
MLflow
Unified platform for tracking, evaluating, and deploying AI models
- Stars
- 23,139
- License
- Apache-2.0
- Last commit
- 3 days ago
Explore leading tools in the MLOps: Experiment Tracking & Model Registry category, including open-source options and SaaS products. Compare features, use cases, and find the best fit for your workflow.
7 open-source projects · 4 SaaS products
These projects are active, self-hostable choices for knowledge management teams evaluating alternatives to SaaS tools.

Unified platform for tracking, evaluating, and deploying AI models

Unified, high-performance gateway for industrial-grade LLM applications

Human‑centric framework for building, scaling, and deploying AI systems

Automagical suite to streamline AI experiment, orchestration, and serving

Track, visualize, and compare AI experiments effortlessly
MLflow provides end‑to‑end experiment tracking, observability, prompt management, evaluation, and model registry, enabling data scientists and GenAI developers to build, compare, and deploy AI applications confidently.
Understand the commercial incumbents teams migrate from and how many open-source alternatives exist for each product.
Experiment tracking, model registry & production monitoring for ML teams
Git/DVC-based platform with MLflow experiment tracking and model registry.
Experiment tracking and model registry to log, compare, and manage ML runs.
Experiment tracking, model registry & production monitoring for ML/LLM teams
Comet lets ML teams log and compare experiments, version datasets and artifacts, register and approve models with governance, and monitor production performance and data drift—all in one platform.
Browse neighbouring categories in ML & AI to widen your evaluation.