Vespa

Vespa is a unified platform for building large-scale AI applications that combines vector search, text search, and machine-learned ranking with real-time inference. It handles billions of data items while maintaining sub-100ms query latency, eliminating the need to cobble together separate vector databases, text search engines, and ML inference systems.

Vespa offers a free trial to build your first application. The platform is available as both open-source deployment options and managed cloud services through Vespa Cloud and Vespa on AWS, though specific pricing details for the managed services aren't specified.

Unlike traditional vector databases, Vespa combines vector search, text search, and structured data querying in a single platform with integrated ML ranking and real-time model inference. It supports hybrid search techniques, multi-vector representations, and complex ranking algorithms that go beyond simple vector similarity, all while maintaining sub-100ms latency at enterprise scale.

You can build search applications, recommendation systems, and RAG (Retrieval-Augmented Generation) applications that require processing billions of constantly changing data items. Vespa supports conversational AI chat, knowledge base Q&A, natural language data querying, file/document analysis, and data visualization generation.

Vespa provides continuous deployment capabilities, automated scaling, and supports real-time model updates without service interruption. The platform can handle thousands of concurrent queries while maintaining consistent sub-100ms latency, making it suitable for mission-critical enterprise applications.

Vespa's streaming search mode is designed for personal or private search applications and delivers full functionality at 20x lower cost than traditional indexing approaches. This mode allows you to search through personal data without the overhead of maintaining large indexes.

Major technology companies including Spotify, Yahoo, Elicit, and Farfetch rely on Vespa for mission-critical applications. Spotify uses it to power search across their music catalog, while Elicit leverages it for AI research applications requiring both precision and speed.

Yes, Vespa has native tensor support that enables sophisticated decisioning and ranking operations directly within the search pipeline. This allows you to execute complex tensor operations for advanced decisioning and integrate with existing ML frameworks for comprehensive AI applications.