Machine Learning · head to head
DVC vs Passbolt
DVC
Machine Learning
Git-style versioning for data sets and models, with the files kept in object storage
- From
- Free
- Rated
- -
Passbolt
Cybersecurity
Open-source password manager for teams with self-hosted or cloud deployment
- 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.; Passbolt pro Edition requires a minimum of 10 users, making it costlier for very small teams.
- They diverge on capability: DVC covers Pointer-file versioning, Passbolt covers Password sharing and folders.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which DVC and Passbolt actually diverge.
Identical on both: starting price (Free), pricing model (open-source), 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 Passbolt
- Password sharing and folders
- Groups and role-based access
- Browser extensions and CLI
- Open API
- LDAP provisioning and SSO
- Activity audit log
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 Passbolt
- Keeping large training data out of Git while still having a repository that describes it preciselynot Passbolt
- Skipping expensive preprocessing stages that have not changed, when iterating on a later stage of a pipelinenot Passbolt
- Teams that need reproducibility but cannot get approval or budget to stand up a platform for itnot Passbolt
Passbolt
- Self-hosting a team password manager for data sovereigntynot DVC
- Provisioning vault access from an existing LDAP/AD directorynot DVC
- Sharing credentials across engineering or IT teams via folders and groupsnot DVC
- Automating credential retrieval through the CLI or open APInot 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.
Passbolt
- Pro Edition requires a minimum of 10 users, making it costlier for very small teams.
- Community Edition lacks SSO and LDAP provisioning, which many organizations need for onboarding at scale.
- Self-hosting the Community Edition requires infrastructure and maintenance effort compared to fully managed competitors.
- Enterprise-tier support and HA consulting require custom, quote-based pricing rather than transparent rates.
Pricing, plan by plan
DVC
Free- Open SourceFree
- Data versioning
- Pipeline management
- Experiment tracking
- DVC StudioFree
- Web UI
- Team collaboration
- Visualizations
Passbolt
Free- Community EditionFree
- Unlimited users
- Password sharing and folders
- Role-based access control
- Pro Edition$4.9/month
- Minimum 10 users, billed annually
- LDAP/AD provisioning
- Single sign-on
- Enterprise Edition$undefined/month
- High availability and disaster recovery consulting
- White-glove migration
- Custom feature development
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 Passbolt if
- You need password sharing and folders.
- You want to start without paying.
- You work on web, windows, mac, linux, api.
- You also want groups and role-based access.
Questions people ask
- Is DVC or Passbolt better?
- Neither clearly leads. DVC starts at Free and Passbolt at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, DVC or Passbolt?
- DVC starts at Free and Passbolt at Free.
- Does DVC or Passbolt run on more platforms?
- DVC runs on Linux, Mac, Windows. Passbolt runs on web, windows, mac, linux, api.
- 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 Passbolt is typically brought in for.
- What can DVC do that Passbolt cannot?
- DVC covers Pointer-file versioning, Remote storage backends, Pipeline definitions, Stage caching. Passbolt covers Password sharing and folders, Groups and role-based access, Browser extensions and CLI, Open API.
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.
Passbolt: Is Passbolt free to use?
Yes. The Community Edition is free forever with unlimited users, including password sharing, folders, groups, role-based access, browser extensions, a CLI, and an open API.
SourceDVC: 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.
Passbolt: What does the Pro Edition cost and add?
Pro Edition costs $4.90 per user per month billed annually, with a 10-user minimum, and adds LDAP/AD provisioning, single sign-on, account recovery escrow, activity audit logs, and next-business-day support.
SourceDVC: 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.
Passbolt: What license is Passbolt distributed under?
All Passbolt editions, including Pro and Enterprise, are distributed under the AGPL v3 open-source license.
SourceDVC: 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.
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.
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