Vespa is an AI search platform that enables search, inference, and organization of vectors, tensors, text, and structured data at serving time and any scale.
The platform addresses the challenge of selecting relevant subsets from large data corpora, evaluating machine-learned models over those subsets, organizing results, and returning them with sub-100-millisecond latency while the underlying data continuously changes. Vespa handles the complexity of distributing these operations across multiple nodes and executing them in parallel, providing high availability and performance guarantees. It manages the full pipeline from data ingestion through query evaluation to result aggregation and return.
Vespa suits organizations building search, recommendation, and personalization systems that must operate on large distributed datasets with strict latency requirements. It is appropriate for teams that need production-ready infrastructure rather than building custom solutions, and it scales from single-node deployments to systems serving hundreds of thousands of queries per second. The platform can be deployed either through the managed cloud service or self-hosted on your own infrastructure. Applications can be extended with custom Java components, and the tool provides APIs for integration with external systems.
The project maintains a regular release cadence with new versions built from the master branch multiple times per week. Development activity is visible in the repository as it occurs, with the full codebase available under the Apache 2.0 license. The project welcomes external contributions and maintains active community engagement through documentation repositories, sample applications, a blog covering feature updates and use cases, and a community Slack channel for questions and feedback.
