Last updated 07-28-2026
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Vespa
Vespa enables developers to build powerful search and recommendation applications by combining vector, text, and structured data search with machine-learned ranking and real-time data serving. It supports complex queries over billions of constantly changing data items with latencies below 100 milliseconds, making it suitable for high-performance, data-driven applications.
What sets Vespa apart is its ability to integrate multiple data types and machine-learned models within a single platform, allowing hybrid search and personalized ranking at scale. It offers infinite automated scalability and continuous deployment, with options for fully managed cloud services or self-hosted deployments, providing flexibility for various enterprise needs.
Vespa supports advanced use cases such as generative AI retrieval-augmented generation (RAG), personalized content delivery, and semi-structured navigation. Its architecture is designed to handle high query volumes and large datasets efficiently, ensuring low latency and strong security for business-critical AI applications.
Developers benefit from Vespa's open-source ecosystem, extensive documentation, and active community, which facilitate building and deploying AI-powered search and recommendation systems with complex ranking and inference capabilities.
Combined vector, text, and structured search supporting billions of data items
Real-time machine-learned ranking with ONNX and XGBoost model integration
Automated scaling to thousands of queries per second with sub-100ms latency
Continuous deployment with automatic platform upgrades multiple times per week
Fully managed cloud service with strong security and compliance
Streaming search mode for personal/private data at 20x lower cost
Integration with multi-vector representations for generative AI (RAG)
Supports combined vector, text, and structured search in one platform
Integrates machine-learned ranking models for personalized results
Scales automatically to very large datasets and high query volumes
Offers fully managed cloud with strong security and compliance
Enables continuous deployment and automatic platform upgrades
Self-hosted deployment requires managing infrastructure and scaling
Pricing details require contacting sales for custom enterprise plans
How does Vespa handle different types of search data?
Vespa supports combined vector, text, and structured data search within the same query, enabling precise and complex retrieval across diverse data types.
Can Vespa run machine-learned models for ranking?
Yes, Vespa integrates machine-learned models such as ONNX and XGBoost for real-time ranking and inference to personalize search results.
What scalability does Vespa offer for large datasets?
Vespa scales automatically to billions of data items and thousands of queries per second, maintaining latencies below 100 milliseconds.
Does Vespa provide a managed cloud service?
Yes, Vespa Cloud offers a fully managed service with automatic scaling, platform upgrades, and strong security features.
Is Vespa suitable for generative AI applications?
Vespa supports retrieval-augmented generation (RAG) by combining hybrid search and multi-vector representations at scale for generative AI use cases.
How does Vespa ensure continuous deployment?
Vespa Cloud performs automatic platform updates multiple times per week, enabling safe and seamless continuous delivery.
Can Vespa be used for personal or private search?
Yes, Vespa offers a streaming search mode optimized for personal data, reducing costs while maintaining full search capabilities.