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Rockset Expands AI Capabilities to Power Billion-Scale Vector Search in the Cloud

New release with Approximate Nearest Neighbor (ANN) Similarity Search, LlamaIndex and LangChain integrations enables developers to efficiently scale AI applications in the cloud

 Rockset, the search and analytics company announced it has expanded its vector search capabilities with approximate nearest neighbor (ANN) search, achieving billion-scale similarity search in the cloud. When coupled with the LlamaIndex and LangChain integrations, the new release enables developers to iterate quickly, and create more relevant AI experiences at scale. This news comes on the heels of Rockset raising $44 million in funding and being named a data streaming for AI partner to Confluent.

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In April of this year, Rockset introduced support for vector search, which has gained rapid momentum as more applications employ machine learning and artificial intelligence to power voice assistants, chatbots, anomaly detection, recommendation and personalization engines, and more. However, many large language models (LLMs) generate vector embeddings with thousands of dimensions, making exact nearest neighbor search computationally expensive and complex. With the new support for ANN, Rockset customers can create vector embeddings on any machine learning model and index them for fast similarity search, at massive scale. New capabilities allow developers to:

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  • Create relevant AI experiences at scale by storing and indexing billions of vectors alongside hundreds of terabytes of metadata, including text, JSON, geo and time-series data. Leverage the power of the search index with an integrated SQL engine for metadata filtering as simple as a SQL WHERE clause
  • Build AI applications with real-time updates by inserting, updating, and deleting vectors and metadata with indexes built on RocksDB. New data is reflected in searches in milliseconds with no expensive reindexing.
  • Separate indexing and search with compute-compute separation to scale AI applications in production with confidence.

“Enterprises will only continue to leverage AI if they have the ability to scale AI applications efficiently, which is why Rockset is designed for billion-scale vector search in the cloud,” said Venkat Venkataramani, co-founder and CEO of Rockset. “Efficiently incorporating real-time signals and updates into vector search applications is no easy feat. We’ve spent years designing Rockset for real-time updates and are thrilled that companies can now build AI applications at scale.”

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Customers are already leveraging the power of Rockset as a vector database to deploy AI/ML applications at scale. “Iteration and speed of new ML products were the most important to us,” said Sai Ravuru, senior manager of data science and analytics in a recent case study with JetBlue. “We saw the immense power of real-time analytics and AI to transform JetBlue’s real-time decision augmentation and automation since stitching together 3-4 database solutions would have slowed down application development. With Rockset, we found a database that could keep up with the fast pace of innovation at JetBlue.”

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