The best Metaflow alternatives
The rest of Best ML Experiment Tracking Software, in this site's order: each with what it costs, where it runs and its record score.
It suits AI developers working on model training, fine-tuning, AI application development, or production monitoring.
Opik may suit teams building LLM applications or agents that need tracing and evaluation.
DagsHub suits developers and teams managing machine-learning data and experiments across multiple project stages.
Lamin suits scientists and engineers in academia, biotech, and global pharma, from personal projects to enterprise R&D.
It suits teams developing, training, and deploying AI/ML models that need GPU infrastructure management and experiment tracking.
The Open Source plan is described for individuals and small teams; Scale is aimed at teams running ML in production.
LabML suits people tracking deep learning experiments who want to compare runs, monitor hardware, or work across multiple machines.
MLRun provides a shared technology stack for data engineers, data scientists and machine-learning engineers.
TensorBoard suits people experimenting with TensorFlow models who want to inspect metrics, graphs, distributions, and profiling data.
The platform is aimed at developers, data scientists, platform and security engineers, and administrators building AI systems.
Rubicon-ML is aimed at developers and science or research users tracking machine-learning experiments.
DVCLive suits Python-based machine learning practitioners who track experiments with Git and DVC.
It is designed for teams building and operating LLM applications or AI agents that need evaluation, feedback, tracing, and regression comparisons.
