What is Vespa used for?

Vespa is an open-source platform for search, recommendation and personalization that serves vectors, tensors, text and structured data with machine-learned ranking at any scale. It is a good fit for large-scale search and RAG applications, recommendation and personalization engines, and vector search with metadata filtering.

Is Vespa open source?

Yes. Vespa is open source under the Apache-2.0 licence. Its source code is on GitHub at vespa-engine/vespa and is written mainly in Java.

Yes. Vespa is open source, so the software itself is free to use. A managed cloud version is also available.

Can I self-host Vespa?

Yes. Vespa can be self-hosted on your own server or infrastructure.

What is Vespa an alternative to?

Vespa is an open-source alternative to Elastic, Algolia, Pinecone and Amazon OpenSearch Service. Other open-source alternatives to Elastic include Typesense, Apache Solr and OpenSearch.

Is Vespa actively maintained?

Yes. The most recent commit to Vespa was on 2 October 2026, and the latest release is v8.753.16, published on 16 September 2026. The project has 7.1k stars on GitHub.

Vespa: open-source alternative to Elastic, Algolia, Pinecone

About Vespa

Vespa is a platform for applications that must pick a subset of data from a large, changing corpus, evaluate machine-learned models over it, organize and aggregate the results and return them quickly. Typical examples are search, recommendation and personalization, and the platform adds vector search and retrieval-augmented generation as well, as shown in its topics.

Doing this over large data sets distributed across many nodes and evaluated in parallel is hard, and Vespa handles it with high availability and performance. It handles searching, running inference over and organizing vectors, tensors, text and structured data while serving. According to the README, Vespa has been developed over many years and runs behind several large internet services.

The repository contains all the code needed to build and run Vespa yourself under the Apache 2.0 licence, and new releases are made from the master branch on weekday mornings. You can deploy applications to the Vespa Cloud service, which offers a free trial, or run your own instance following the getting started guide. It is written in Java and C++ and suits teams building large-scale search and recommendation systems.

Key features

Search over text, vectors and tensors
Machine-learned model inference at serving time
Structured data filtering and aggregation
Distributed, highly available serving
Real-time updates while serving queries
Cloud service with a free trial

Good fit for

→Large-scale search and RAG applications
→Recommendation and personalization engines
→Vector search with metadata filtering

Built with: Java
Tags: search-engine; vector-search; recommendation; rag; machine-learning; java; tensor; big-data; vector-database

Vespa: questions and answers

What is Vespa used for?: Vespa is an open-source platform for search, recommendation and personalization that serves vectors, tensors, text and structured data with machine-learned ranking at any scale. It is a good fit for large-scale search and RAG applications, recommendation and personalization engines, and vector search with metadata filtering.
Is Vespa open source?: Yes. Vespa is open source under the Apache-2.0 licence. Its source code is on GitHub at vespa-engine/vespa and is written mainly in Java.
Is Vespa free?: Yes. Vespa is open source, so the software itself is free to use. A managed cloud version is also available.
Can I self-host Vespa?: Yes. Vespa can be self-hosted on your own server or infrastructure.
What is Vespa an alternative to?: Vespa is an open-source alternative to Elastic, Algolia, Pinecone and Amazon OpenSearch Service. Other open-source alternatives to Elastic include Typesense, Apache Solr and OpenSearch.
Is Vespa actively maintained?: Yes. The most recent commit to Vespa was on 2 October 2026, and the latest release is v8.753.16, published on 16 September 2026. The project has 7.1k stars on GitHub.