Announcing vector streaming search: AI assistants at scale without breaking the bank

Vespa's announcement of vector streaming search offers a cost-effective alternative to traditional approximate nearest neighbor (ANN) search methods for handling personal data in AI applications. By localizing user data and using a unique document identification system, Vespa's approach eliminates the need for storing massive amounts of vector data in memory, significantly reducing costs and improving performance. Unlike ANN, which may miss crucial data, vector streaming search guarantees comprehensive coverage of relevant data while maintaining low latency and high throughput, even as it scales to handle billions of documents. Through co-located disk storage and advanced data management techniques, Vespa's solution achieves efficient query and write performance without the high memory costs associated with traditional vector databases. This method also allows seamless integration with metadata and text searches, providing a robust framework for delivering high-quality user experiences in AI-driven applications.