MLflow

What is MLflow?

MLflow is an open-source AI engineering platform that teams use to debug, evaluate, monitor and ship agents, LLM applications and traditional machine learning models. For generative AI work it captures traces of every step, scores quality with built-in metrics and LLM judges, versions prompts, and offers an AI Gateway that puts one OpenAI-compatible interface in front of many model providers. For classic ML it covers experiment tracking, a model registry and deployment tools, including serving as a REST API or running batch inference on Apache Spark. The core project is Apache-2.0 licensed and backed by the Linux Foundation, and tracing is built on OpenTelemetry. You can start a local server with one command, or use the free managed MLflow Cloud from the project's creators. The MLflow software itself has no price; you pay only for infrastructure you run it on.

Use cases

• Trace LLM and agent runs
• Evaluate LLM output quality
• Version and optimize prompts
• Route requests across LLM providers
• Track ML experiments
• Deploy models as REST APIs
• Run batch inference on Spark

Who it's for

• ML engineers
• AI application developers
• Data scientists
• Platform teams
• Research teams

Why use MLflow

• One open-source platform covers tracing, evaluation, prompts and a gateway, so you avoid stitching separate tools together.
• Tracing follows OpenTelemetry and works with any LLM provider or agent framework rather than a single ecosystem.
• The same tool handles older ML workflows such as experiment tracking and model registry, which helps teams that run both LLM and classic models.
• Starting is quick: run one command to launch a local server, then add an autolog call to your code.
• A free managed option removes server setup for people who only want to try it.

MLflow features

• Traces of LLM and agent steps built on OpenTelemetry
• 50+ built-in evaluation metrics and LLM judges, plus custom ones
• Prompt versioning with lineage tracking and automatic prompt optimization
• AI Gateway with an OpenAI-compatible interface, rate limits and fallbacks
• Agent Server for hosting agents on FastAPI with streaming and tracing
• Experiment tracking and a production model registry
• Model deployment to Docker, Kubernetes, Databricks, Azure ML or AWS SageMaker
• Batch inference on Apache Spark
• SDKs for Python, TypeScript/JavaScript, Java and R

MLflow integrations

• OpenAI
• OpenTelemetry
• Databricks
• Azure ML
• AWS SageMaker
• Kubernetes
• Docker
• Apache Spark
• FastAPI

How to get started with MLflow

1. Start a server with the command uvx mlflow server, or use Docker or the managed MLflow Cloud.
2. Point your code at the tracking URI and enable autologging for your LLM library, for example the OpenAI one.
3. Run your application as usual and open the MLflow UI to see traces, metrics and parameters.
4. Add evaluations with built-in metrics or LLM judges to track quality over time.
5. Version prompts, route model calls through the AI Gateway and deploy agents or models when ready.

MLflow limitations

• Self-hosting means you run and maintain the tracking server
• Applications need a small amount of instrumentation code to produce traces
• Managed hosting is offered through MLflow Cloud or Databricks, with its own sign-up

MLflow pricing

MLflow is open source, so you can use and self-host it for free.

MLflow plans

• Self-hosted Open Source

  Free

  Apache-2.0 license

  • Full control over your own infrastructure
  • Community support

• MLflow Cloud

  Free

  fully managed

  • Built and maintained by the original creators of MLflow
  • Full OSS compatibility

Plans as listed on the official pricing page on 24 Oct 2026. Prices can change and may vary by country or exclude tax. Check the official pricing page.

MLflow alternatives

The main MLflow alternatives listed on Debuly are Langfuse, Weights & Biases, Kubeflow, ZenML, Arize Phoenix and Respan. The table compares pricing and platforms; order is A–Z.

1. 

   Arize Phoenix

   Open-source tool for tracing, evaluating and experimenting on LLM apps and agents, free to self-host or run locally.

   Developer platformsAutomation and agents
   Open sourceVisit Arize Phoenix (official site)
2. 

   Kubeflow

   Open source, Kubernetes-native AI platform made of modular projects for training, tuning, pipelines, notebooks and model management.

   Developer platformsAutomation and agents
   Open sourceVisit Kubeflow (official site)
3. 

   Langfuse

   Open-source LLM observability and evaluation platform for tracing agents, managing prompts and scoring outputs.

   Developer platformsAutomation and agentsCoding
   FreemiumVisit Langfuse (official site)
4. 

   Respan

   LLM gateway with built-in tracing, monitors and automated evals, made for AI agents and LLM apps that are live in production.

   Developer platformsAutomation and agents
   FreemiumVisit Respan (official site)
5. 

   Weights & Biases

   Platform for tracking ML experiments and tracing, evaluating and monitoring LLM apps and agents with W&B Weave.

   Developer platformsCodingAutomation and agents
   FreemiumVisit Weights & Biases (official site)
6. 

   ZenML

   Open-source orchestration for Python ML pipelines and AI agents, with Kitaru for recording and replaying agent runs against production data.

   Developer platformsAutomation and agents
   Open sourceVisit ZenML (official site)

MLflow FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from mlflow.org · fetched 24 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

Spot an error? Report an issue with this listing. We also remove a logo or image on the owner's request.