AI · head to head
Galileo vs MLflow
Galileo
AI
Evaluation and observability platform for GenAI applications and agents
- From
- Free
- Rated
- -
MLflow
Machine Learning
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Galileo the free plan is limited to 5,000 traces per month, which is quickly outgrown by production workloads.; MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- They diverge on capability: Galileo covers Pre-built evaluations, MLflow covers Experiment tracking.
Where they differ
Only the attributes on which Galileo and MLflow actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Galileo
- Pre-built evaluations
- Ground truth capture
- Luna models
- Agent behavior analysis
- Production guardrails
- Flexible deployment
Only in MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- TensorFlow
- PyTorch
- scikit-learn
What people use each for
The jobs each tool is most often brought in to do.
Galileo
- Evaluating RAG and agent applications before production releasenot MLflow
- Monitoring live GenAI applications for failures and driftnot MLflow
- Applying real-time guardrails without custom integration worknot MLflow
- Reducing evaluation costs using distilled Luna judge modelsnot MLflow
MLflow
- Machine learningnot Galileo
- Data analysisnot Galileo
- Model trainingnot Galileo
- Predictive analyticsnot Galileo
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Galileo
- The free plan is limited to 5,000 traces per month, which is quickly outgrown by production workloads.
- Real-time guardrails and unlimited trace capacity are reserved for the custom-priced Enterprise tier.
- Pro plan pricing scales with trace volume, so costs can grow unpredictably as usage increases.
- On-premises deployment requires an Enterprise contract rather than being available self-serve.
MLflow
- Requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- Basic UI and visualization: lacks rich interactive dashboards and real-time monitoring compared to commercial platforms
- Limited collaboration: no built-in role-based access control or multi-user management features
- Production monitoring gaps: drift detection, explainability, and alerting require separate dedicated tools
Pricing, plan by plan
Galileo
Free- FreeFree
- 5,000 traces/month
- Unlimited users
- Unlimited custom evaluations
- Pro$100/month
- 50,000 traces/month
- Standard role-based access control
- Advanced analytics and insights
- Enterprise$undefined/mo
- Unlimited trace capacity
- Custom rate limits
- Hosted, VPC, or on-prem deployment
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Which should you pick?
Choose Galileo if
- You need pre-built evaluations.
- You want to start without paying.
- You work on web, api.
- You also want ground truth capture.
Choose MLflow if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Python API, REST API.
- You also want model registry.
Questions people ask
- Is Galileo or MLflow better?
- Neither clearly leads. Galileo starts at Free and MLflow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Galileo or MLflow?
- Galileo starts at Free and MLflow at Free.
- Does Galileo or MLflow run on more platforms?
- Galileo runs on web, api. MLflow runs on Web, Python API, REST API.
- Can I use Galileo for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Galileo best used for?
- Galileo is most often used for evaluating rag and agent applications before production release, monitoring live genai applications for failures and drift, applying real-time guardrails without custom integration work, reducing evaluation costs using distilled luna judge models. Of those, evaluating rag and agent applications before production release and monitoring live genai applications for failures and drift are not what MLflow is typically brought in for.
- What can Galileo do that MLflow cannot?
- Galileo covers Pre-built evaluations, Ground truth capture, Luna models, Agent behavior analysis. MLflow covers Experiment tracking, Model registry, Model packaging, Deployment.
Answered from the vendors’ own pages
Galileo: What does Galileo cost?
Galileo offers a free plan, a Pro plan at $100/month billed yearly (with a 33% annual discount), and a custom-priced Enterprise plan for unlimited trace capacity.
SourceMLflow: Is MLflow free to use?
Yes, MLflow is completely open-source and free. However, teams typically incur infrastructure costs for hosting and maintaining the MLflow tracking server. Databricks offers Managed MLflow as a commercial option for cloud deployment.
SourceGalileo: Is there a free plan, and what are its limits?
The Free plan includes 5,000 traces per month with unlimited users and unlimited custom evaluations, aimed at developers and small teams experimenting with GenAI.
SourceMLflow: Can MLflow track experiments for different ML frameworks?
Yes, MLflow is framework-agnostic and works with TensorFlow, PyTorch, scikit-learn, XGBoost, and any other ML framework. This flexibility is a core design principle allowing teams to use diverse tools.
SourceGalileo: How is usage metered?
Galileo's pricing scales based on the number of traces processed each month, with Free capped at 5,000, Pro at 50,000, and Enterprise offering unlimited trace capacity.
SourceMLflow: Does MLflow include a model registry?
Yes, MLflow Model Registry (added in 2018) provides a central model store with versioning, stage transitions, and deployment tracking. This enables production model governance and lineage tracking.
SourceMLflow: What are MLflow's main limitations?
MLflow requires significant infrastructure setup and maintenance. The UI is basic compared to commercial tools, collaboration is limited without third-party RBAC solutions, and production monitoring requires separate tools for drift detection and alerting.
SourceMLflow: Can MLflow handle LLM and agent tracing?
MLflow added LLM and agent tracing capabilities in recent versions, though the native support is limited compared to specialized LLM observability platforms that replaced weak LLM tracing.
SourceRelated pages
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