head to head · open source
Qdrant vs Activeloop
Qdrant has 35,414 GitHub stars, 2,796 forks, 704 open issues and last shipped 2 days ago. Activeloop has 9,265 stars, 725 forks, 66 open issues and last shipped 6 months ago. Qdrant leads on adoption by 282% (35,414 vs 9,265 stars). Qdrant is written in Rust under Apache-2.0; Activeloop is written in C++ under Apache-2.0. Qdrant has attracted 8% as many forks as stars, Activeloop 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 (mlops), 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.
← all 20902 open source comparisons
Side by side
| Qdrant | Activeloop | |
|---|---|---|
| GitHub stars | ★ 35K | ★ 9.3K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Rust | C++ |
| Last push | 2026-11-03 | 2026-05-21 |
| Forks | ⑂ 2.8K | ⑂ 725 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Qdrant if
- You weight community size — 35K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches Rust
- You value the larger contributor base for long-term maintenance
pick Activeloop if
- You want the Activeloop feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches C++
- You evaluated both and Activeloop fits your workflow better
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 →
About Activeloop
Deep Lake: Database for AI Docs • Get Started • API Reference • LangChain & VectorDBs Course • Blog • Whitepaper • Slack • Twitter What is Deep Lake? Deep Lake is a Database for AI powered by a storage format optimized for deep learning applications. Deep Lake can be used for: 1. Storing and searching data plus vectors while building LLM applications 2. Managing datasets while training deep learning models Deep Lake simplifies the deployment of enterprise grade LLM based products by offering storage for all data types (embeddings, audio, text, videos, images, dicom, pdfs…
read the full Activeloop overview →
More in Infrastructure & Operations
Related comparisons
More Databases projects
Compare either of these against the rest of the Databases field.
Frequently asked questions
Is Qdrant or Activeloop more popular?
Qdrant has 35,414 GitHub stars and Activeloop has 9,265. Qdrant has the larger community by that measure.
Are Qdrant and Activeloop free?
Both are open source. Qdrant is licensed under Apache-2.0 and Activeloop under Apache-2.0. Neither carries a licence fee.
What is the difference between Qdrant and Activeloop?
Qdrant is written in Rust and Activeloop in C++. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, Qdrant or Activeloop?
Choose Qdrant if you want the larger community (35,414 stars) or its Apache-2.0 licence terms. Choose Activeloop if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.