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Machine Learning · head to head

DVC vs Immuta

DVC logo

DVC

Machine Learning

Git-style versioning for data sets and models, with the files kept in object storage

From
Free
Free plan
Yes
Visit DVCFull review
Immuta logo

Immuta

Databases

Attribute-based access control and masking applied inside Snowflake, Databricks and BigQuery

From
On request
Free plan
No
Visit ImmutaFull review

The short version

  • DVC is free; Immuta prices on request.
  • Only DVC lists pointer-file versioning; only Immuta lists attribute-based policy.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which DVC and Immuta actually diverge.

Attributes where DVC and Immuta differ
AttributeDVCImmuta
Starting priceFreeOn request
Pricing modelOpen-sourceQuote
Free tierYesNo
PlatformsLinux, macOS, WindowsWeb, API
CategoryMachine LearningDatabases

Not yet recorded: founded for Immuta.

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 Immuta

  • Attribute-based policy
  • Native enforcement
  • Dynamic masking
  • Row-level filtering
  • Purpose-based access
  • Sensitive data tagging
  • Audit logging
  • Multi-platform

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 Immuta
  • Keeping large training data out of Git while still having a repository that describes it preciselynot Immuta
  • Skipping expensive preprocessing stages that have not changed, when iterating on a later stage of a pipelinenot Immuta
  • Teams that need reproducibility but cannot get approval or budget to stand up a platform for itnot Immuta

Immuta

  • A bank whose Snowflake estate has grown to tens of thousands of roles that no one can review before an auditnot DVC
  • A healthcare analytics team that must let researchers query patient data with identifiers masked unless a specific purpose is recordednot DVC
  • A multinational applying different residency and access rules per jurisdiction to the same tables without duplicating datasetsnot DVC
  • An organisation running both Snowflake and Databricks that wants one policy set rather than two divergent implementationsnot 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

Immuta

  • Contracts commonly start around one hundred to two hundred thousand US dollars a year for mid-market deployments and exceed five hundred thousand at enterprise scale, which excludes most data teams without a regulatory mandate
  • Policy is only as good as the data classification underneath it, so an organisation with poorly tagged columns will spend months on classification before Immuta enforces anything useful
  • Native enforcement means capability varies by platform, and a feature available on Snowflake may be absent or behave differently on BigQuery, which undermines the promise of one policy set everywhere
  • Adding an access governance layer creates a new dependency in the path to data: a misconfigured policy silently returns fewer rows rather than erroring, and analysts can act on incomplete results without noticing
  • It governs cloud data platforms, so personal data in operational databases, files and SaaS applications sits outside its scope and needs separate controls, meaning Immuta is rarely the whole answer

Pricing, plan by plan

DVC

Free
  • Open SourceFree
    • Data versioning
    • Pipeline management
    • Experiment tracking
  • DVC StudioFree
    • Web UI
    • Team collaboration
    • Visualizations

Immuta

On request
  • Immuta PlatformOn request
    • Attribute-based policy authoring
    • Native enforcement in supported data platforms
    • Dynamic masking and row-level security

Which should you pick?

Choose DVC if

  • You need pointer-file versioning.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want remote storage backends.
DVC review

Choose Immuta if

  • You need attribute-based policy.
  • You work on Web, API.
  • You also want native enforcement.
Immuta review

Questions people ask

Is DVC or Immuta better?
Neither clearly leads. DVC starts at Free and Immuta at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DVC or Immuta?
DVC has a free tier; the other does not. Paid plans start at Free for DVC and On request for Immuta.
Does DVC or Immuta run on more platforms?
DVC runs on Linux, macOS, Windows. Immuta runs on Web, API.
Can I use DVC for free?
Yes. DVC has a free tier, so you can try it without paying. Immuta starts at On request.
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 Immuta is typically brought in for.
What can DVC do that Immuta cannot?
DVC covers Pointer-file versioning, Remote storage backends, Pipeline definitions, Stage caching. Immuta covers Attribute-based policy, Native enforcement, Dynamic masking, Row-level filtering.

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.

Immuta: Does Immuta sit in the query path?

No. It compiles policies into the data platform's own native controls, so queries run at normal speed through your existing tools.

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.

Immuta: What does it cost?

Not published. Market data suggests roughly 100,000 to 200,000 US dollars a year for mid-market deployments and considerably more at enterprise scale.

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.

Immuta: Is Immuta still independent?

Yes. It remains independently owned, unlike several competitors in data access governance that have been acquired.

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.

Immuta: Does it work across more than one warehouse?

Yes, one policy set can target Snowflake, Databricks, BigQuery and Starburst, though enforcement capability varies by platform.

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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