What is Meilisearch used for?

Meilisearch is a fast search engine API written in Rust that adds typo-tolerant, filterable and hybrid semantic search to websites and apps, available self-hosted or as a cloud service. It is a good fit for site search for content and documentation, ecommerce catalog search with facets, and semantic search in AI applications.

Is Meilisearch open source?

Yes. Meilisearch is open source under a custom licence. Its source code is on GitHub at meilisearch/meilisearch and is written mainly in Rust.

Yes. Meilisearch is open source, so the software itself is free to use under the terms of its own licence. A managed cloud version is also available.

Can I self-host Meilisearch?

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

What is Meilisearch an alternative to?

Meilisearch is an open-source alternative to Algolia, AddSearch, Azure AI Search and Searchspring. Other open-source alternatives to Algolia include Typesense, Marqo and Vespa.

Is Meilisearch actively maintained?

Yes. The most recent commit to Meilisearch was on 1 October 2026, and the latest release is v1.54.3, published on 1 October 2026. The project has 59k stars on GitHub.

Meilisearch: open-source alternative to Algolia, AddSearch

About Meilisearch

Meilisearch is a search engine exposed through an API, written in Rust and designed to be dropped into applications and websites. It aims to give developers a good search experience quickly, with features that work out of the box rather than needing extensive tuning.

Its capabilities include hybrid search that combines semantic and full-text results, search-as-you-type with a target response time under 50 milliseconds, typo tolerance, filtering and faceted search, sorting and geosearch. The topics also describe it as a vector database for AI use cases. The project maintains example applications for movies, ecommerce with facets and filters, conversational home booking, personalization and a multi-tenant CRM, which show how the features fit together.

The license is listed as 'Other', so confirm the terms for your use case. Meilisearch can be run on your own infrastructure, and Meilisearch Cloud offers it as a managed service. It is a common pick for site search, product catalogs and in-app search where a lighter alternative to a large cluster is attractive.

Key features

Hybrid semantic and full-text search
Instant search-as-you-type results
Typo tolerance and fuzzy matching
Filtering and faceted search
Sorting and geosearch
Vector storage for AI search
REST API with example demo apps

Good fit for

→Site search for content and documentation
→Ecommerce catalog search with facets
→Semantic search in AI applications

Built with: Rust
Tags: search-engine; full-text-search; typo-tolerance; vector-search; rust; hybrid-search; faceted-search; self-hosted

Meilisearch: questions and answers

What is Meilisearch used for?: Meilisearch is a fast search engine API written in Rust that adds typo-tolerant, filterable and hybrid semantic search to websites and apps, available self-hosted or as a cloud service. It is a good fit for site search for content and documentation, ecommerce catalog search with facets, and semantic search in AI applications.
Is Meilisearch open source?: Yes. Meilisearch is open source under a custom licence. Its source code is on GitHub at meilisearch/meilisearch and is written mainly in Rust.
Is Meilisearch free?: Yes. Meilisearch is open source, so the software itself is free to use under the terms of its own licence. A managed cloud version is also available.
Can I self-host Meilisearch?: Yes. Meilisearch can be self-hosted on your own server or infrastructure.
What is Meilisearch an alternative to?: Meilisearch is an open-source alternative to Algolia, AddSearch, Azure AI Search and Searchspring. Other open-source alternatives to Algolia include Typesense, Marqo and Vespa.
Is Meilisearch actively maintained?: Yes. The most recent commit to Meilisearch was on 1 October 2026, and the latest release is v1.54.3, published on 1 October 2026. The project has 59k stars on GitHub.