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MLflow

LLMOps & AI Dev Platforms

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★★★★☆ 4.4 / 5 on G2 · 4.4 on Capterra (50+ G2 reviews)
4.4 / 5
G2 rating, 50+ reviews [source]
Open source
ML lifecycle [source]
Free
Open source [source]
Tracking
+ registry [source]

Quick facts

CategoryOpen source ML lifecycle
VendorMLflow (LF, Databricks)
Starts atOpen source; free
Best forOpen source ML lifecycle management
DeploymentSelf host; managed (Databricks)
Free planYes (open source)
IntegrationsML frameworks, platforms
G2 rating4.4 / 5
Capterra rating4.4 / 5

What is MLflow?

MLflow is the leading open source platform for managing the ML lifecycle (tracking, projects, models, registry), now with LLM features, suited to teams wanting open source, standard ML lifecycle management. An open source project (created by Databricks, now widely adopted), it provides experiment tracking (logging parameters, metrics and artifacts), packaging (projects), model management and a model registry (versioning and stages), and increasingly LLM and generative AI features (tracing, evaluation), all free and open source, deployable anywhere, and available managed within Databricks. The de facto open source standard for ML lifecycle management, MLflow is a leading choice for teams wanting open, standard, free MLOps. For open source ML lifecycle management, MLflow is a leading choice.

MLflow's distinction is being the open source standard for ML lifecycle management, free and widely adopted. It provides tracking, packaging, model management and a registry as open source, deployable anywhere, with a huge community and broad integration, and growing LLM features, which proprietary platforms do not match for open source ubiquity and no licensing cost. That open source, standard, free ML lifecycle management sets it apart.

MLflow at a glanceMLflow is the leading open source platform for the ML lifecycle [MLflow]. It holds about a 4.4 out of 5 rating on G2 and 4.4 on Capterra, the open source standard for ML lifecycle management.

Who MLflow is best for

MLflow is best for ML teams and organisations that want an open source, free, standard platform for the ML lifecycle (experiment tracking, model management, registry), deployable anywhere, with growing LLM features, and (optionally) managed within Databricks. It suits open source ML lifecycle management. It is a weaker fit for teams wanting a polished managed experience out of the box, where Weights and Biases fits, a purely LLM observability tool, where LangSmith or Langfuse fit, or just an AI gateway, where Portkey or LiteLLM fit.

Key features

The core strengths are experiment tracking (parameters, metrics, artifacts), packaging (projects), model management and a model registry (versioning, stages), growing LLM and generative AI features (tracing, evaluation), open source and deployable anywhere, broad integration, and managed availability in Databricks. MLflow offers open source ML lifecycle management. Its open source, standard, free nature is its defining advantage.

MLflow pricing

MLflow is fully open source and free to use (you deploy and run it yourself, paying only for your infrastructure), and is also available managed as part of Databricks (paid, within the Databricks platform). So the software is free and open source, with a managed option via Databricks. Confirm details on the MLflow site.

Integrations and ecosystem

MLflow integrates with virtually all major ML frameworks and libraries (scikit learn, PyTorch, TensorFlow, XGBoost and more), platforms and the ML stack, with a huge community and ecosystem, plus Databricks. Its broad integration and community, which make it a universal ML lifecycle tool, are major strengths.

MLflow and open source standard

Being the open source standard for ML lifecycle management, free and widely adopted, is the signature strength: MLflow provides tracking, packaging, model management and a registry as open source, deployable anywhere, with a huge community and growing LLM features, which proprietary platforms do not match for open source ubiquity and no licensing cost. For open source ML lifecycle management, this is the main reason to choose MLflow.

Ease of use and support

MLflow is well rated for being the open source ML lifecycle standard, free, flexible and broadly integrated, used everywhere in ML, with community support (and Databricks for managed), though reviewers note self hosting and managing it needs effort, and it is less polished out of the box than some managed platforms. Its reviews praise open source value, with self hosting effort the main consideration.

Is MLflow worth it?

For ML teams wanting an open source, free, standard platform for the ML lifecycle, deployable anywhere, with growing LLM features, MLflow is an excellent choice. The main caution is that it needs self hosting effort and is less polished than managed platforms, so a polished managed experience suits Weights and Biases, LLM observability LangSmith or Langfuse, and a gateway Portkey, but for open source ML lifecycle management MLflow leads.

Pricing

Open source

$0
self host
  • Full ML lifecycle
  • Tracking and registry
  • Deploy anywhere

Managed (Databricks)

Paid
within Databricks
  • Managed MLflow
  • Platform integration
  • Databricks users

Pros and cons

Pros

  • Open source and free
  • The ML lifecycle standard
  • Tracking, registry and packaging
  • Growing LLM features
  • Broad integration and community

Cons

  • Self hosting needs effort
  • Less polished than managed platforms
  • Managed only via Databricks
  • Setup and operations

MLflow alternatives

These llmops & ai dev platforms tools come up most often against MLflow.

LangSmith

LLM observability, testing, and evaluation platform

Weights & Biases

ML and LLM experiment tracking platform

Langfuse

Open-source LLM engineering and observability

MLflow FAQ

What is MLflow used for?
MLflow is used to manage the machine learning lifecycle: experiment tracking (parameters, metrics, artifacts), packaging (projects), model management and a model registry (versioning and stages), and increasingly LLM features, all open source, so ML teams develop, track, version and manage models in a standard, open way.
Is MLflow free?
Yes. MLflow is fully open source and free to use (you deploy and run it yourself, paying only for your infrastructure), and is also available managed as part of Databricks (paid), so the software itself is free with an optional managed platform.
MLflow vs Weights and Biases, which is better?
MLflow is the free, open source ML lifecycle standard (self hosted, deploy anywhere), while Weights and Biases is a polished, managed platform with rich tracking and collaboration. MLflow suits teams wanting open source and no licensing cost; W&B suits those wanting managed, feature rich convenience.
Does MLflow support LLMs?
Yes. MLflow has added LLM and generative AI features (such as tracing and evaluation) to its core ML lifecycle capabilities, so teams can use it for LLM work alongside traditional ML, extending the open source standard into LLMOps.
Is MLflow hard to set up?
MLflow is straightforward to start with but, being open source and self hosted, running and managing it in production (tracking server, registry, storage) takes some effort and operations, which is the trade off for its openness and no licensing cost. Managed MLflow via Databricks reduces this for Databricks users.
Who created MLflow?
MLflow was created by Databricks and is now a widely adopted open source project (with broad community governance), the de facto standard for ML lifecycle management, available free and open source anywhere, as well as managed within the Databricks platform.

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