--- title: "fastembed-rs vs meilisearch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/anush008-fastembed-rs-vs-meilisearch-meilisearch" tools: ["anush008-fastembed-rs", "meilisearch-meilisearch"] --- # fastembed-rs vs meilisearch *GraphCanon updated Jul 12, 2026* ## Verdict Pick fastembed-rs when license: fastembed-rs is Apache-2.0, meilisearch is Other; pick meilisearch when license: meilisearch is Other, fastembed-rs is Apache-2.0. [fastembed-rs](https://docs.rs/fastembed) reports 958 GitHub stars, 133 forks, and 2 open issues, last pushed Jun 30, 2026. [meilisearch](https://www.meilisearch.com) has 58k stars, 2.6k forks, and 310 open issues, last pushed Jul 9, 2026. Figures are from public GitHub metadata via [fastembed-rs's repository](https://github.com/Anush008/fastembed-rs) and [meilisearch's repository](https://github.com/meilisearch/meilisearch). | | [fastembed-rs](/tools/anush008-fastembed-rs.md) | [meilisearch](/tools/meilisearch-meilisearch.md) | | --- | --- | --- | | Tagline | Rust library for generating vector embeddings, reranking locally! | A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications. | | Stars | 958 | 58,493 | | Forks | 133 | 2,607 | | Open issues | 2 | 310 | | Language | Rust | Rust | | Adopt for | - | Meilisearch is a Rust-based, lightning-fast hybrid search engine that integrates easily into web and mobile applications. It supports both full-text and vector searches. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Data & Retrieval, LLM Frameworks, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [fastembed-rs](/tools/anush008-fastembed-rs.md) | [meilisearch](/tools/meilisearch-meilisearch.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 10d | 1d | | Open issues (now) | 2 | 310 | | Owner type | User | Organization | | Full report | [trust report](/tools/anush008-fastembed-rs/trust.md) | [trust report](/tools/meilisearch-meilisearch/trust.md) | ## Decision facts: meilisearch - **Adopt for:** Meilisearch is a Rust-based, lightning-fast hybrid search engine that integrates easily into web and mobile applications. It supports both full-text and vector searches. ## Choose when ### Choose fastembed-rs if… - License: fastembed-rs is Apache-2.0, meilisearch is Other. - Tags unique to fastembed-rs: embeddings, fastembed, rag, reranker. - Also covers LLM Frameworks. ### Choose meilisearch if… - License: meilisearch is Other, fastembed-rs is Apache-2.0. - Tags unique to meilisearch: ai, api, app-search, database. - meilisearch ships Docker support for self-hosted deployment. - - You require fast integration capabilities for your web or mobile application, as Meilisearch offers flexible deployment options. ## When NOT to use fastembed-rs - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use meilisearch - - When you specifically need language support for a large number of languages beyond what Meilisearch currently offers, as some specialized multilingual search engines might handle more languages nimb - - If your application does not require real-time search-as-you-type or typo tolerance features which can add overhead and may slow down performance in less demanding scenarios. ## Common questions ### What is the difference between fastembed-rs and meilisearch? fastembed-rs: Rust library for generating vector embeddings, reranking locally!. meilisearch: A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications.. See the comparison table for live GitHub stats and shared categories. ### When should I choose fastembed-rs over meilisearch? Choose fastembed-rs over meilisearch when License: fastembed-rs is Apache-2.0, meilisearch is Other; Tags unique to fastembed-rs: embeddings, fastembed, rag, reranker; Also covers LLM Frameworks. ### When should I choose meilisearch over fastembed-rs? Choose meilisearch over fastembed-rs when License: meilisearch is Other, fastembed-rs is Apache-2.0; Tags unique to meilisearch: ai, api, app-search, database; meilisearch ships Docker support for self-hosted deployment; - You require fast integration capabilities for your web or mobile application, as Meilisearch offers flexible deployment options. ### When should I avoid fastembed-rs? Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid meilisearch? - When you specifically need language support for a large number of languages beyond what Meilisearch currently offers, as some specialized multilingual search engines might handle more languages nimb - If your application does not require real-time search-as-you-type or typo tolerance features which can add overhead and may slow down performance in less demanding scenarios. ### Is fastembed-rs or meilisearch more popular on GitHub? meilisearch has more GitHub stars (58,493 vs 958). Stars measure visibility, not whether either tool fits your constraints. ### Are fastembed-rs and meilisearch open source? Yes - both are open-source projects on GitHub (fastembed-rs: Apache-2.0, meilisearch: Other). ### Where can I find alternatives to fastembed-rs or meilisearch? GraphCanon lists graph-backed alternatives at [fastembed-rs alternatives](/tools/anush008-fastembed-rs/alternatives) and [meilisearch alternatives](/tools/meilisearch-meilisearch/alternatives) ([fastembed-rs markdown twin](/tools/anush008-fastembed-rs/alternatives.md), [meilisearch markdown twin](/tools/meilisearch-meilisearch/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/anush008-fastembed-rs-vs-meilisearch-meilisearch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, fastembed-rs or meilisearch? fastembed-rs: Active. meilisearch: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for fastembed-rs and meilisearch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [fastembed-rs trust report](/tools/anush008-fastembed-rs/trust); [meilisearch trust report](/tools/meilisearch-meilisearch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=anush008-fastembed-rs`](/api/graphcanon/graph?tool=anush008-fastembed-rs) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_