Machine Learning · head to head
DVC vs Galileo
DVC
Machine Learning
Git-style versioning for data sets and models, with the files kept in object storage
- From
- Free
- Free plan
- Yes
Galileo
AI
Evaluation and observability platform for GenAI applications and agents
- From
- Free
- Free plan
- Yes
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.; Galileo the free plan is limited to 5,000 traces per month, which is quickly outgrown by production workloads.
- They diverge on capability: DVC covers Pointer-file versioning, Galileo covers Pre-built evaluations.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which DVC and Galileo 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 Galileo
- Pre-built evaluations
- Ground truth capture
- Luna models
- Agent behavior analysis
- Production guardrails
- Flexible deployment
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 Galileo
- Keeping large training data out of Git while still having a repository that describes it preciselynot Galileo
- Skipping expensive preprocessing stages that have not changed, when iterating on a later stage of a pipelinenot Galileo
- Teams that need reproducibility but cannot get approval or budget to stand up a platform for itnot Galileo
Galileo
- Evaluating RAG and agent applications before production releasenot DVC
- Monitoring live GenAI applications for failures and driftnot DVC
- Applying real-time guardrails without custom integration worknot DVC
- Reducing evaluation costs using distilled Luna judge modelsnot 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.
Galileo
- The free plan is limited to 5,000 traces per month, which is quickly outgrown by production workloads.
- Real-time guardrails and unlimited trace capacity are reserved for the custom-priced Enterprise tier.
- Pro plan pricing scales with trace volume, so costs can grow unpredictably as usage increases.
- On-premises deployment requires an Enterprise contract rather than being available self-serve.
Pricing, plan by plan
DVC
Free- Open SourceFree
- Data versioning
- Pipeline management
- Experiment tracking
- DVC StudioFree
- Web UI
- Team collaboration
- Visualizations
Galileo
Free- FreeFree
- 5,000 traces/month
- Unlimited users
- Unlimited custom evaluations
- Pro$100/month
- 50,000 traces/month
- Standard role-based access control
- Advanced analytics and insights
- Enterprise$undefined/mo
- Unlimited trace capacity
- Custom rate limits
- Hosted, VPC, or on-prem deployment
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 Galileo if
- You need pre-built evaluations.
- You want to start without paying.
- You work on web, api.
- You also want ground truth capture.
Questions people ask
- Is DVC or Galileo better?
- Neither clearly leads. DVC starts at Free and Galileo at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, DVC or Galileo?
- DVC starts at Free and Galileo at Free.
- Does DVC or Galileo run on more platforms?
- DVC runs on Linux, Mac, Windows. Galileo runs on web, 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 Galileo is typically brought in for.
- What can DVC do that Galileo cannot?
- DVC covers Pointer-file versioning, Remote storage backends, Pipeline definitions, Stage caching. Galileo covers Pre-built evaluations, Ground truth capture, Luna models, Agent behavior analysis.
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.
Galileo: What does Galileo cost?
Galileo offers a free plan, a Pro plan at $100/month billed yearly (with a 33% annual discount), and a custom-priced Enterprise plan for unlimited trace capacity.
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.
Galileo: Is there a free plan, and what are its limits?
The Free plan includes 5,000 traces per month with unlimited users and unlimited custom evaluations, aimed at developers and small teams experimenting with GenAI.
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.
Galileo: How is usage metered?
Galileo's pricing scales based on the number of traces processed each month, with Free capped at 5,000, Pro at 50,000, and Enterprise offering unlimited trace capacity.
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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