--- title: "meilisearch vs fastembed" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/meilisearch-meilisearch-vs-qdrant-fastembed" tools: ["meilisearch-meilisearch", "qdrant-fastembed"] --- # meilisearch vs fastembed *GraphCanon updated Jul 12, 2026* ## Verdict Pick meilisearch if 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; pick fastembed if fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. [meilisearch](https://www.meilisearch.com) reports 58k GitHub stars, 2.6k forks, and 310 open issues, last pushed Jul 9, 2026. [fastembed](https://qdrant.github.io/fastembed/) has 3.1k stars, 213 forks, and 137 open issues, last pushed Jun 23, 2026. Figures are from public GitHub metadata via [meilisearch's repository](https://github.com/meilisearch/meilisearch) and [fastembed's repository](https://github.com/qdrant/fastembed). | | [meilisearch](/tools/meilisearch-meilisearch.md) | [fastembed](/tools/qdrant-fastembed.md) | | --- | --- | --- | | Tagline | A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications. | Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings | | Stars | 58,493 | 3,085 | | Forks | 2,607 | 213 | | Open issues | 310 | 137 | | Language | Rust | Python | | 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. | Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 License | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [meilisearch](/tools/meilisearch-meilisearch.md) | [fastembed](/tools/qdrant-fastembed.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 18d | | Open issues (now) | 310 | 137 | | Full report | [trust report](/tools/meilisearch-meilisearch/trust.md) | [trust report](/tools/qdrant-fastembed/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. ## Decision facts: fastembed - **Requirements:** Does not require Docker, making the setup straightforward for Python environments. - **Adopt for:** Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. - **License detail:** Apache-2.0 License ## Choose when ### Choose meilisearch if… - meilisearch is primarily Rust; fastembed is Python. - License: meilisearch is Other, fastembed 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. ### Choose fastembed if… - fastembed is primarily Python; meilisearch is Rust. - License: fastembed is Apache-2.0, meilisearch is Other. - Requirements: Does not require Docker, making the setup straightforward for Python environments.. - Tags unique to fastembed: embeddings, openai, rag, retrieval-augmented-generation. - When you need to generate high-quality embeddings quickly in Python. ## 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. ## When NOT to use fastembed - If your project is not using Python, as Fastembed does not offer support for other programming languages directly. - In scenarios demanding heavy customization or fine-tuning at a lower level than what Fastembed provides out-of-the-box. Consider alternatives that may offer more flexibility. ## Common questions ### What is the difference between meilisearch and fastembed? meilisearch: A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications.. fastembed: Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings. See the comparison table for live GitHub stats and shared categories. ### When should I choose meilisearch over fastembed? Choose meilisearch over fastembed when meilisearch is primarily Rust; fastembed is Python; License: meilisearch is Other, fastembed 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 choose fastembed over meilisearch? Choose fastembed over meilisearch when fastembed is primarily Python; meilisearch is Rust; License: fastembed is Apache-2.0, meilisearch is Other; Requirements: Does not require Docker, making the setup straightforward for Python environments.; Tags unique to fastembed: embeddings, openai, rag, retrieval-augmented-generation; When you need to generate high-quality embeddings quickly in Python. ### 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. ### When should I avoid fastembed? If your project is not using Python, as Fastembed does not offer support for other programming languages directly. In scenarios demanding heavy customization or fine-tuning at a lower level than what Fastembed provides out-of-the-box. Consider alternatives that may offer more flexibility. ### Is meilisearch or fastembed more popular on GitHub? meilisearch has more GitHub stars (58,493 vs 3,085). Stars measure visibility, not whether either tool fits your constraints. ### Are meilisearch and fastembed open source? Yes - both are open-source projects on GitHub (meilisearch: Other, fastembed: Apache-2.0). ### Where can I find alternatives to meilisearch or fastembed? GraphCanon lists graph-backed alternatives at [meilisearch alternatives](/tools/meilisearch-meilisearch/alternatives) and [fastembed alternatives](/tools/qdrant-fastembed/alternatives) ([meilisearch markdown twin](/tools/meilisearch-meilisearch/alternatives.md), [fastembed markdown twin](/tools/qdrant-fastembed/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/meilisearch-meilisearch-vs-qdrant-fastembed.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, meilisearch or fastembed? meilisearch: Very active. fastembed: 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 meilisearch and fastembed? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [meilisearch trust report](/tools/meilisearch-meilisearch/trust); [fastembed trust report](/tools/qdrant-fastembed/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=meilisearch-meilisearch`](/api/graphcanon/graph?tool=meilisearch-meilisearch) - 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/_