MLflow
An AI engineering platform for tracing, evaluating and managing LLM apps, agents and classical ML models.
Experiment tracking, a model registry, LLM tracing, prompt management and an AI Gateway in one server.
MLflow is a Python-first platform for the machine learning lifecycle that now also covers LLM and agent observability. Data scientists and ML engineers use it to track experiments, version models and debug production AI apps.
What it is
Start a server with uvx mlflow server, add one line such as mlflow.openai.autolog(), and traces and metrics appear in the MLflow UI. Beyond tracing it offers evaluation, a prompt registry, prompt optimization, an AI Gateway, experiment tracking, a model registry and deployment to Docker, Kubernetes, Azure ML and SageMaker.
Who it's for
It suits data science and ML engineering teams who write Python, TypeScript or Java and want one tracking server. It does not suit non-programmers; there is no no-code interface.
Key features
LLM tracing
One-line automatic tracing for more than 60 frameworks, built on OpenTelemetry.
Experiment tracking
Log models, parameters, metrics and evaluation results across runs.
Model registry
Manage the lifecycle of ML models collaboratively.
Prompt registry
Version and optimize prompts used by LLM applications.
AI Gateway
Govern model access and costs for agents and LLM apps.
Deployment targets
Deploy models to Docker, Kubernetes, Azure ML, AWS SageMaker and more.
Up and running in 3 steps
Our honest verdict
What's great
- One server covers classic model tracking and newer LLM and agent tracing, so you avoid two separate tools.
- Auto-logging needs one line (for example mlflow.openai.autolog()), so adding it to existing code is quick.
- Works with Python, TypeScript/JavaScript, Java and any language through OpenTelemetry and MCP integrations.
- Hosting is flexible: local, on-premises clusters, cloud platforms or managed services.
Where it falls short
- It is a developer platform; you need to write code to log runs, and there is no point-and-click workflow.
- Production use means running and securing a tracking server, a backing database and artifact storage yourself.
- The feature set is broad (tracing, evaluation, gateway, registry), so a small team will use a fraction of it.
- The README shows no managed MLflow price; managed services are a separate decision with third parties.
Pricing & license
MLflow is free under Apache-2.0, and mlflow.org states it is forever free. The only cost is running the server, database and storage yourself; no managed plan or price is shown on the pages I read.
Open remote AI-training roles
870 open remote AI-training roles, updated November 14, 2026; most pay $20–$170 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
These reviewed AI tools do the same job instantly — from our scored directory.
Use it, or have us set it up
FAQ
Is MLflow only for classic ML models?
No. The README presents it as a platform for agents, LLMs and ML models, with tracing, evaluation, prompt management and an AI Gateway as well as experiment tracking and a model registry.
Which frameworks does it trace?
The README says one-line automatic tracing is available for more than 60 frameworks, plus OpenTelemetry integration.
Does it cost anything?
MLflow's site says it is 100% open source under Apache 2.0 and forever free; you pay for the servers and storage you run it on.
