head to head · open source
serviette vs mlop
serviette has 58,732 GitHub stars, 1,547 forks, 8 open issues and last shipped yesterday. mlop has 394 stars, 12 forks, 3 open issues and last shipped 8 months ago. serviette leads on adoption by 14,807% (58,732 vs 394 stars). serviette is written in Python under MIT; mlop is written in Python under Apache-2.0. serviette has attracted 3% as many forks as stars, mlop 3%. serviette was the more recently maintained of the two, and both are self-hostable with no licence fee.
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
| serviette | mlop | |
|---|---|---|
| GitHub stars | ★ 59K | ★ 394 |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-03-05 |
| Forks | ⑂ 1.5K | ⑂ 12 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick serviette if
- You weight community size — 59K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick mlop if
- You want the mlop feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Python
- You evaluated both and mlop fits your workflow better
About serviette
serviette Looking for llm app templates? All templates, cookbooks and examples are still available in full in the Pathway repository under examples/llm app . A no code, always up to date RAG server for any vector database — built on the Pathway Live Data Framework. Point serviette at a folder of documents, pick a vector database and an embedder in one YAML file, and run two commands: you get a chat and a REST API that answer from those documents and cite their sources. From then on, any change to the documents — an edit, a new file, a deletion — is reflected in the answers within seconds. serviette runs on…
read the full serviette overview →
About mlop
mlop is a Machine Learning Operations (MLOps) framework. It provides self hostable superior experimental tracking capabilities and lifecycle management for training ML models. To get started, try out our introductory notebook or get an account with us today! 🎥 Demo mlop adopts a KISS philosophy that allows it to outperform all other tools in this category. Supporting high and stable data throughput should be THE top priority for efficient MLOps. mlop logger (bottom left) v. a conventional logger (bottom right) 🚀 Getting Started Try mlop on our platform in a notebook & start integrating in just 5 lines of…
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Frequently asked questions
Is serviette or mlop more popular?
serviette has 58,732 GitHub stars and mlop has 394. serviette has the larger community by that measure.
Are serviette and mlop free?
Both are open source. serviette is licensed under MIT and mlop under Apache-2.0. Neither carries a licence fee.
What is the difference between serviette and mlop?
serviette is written in Python and mlop in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, serviette or mlop?
Choose serviette if you want the larger community (58,732 stars) or its MIT licence terms. Choose mlop if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.