mlop – open-source ML Experiment Tracking
Hey HN! We built mlop (https://mlop.ai), a fully open source (https://github.com/mlop-ai) ML experiment tracking platform, much like Weights & Biases. Unlike the existing competitors we focus heavily on performance (yes our ingestion backend is in Rust), and top tier user experience, and we are fully open sourced with easy self hosting using docker. Why did we build this? WandB was misleading about their performance, they say they are non blocking, but in fact, they block user code (see video https://docs.mlop.ai/docs/demo), our logger tries to be…
mlop – open-source ML Experiment Tracking launched on April 30, 2025 with 10 votes, #3,208 of 7,818 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
mlop is an open-source ML experiment tracking platform designed for machine learning engineers and researchers who need to log and monitor training experiments. Built with a Rust-based ingestion backend, it prioritizes performance and non-blocking logging that doesn't slow down user code. The platform offers self-hosting via Docker and maintains API compatibility with similar tools, making migration straightforward for existing users.
written from the facts on this page · September 2026
From the sources
mlop has 8 repositories available. Follow their code on GitHub.
mlop is a fully open source dev tool for machine learning engineers that traces key metrics of model training, all the way down to the parameters and gradients level. Unlike other tools, we are laser focused on efficiency and stability. Our platform is designed from the ground up to be fast and scalable - we help you catch issues early and prevent wasted compute. mlop is a platform built for and by ML engineers, supported by our community ! We were tired of the current state of the art in ML observability tools, and this tool was born to help mitigate the inefficiencies - specifically, we hope to better inform you about your model performance and training runs; and actually save you ,…from github.com
In the maker’s words, at launch
Hey HN! We built mlop (https://mlop.ai), a fully open source (https://github.com/mlop-ai) ML experiment tracking platform, much like Weights & Biases. Unlike the existing competitors we focus heavily on performance (yes our ingestion backend is in Rust), and top tier user experience, and we…
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