Machine Learning · head to head
DVC vs Rowy
DVC
Machine Learning
Git-style versioning for data sets and models, with the files kept in object storage
- From
- Free
- Rated
- -
Rowy
Spreadsheets
Spreadsheet interface for Google Cloud Firestore with cloud functions written in the browser
- From
- Free
- Rated
- -
The short version
- Each has a real cost: DVC dVC knows only about files that were added through DVC, so one person copying data in by hand leaves a pipeline that reproduces to a different answer with no error and nothing to indicate which result is the real one.; Rowy every Firestore limit is inherited, including the one megabyte per document ceiling and roughly one sustained write per second per document, which surprises users who expect spreadsheet behaviour.
- They diverge on capability: DVC covers Pointer-file versioning, Rowy covers Firestore backed grid.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which DVC and Rowy 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 DVC
- Pointer-file versioning
- Remote storage backends
- Pipeline definitions
- Stage caching
- Experiment tracking
- Metrics and plots comparison
- Data registry pattern
- Content-addressed cache
Only in Rowy
- Firestore backed grid
- Browser authored cloud functions
- Typed columns
- Runs in your Google Cloud project
- Role based access
- Form view
- Open source
What people use each for
The jobs each tool is most often brought in to do.
DVC
- Making a model reproducible by tying the exact data set version, code commit and parameters together in one Git historynot Rowy
- Keeping large training data out of Git while still having a repository that describes it preciselynot Rowy
- Skipping expensive preprocessing stages that have not changed, when iterating on a later stage of a pipelinenot Rowy
- Teams that need reproducibility but cannot get approval or budget to stand up a platform for itnot Rowy
Rowy
- A Firebase application that needs an internal admin interface without building onenot DVC
- Operations staff correcting production records in Firestore without developer involvementnot DVC
- Attaching a small transformation or notification function to a field change without a full deployment pipelinenot DVC
- A content team populating documents that a mobile application reads directly from Firestorenot DVC
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
DVC
- DVC knows only about files that were added through DVC, so one person copying data in by hand leaves a pipeline that reproduces to a different answer with no error and nothing to indicate which result is the real one.
- Every tracked revision writes a new pointer into Git and a new copy into the remote cache, so a data set revised daily accumulates full copies in object storage and the storage bill grows with the length of the history rather than the size of the data.
- Merge conflicts in dvc.lock and dvc.yaml are routine on parallel branches and are unreadable to anyone who has not learned the format, which in practice means the person who introduced DVC resolves all of them.
- Checking out a large data set materialises it in the working directory, so a laptop working against a repository with several hundred gigabytes tracked needs disk for the workspace and the cache together, and the reflink or hardlink optimisations that avoid doubling that are filesystem-dependent.
- It has no access control of its own and inherits whatever the remote grants, so a repository everyone can read plus a bucket everyone can read means everyone can reconstruct every historical version of every data set, which is frequently not what was intended.
Rowy
- Every Firestore limit is inherited, including the one megabyte per document ceiling and roughly one sustained write per second per document, which surprises users who expect spreadsheet behaviour.
- Firestore has no joins, so linking tables in the grid is a convenience layer rather than a relational feature and reports across collections still need a separate query.
- It is useful only to teams already on Firebase, so choosing it effectively locks the operational tooling to one cloud vendor.
- Query costs are billed by document read, and an unfiltered grid on a large collection can generate a bill that a spreadsheet user has no intuition for.
- Development activity has slowed relative to the broader category, so evaluate current maintenance before making it load bearing for internal operations.
Pricing, plan by plan
DVC
Free- Open SourceFree
- Data versioning
- Pipeline management
- Experiment tracking
- DVC StudioFree
- Web UI
- Team collaboration
- Visualizations
Rowy
Free- Open source self hostedFree
- No licence fee
- Deploys into your own Google Cloud project
- You pay Google Cloud for Firestore reads, writes and storage
Which should you pick?
Choose DVC if
- You need pointer-file versioning.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want remote storage backends.
Choose Rowy if
- You need firestore backed grid.
- You want to start without paying.
- You also want browser authored cloud functions.
Questions people ask
- Is DVC or Rowy better?
- Neither clearly leads. DVC starts at Free and Rowy at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, DVC or Rowy?
- DVC starts at Free and Rowy at Free.
- Does DVC or Rowy run on more platforms?
- DVC runs on Linux, Mac, Windows. Rowy runs on Web.
- Can I use DVC for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is DVC best used for?
- DVC is most often used for making a model reproducible by tying the exact data set version, code commit and parameters together in one git history, keeping large training data out of git while still having a repository that describes it precisely, skipping expensive preprocessing stages that have not changed, when iterating on a later stage of a pipeline, teams that need reproducibility but cannot get approval or budget to stand up a platform for it. Of those, making a model reproducible by tying the exact data set version, code commit and parameters together in one git history and keeping large training data out of git while still having a repository that describes it precisely are not what Rowy is typically brought in for.
- What can DVC do that Rowy cannot?
- DVC covers Pointer-file versioning, Remote storage backends, Pipeline definitions, Stage caching. Rowy covers Firestore backed grid, Browser authored cloud functions, Typed columns, Runs in your Google Cloud project.
Answered from the vendors’ own pages
DVC: Does DVC put my data in Git?
No. Git gets a small pointer file containing a hash. The data goes to a cache on disk and to a remote you configure, such as an S3 bucket.
Rowy: Where is my data stored?
In your own Firestore instance inside your own Google Cloud project. Rowy does not hold a copy.
DVC: Do I need to run a server?
No, and that is most of its appeal. It is a command line tool plus storage you already have. DVC Studio, the hosted web interface, is optional and separately paid.
Rowy: What does it cost to run?
No licence fee. You pay Google Cloud for Firestore reads, writes, storage and function invocations, and an open grid on a big collection reads a lot.
DVC: How is it different from Git LFS?
Git LFS versions large files and stops there. DVC also defines pipelines, tracks which stage produced which output, records metrics and lets you compare experiments, and it works with ordinary object storage rather than an LFS server.
Rowy: Can it do joins across collections?
Not really. Firestore does not support joins, and no interface layer can add them.
DVC: Is it free?
The tool is Apache 2.0 and free. You pay for the object storage that holds the data, and optionally for DVC Studio.
Rowy: Is there a row limit?
Not from Rowy. The limits that bite are Firestore document size and per document write throughput.
DVC: Can several people work on the same data set?
Yes, through the shared remote, but only if all of them use DVC for every change. The tool cannot enforce a discipline it does not own, and a single manual copy silently breaks the guarantee.
Rowy: Is it suitable if I am not on Firebase?
No. It is specifically a Firestore interface.
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