head to head · open source
Meilisearch vs Qdrant
Meilisearch has 59,320 GitHub stars, 2,710 forks, 321 open issues and last shipped yesterday. Qdrant has 34,650 stars, 2,682 forks, 704 open issues and last shipped yesterday. Meilisearch leads on adoption by 71% (59,320 vs 34,650 stars). Meilisearch is written in Rust under a custom or non-standard licence; Qdrant is written in Rust under Apache-2.0. Meilisearch has attracted 5% as many forks as stars, Qdrant 8%. Qdrant was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (hybrid-search), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| Meilisearch | Qdrant | |
|---|---|---|
| GitHub stars | ★ 59K | ★ 35K |
| License | Custom / other | Apache-2.0 |
| Written in | Rust | Rust |
| Last push | 2026-09-17 | 2026-09-17 |
| Forks | ⑂ 2.7K | ⑂ 2.7K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Meilisearch if
- You weight community size — 59K stars and counting
- You want the Custom / other license terms
- Your stack matches Rust
- You value the larger contributor base for long-term maintenance
pick Qdrant if
- You want the Qdrant feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Rust
- You evaluated both and Qdrant fits your workflow better
About Meilisearch
Meilisearch is an open source search engine built in Rust that delivers fast, relevant search results for applications and websites. It operates as a standalone service with a RESTful HTTP API, designed to integrate directly into existing data pipelines without requiring major architectural changes.
read the full Meilisearch overview →
About Qdrant
Qdrant is a vector similarity search engine and database built in Rust, designed for production use in AI applications. It stores, manages, and retrieves high dimensional vectors alongside associated metadata (payloads), supporting fast nearest neighbor search with filtering. It solves the problem of efficiently performing semantic or neural based matching at scale—such as finding similar images, recommending content, or powering retrieval augmented generation (RAG)—by replacing slow or inflexible brute force or index based approaches with a purpose built engine optimized for vector operations.…
read the full Qdrant overview →
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Frequently asked questions
Is Meilisearch or Qdrant more popular?
Meilisearch has 59,320 GitHub stars and Qdrant has 34,650. Meilisearch has the larger community by that measure.
Are Meilisearch and Qdrant free?
Both are open source. Meilisearch has no licence declared in this registry, and Qdrant is licensed under Apache-2.0. Both are free to self-host.
What is the difference between Meilisearch and Qdrant?
Meilisearch is written in Rust and Qdrant in Rust. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, Meilisearch or Qdrant?
Choose Meilisearch if you want the larger community (59,320 stars) or its Custom / other licence terms. Choose Qdrant if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.