Machine Learning & Data Science · head to head
MLflow vs Amazon Redshift ML
MLflow
Machine Learning & Data Science
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
Amazon Redshift ML
Machine Learning & Data Science
Create machine learning models using SQL
- From
- Free
- Rated
- -
The short version
- Each has a real cost: MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves; Amazon Redshift ML free tier covers only two CREATE MODEL requests per month for two months, capped at 100,000 cells per request; beyond that training is metered at $20 per million cells for the first 10 million, dropping in tiers to $7 per million cells over 100 million
- They diverge on capability: MLflow covers Experiment tracking, Amazon Redshift ML covers SQL-based ML.
Where they differ
Only the attributes on which MLflow and Amazon Redshift ML actually diverge.
| Attribute | MLflow | Amazon Redshift ML |
|---|---|---|
| Pricing model | open-source | usage-based |
| Platforms | Web, Python API, REST API | Web |
| Founded | 2018 | 2006 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).
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 MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- TensorFlow
- PyTorch
- scikit-learn
Only in Amazon Redshift ML
- SQL-based ML
- AutoML
- SageMaker integration
- BYOM support
- In-database predictions
- Amazon Redshift
- SageMaker
- S3
What people use each for
The jobs each tool is most often brought in to do.
MLflow
- Machine learningnot Amazon Redshift ML
- Data analysisnot Amazon Redshift ML
- Model trainingnot Amazon Redshift ML
- Predictive analyticsnot Amazon Redshift ML
Amazon Redshift ML
- Training and running machine learning models directly from SQL inside Amazon Redshiftnot MLflow
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
Amazon Redshift ML
- Free tier covers only two CREATE MODEL requests per month for two months, capped at 100,000 cells per request; beyond that training is metered at $20 per million cells for the first 10 million, dropping in tiers to $7 per million cells over 100 million
Pricing, plan by plan
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Amazon Redshift ML
Free- Free TrialFree
- 2-month trial
- 750 DC2.Large hours
- On-Demand$0.25/hour
- Per-node pricing
- SageMaker training
Which should you pick?
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.
Choose Amazon Redshift ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl.
Questions people ask
- Is MLflow or Amazon Redshift ML better?
- Neither clearly leads. MLflow starts at Free and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, MLflow or Amazon Redshift ML?
- MLflow starts at Free and Amazon Redshift ML at Free.
- Does MLflow or Amazon Redshift ML run on more platforms?
- MLflow runs on Web, Python API, REST API. Amazon Redshift ML runs on Web.
- Can I use MLflow for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is MLflow best used for?
- MLflow is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Amazon Redshift ML is typically brought in for.
- What can MLflow do that Amazon Redshift ML cannot?
- MLflow covers Experiment tracking, Model registry, Model packaging, Deployment. Amazon Redshift ML covers SQL-based ML, AutoML, SageMaker integration, BYOM support.
Answered from the vendors’ own pages
MLflow: 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.
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.
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
More on Amazon Redshift ML
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