Machine Learning · head to head
DVC vs Ory Kratos
DVC
Machine Learning
Git-style versioning for data sets and models, with the files kept in object storage
- 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.; Ory Kratos headless means you build every screen, which is significant work compared with a hosted login page
- They diverge on capability: DVC covers Pointer-file versioning, Ory Kratos covers Headless API.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which DVC and Ory Kratos actually diverge.
| Attribute | DVC | Ory Kratos |
|---|---|---|
| Pricing model | open-source | Open-source self-hosted, with a paid managed network |
| Platforms | Linux, Mac, Windows | Linux, Docker, Kubernetes, Self-hosted |
| Category | Machine Learning | Cybersecurity |
| Founded | 2018 | Unknown |
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 Ory Kratos
- Headless API
- Self-service flows
- Multi-factor authentication
- Pluggable identity schemas
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 Ory Kratos
- Keeping large training data out of Git while still having a repository that describes it preciselynot Ory Kratos
- Skipping expensive preprocessing stages that have not changed, when iterating on a later stage of a pipelinenot Ory Kratos
- Teams that need reproducibility but cannot get approval or budget to stand up a platform for itnot Ory Kratos
Ory Kratos
- Products needing complete control over the look and flow of authenticationnot DVC
- Applications that must not hand user identity data to a third partynot DVC
- Teams building identity as infrastructure across several servicesnot 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.
Ory Kratos
- Headless means you build every screen, which is significant work compared with a hosted login page
- More moving parts than a monolithic IAM: Kratos handles identity, and OAuth2 needs Ory Hydra alongside
- Documentation assumes real familiarity with identity concepts and is not a gentle introduction
- Self-hosting identity carries the security and availability burden that hosted providers absorb
Pricing, plan by plan
DVC
Free- Open SourceFree
- Data versioning
- Pipeline management
- Experiment tracking
- DVC StudioFree
- Web UI
- Team collaboration
- Visualizations
Ory Kratos
Free- Self-hostedFree
- Full identity server
- All flows
- Community support
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 Ory Kratos if
- You need headless api.
- You want to start without paying.
- You work on Linux, Docker, Kubernetes, Self-hosted.
- You also want self-service flows.
Questions people ask
- Is DVC or Ory Kratos better?
- Neither clearly leads. DVC starts at Free and Ory Kratos at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, DVC or Ory Kratos?
- DVC starts at Free and Ory Kratos at Free.
- Does DVC or Ory Kratos run on more platforms?
- DVC runs on Linux, Mac, Windows. Ory Kratos runs on Linux, Docker, Kubernetes, Self-hosted.
- 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 Ory Kratos is typically brought in for.
- What can DVC do that Ory Kratos cannot?
- DVC covers Pointer-file versioning, Remote storage backends, Pipeline definitions, Stage caching. Ory Kratos covers Headless API, Self-service flows, Multi-factor authentication, Pluggable identity schemas.
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.
Ory Kratos: Is Ory Kratos free?
Yes, open source and free to self-host. Ory Network is a paid managed service.
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.
Ory Kratos: What does headless mean here?
Kratos provides identity flows as APIs and no user interface. You build the login, registration and recovery screens yourself.
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.
Ory Kratos: Does Kratos do OAuth2?
No. Kratos handles user identity; OAuth2 and OpenID Connect provider functionality is Ory Hydra, a separate component.
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.
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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- Ory Kratos vs Envysion
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