Software · head to head
Baserow vs DVC
The short version
- Each has a real cost: Baserow the free tier is capped at 3,000 rows and 2GB of storage per workspace; DVC dVC is Apache 2.0 licensed open source with no enterprise tier or paid support offering documented in the project itself; teams needing SLA-backed support get nothing from the DVC project directly.
- They diverge on capability: Baserow covers Database tables, DVC covers Data versioning.
Where they differ
Only the attributes on which Baserow and DVC actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Unknown).
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 DVC
- Data versioning
- Pipeline management
- Experiment tracking
- Remote storage
- Git integration
- Git
- S3
- Azure Blob
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 DVC
- Structured team data with Kanban, calendar and grid viewsnot DVC
- Building internal tools on top of a database with an APInot DVC
- Sharing data with external app users without giving them full seatsnot DVC
DVC
- 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
DVC
- DVC is Apache 2.0 licensed open source with no enterprise tier or paid support offering documented in the project itself; teams needing SLA-backed support get nothing from the DVC project directly.
Pricing, plan by plan
Baserow
Free- FreeFree
- Unlimited rows
- Core features
- Community support
- Premium$5/user/month
- Row comments
- Kanban view
- Survey form
DVC
Free- Open SourceFree
- Data versioning
- Pipeline management
- Experiment tracking
- DVC StudioFree
- Web UI
- Team collaboration
- Visualizations
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 DVC if
- You need data versioning.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want pipeline management.
Questions people ask
- Is Baserow or DVC better?
- Neither clearly leads. Baserow starts at Free and DVC at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Baserow or DVC?
- Baserow starts at Free and DVC at Free.
- Does Baserow or DVC run on more platforms?
- Baserow runs on Web, Api, Self-hosted. DVC runs on Linux, Mac, Windows.
- 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 DVC is typically brought in for.
- What can Baserow do that DVC cannot?
- Baserow covers Database tables, Multiple views, Forms, API access. DVC covers Data versioning, Pipeline management, Experiment tracking, Remote storage.
Related pages
Keep looking
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 MLflow
- 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
- DVC vs Tableau
- DVC vs Looker
- DVC vs Metabase
- DVC vs Redash
- DVC vs Fibery
- DVC vs Apache Superset
- DVC vs Budibase
- DVC vs NocoDB
- DVC vs AWS SageMaker
- DVC vs Google Vertex AI
- DVC vs Azure Machine Learning
- DVC vs DataRobot
- DVC vs Snowflake
- DVC vs TensorFlow
- DVC vs Comet ML
- DVC vs Keras
- DVC vs MLflow
- DVC vs Jupyter
- DVC vs PyTorch
- DVC vs scikit-learn
- DVC vs Apache Spark MLlib
- DVC vs Weights & Biases
- DVC vs Alteryx
- DVC vs Anaconda
- DVC vs Databricks
- DVC vs Dataiku


