entrepreneurs, marketers, writers, and researchers.
© 2025 TheOutpost.AI All rights reserved