Spreadsheet & Data · head to head
Baserow vs MLflow
MLflow
Machine Learning & Data Science
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Baserow the free tier is capped at 3,000 rows and 2GB of storage per workspace; MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- They diverge on capability: Baserow covers Database tables, MLflow covers Experiment tracking.
Where they differ
Only the attributes on which Baserow 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 Baserow
- Database tables
- Multiple views
- Forms
- API access
- Real-time collaboration
- Templates
- Plugins
- Self-hosting
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.
Baserow
- Self-hosting an open source alternative to a spreadsheet databasenot MLflow
- Structured team data with Kanban, calendar and grid viewsnot MLflow
- Building internal tools on top of a database with an APInot MLflow
- Sharing data with external app users without giving them full seatsnot MLflow
MLflow
- Machine learningnot Baserow
- Data analysisnot Baserow
- Model trainingnot Baserow
- Predictive analyticsnot Baserow
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Baserow
- The free tier is capped at 3,000 rows and 2GB of storage per workspace
- Kanban, calendar and survey views need Premium at $10 per user per month billed yearly
- Role-based permissions, audit logs and SSO require Premium or higher
- Row limits are per workspace rather than per table, so splitting data across bases does not raise the ceiling
- Automation runs are metered as credits, 2,000 a month on free
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
Baserow
Free- FreeFree
- Unlimited rows
- Core features
- Community support
- Premium$5/user/month
- Row comments
- Kanban view
- Survey form
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Which should you pick?
Choose Baserow if
- You need database tables.
- You want to start without paying.
- You work on Web, Api, Self-hosted.
- You also want multiple views.
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 Baserow or MLflow better?
- Neither clearly leads. Baserow 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, Baserow or MLflow?
- Baserow starts at Free and MLflow at Free.
- Does Baserow or MLflow run on more platforms?
- Baserow runs on Web, Api, Self-hosted. MLflow runs on Web, Python API, REST API.
- Can I use Baserow for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Baserow best used for?
- Baserow is most often used for self-hosting an open source alternative to a spreadsheet database, structured team data with kanban, calendar and grid views, building internal tools on top of a database with an api, sharing data with external app users without giving them full seats. Of those, self-hosting an open source alternative to a spreadsheet database and structured team data with kanban, calendar and grid views are not what MLflow is typically brought in for.
- What can Baserow do that MLflow cannot?
- Baserow covers Database tables, Multiple views, Forms, API access. MLflow covers Experiment tracking, Model registry, Model packaging, Deployment.
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
Other head to heads
- Baserow vs Tableau
- Baserow vs Looker
- Baserow vs Metabase
- Baserow vs Redash
- Baserow vs Fibery
- Baserow vs Apache Superset
- Baserow vs Budibase
- Baserow vs NocoDB
- Baserow vs AWS SageMaker
- Baserow vs Google Vertex AI
- Baserow vs Azure Machine Learning
- Baserow vs DataRobot
- Baserow vs Snowflake
- Baserow vs TensorFlow
- Baserow vs Comet ML
- Baserow vs Keras
- Baserow vs Jupyter
- Baserow vs PyTorch
- Baserow vs scikit-learn
- Baserow vs Apache Spark MLlib
- Baserow vs Weights & Biases
- Baserow vs Alteryx
- Baserow vs Anaconda
- Baserow vs Databricks
- Baserow vs Dataiku
- Baserow vs DVC
- MLflow vs Tableau
- MLflow vs Looker
- MLflow vs Metabase
- MLflow vs Redash
- MLflow vs Fibery
- MLflow vs Apache Superset
- MLflow vs Budibase
- MLflow vs NocoDB
- MLflow vs AWS SageMaker
- MLflow vs Google Vertex AI
- MLflow vs Azure Machine Learning
- MLflow vs DataRobot
- MLflow vs Snowflake
- MLflow vs TensorFlow
- MLflow vs Comet ML
- MLflow vs Keras
- MLflow vs Jupyter
- MLflow vs PyTorch
- MLflow vs scikit-learn
- MLflow vs Apache Spark MLlib
- MLflow vs Weights & Biases
- MLflow vs Alteryx
- MLflow vs Anaconda
- MLflow vs Databricks
- MLflow vs Dataiku
- MLflow vs DVC