head to head · open source
Meilisearch vs Qdrant
Meilisearch has 59,758 GitHub stars, 2,786 forks, 321 open issues and last shipped yesterday. Qdrant has 35,390 stars, 2,793 forks, 704 open issues and last shipped yesterday. Meilisearch leads on adoption by 69% (59,758 vs 35,390 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 | ★ 60K | ★ 35K |
| License | Custom / other | Apache-2.0 |
| Written in | Rust | Rust |
| Last push | 2026-11-02 | 2026-11-02 |
| Forks | ⑂ 2.8K | ⑂ 2.8K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Meilisearch if
- You weight community size — 60K 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
Website Roadmap Meilisearch Cloud Blog Documentation FAQ Discord ⚡ A lightning fast search engine that fits effortlessly into your apps, websites, and workflow 🔍 Meilisearch helps you shape a delightful search experience in a snap, offering features that work out of the box to speed up your workflow. 🖥 Examples Movies — An application to help you find streaming platforms to watch movies using hybrid search. Flickr — Search and explore one hundred million Flickr images with semantic search. Ecommerce — Ecommerce website using disjunctive facets, range and rating filtering, and pagination. Home Booking A…
read the full Meilisearch overview →
About Qdrant
Vector Search Engine for the next generation of AI applications Qdrant (read: quadrant ) is a vector similarity search engine and vector database. It provides a production ready service with a convenient API to store, search, and manage points—vectors with an additional payload. Qdrant is tailored for extended filtering support, making it useful for all sorts of neural network or semantic based matching, faceted search, and other applications. Qdrant is written in Rust 🦀, which makes it fast and reliable even under high load. See benchmarks. With Qdrant, embeddings or neural network encoders can be turned into…
read the full Qdrant overview →
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Frequently asked questions
Is Meilisearch or Qdrant more popular?
Meilisearch has 59,758 GitHub stars and Qdrant has 35,390. 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 (59,758 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.