Cybersecurity · head to head
Chainguard vs DVC

Chainguard
Cybersecurity
Secure-by-default open source software with hardened container images and libraries
- From
- Free
- Rated
- -

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: Chainguard containers Catalog at 19,000 USD/year expensive for teams under 10 people; 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.
- They diverge on capability: Chainguard covers Hardened container images, DVC covers Pointer-file versioning.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Chainguard and DVC actually diverge.
| Attribute | Chainguard | DVC |
|---|---|---|
| Pricing model | Licensing by artifact type and team size | open-source |
| Platforms | Cloud, Container, VM | Linux, Mac, Windows |
| Category | Cybersecurity | Machine Learning |
| Founded | Unknown | 2018 |
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 Chainguard
- Hardened container images
- CVE remediation SLA
- SLSA L2/L3 builds
- Sigstore signatures
- SBOM generation
- Language libraries
- VM images
- Artifact scanning
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
What people use each for
The jobs each tool is most often brought in to do.
Chainguard
- Deploying hardened container images with minimal attack surfacenot DVC
- Meeting supply chain security requirements for regulated industriesnot DVC
- Reducing CVE exposure with contractual remediation guaranteesnot DVC
- Building secure language packages with automatic backportsnot DVC
- Verifying artifact provenance with Sigstore signaturesnot DVC
DVC
- Making a model reproducible by tying the exact data set version, code commit and parameters together in one Git historynot Chainguard
- Keeping large training data out of Git while still having a repository that describes it preciselynot Chainguard
- Skipping expensive preprocessing stages that have not changed, when iterating on a later stage of a pipelinenot Chainguard
- Teams that need reproducibility but cannot get approval or budget to stand up a platform for itnot Chainguard
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Chainguard
- Containers Catalog at 19,000 USD/year expensive for teams under 10 people
- Per-image pricing for containers requires custom quotes with no transparency
- Free tier limited to 5 container images for testing
- Libraries pricing by ecosystem and developer count lacks transparent per-developer cost
- VM image catalog pricing opacity makes cost estimation difficult
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.
Pricing, plan by plan
Chainguard
Free- Free TierFree
- Five container images to test and deploy
- Containers Per-Image$undefined/custom
- Licensed by quantity and type
- Base images, application images, AI/ML images, FIPS variants
- Custom pricing per image
- Containers Catalog$19000/year
- For 10-person engineering teams
- 2,000+ container images
- Contractual CVE remediation SLAs
- Libraries Licensing$undefined/custom
- Licensed by ecosystem (Python, Java, JavaScript)
- Licensed by developer count
- Unlimited pulls with no metering
DVC
Free- Open SourceFree
- Data versioning
- Pipeline management
- Experiment tracking
- DVC StudioFree
- Web UI
- Team collaboration
- Visualizations
Which should you pick?
Choose Chainguard if
- You need hardened container images.
- You want to start without paying.
- You work on Cloud, Container, VM.
- You also want cve remediation sla.
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.
Questions people ask
- Is Chainguard or DVC better?
- Neither clearly leads. Chainguard starts at Free and DVC at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Chainguard or DVC?
- Chainguard starts at Free and DVC at Free.
- Does Chainguard or DVC run on more platforms?
- Chainguard runs on Cloud, Container, VM. DVC runs on Linux, Mac, Windows.
- Can I use Chainguard for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Chainguard best used for?
- Chainguard is most often used for deploying hardened container images with minimal attack surface, meeting supply chain security requirements for regulated industries, reducing cve exposure with contractual remediation guarantees, building secure language packages with automatic backports. Of those, deploying hardened container images with minimal attack surface and meeting supply chain security requirements for regulated industries are not what DVC is typically brought in for.
- What can Chainguard do that DVC cannot?
- Chainguard covers Hardened container images, CVE remediation SLA, SLSA L2/L3 builds, Sigstore signatures. DVC covers Pointer-file versioning, Remote storage backends, Pipeline definitions, Stage caching.
Answered from the vendors’ own pages
Chainguard: How much is the Chainguard Containers Catalog?
The Containers Catalog is 19,000 USD per year for 10-person engineering teams, providing access to 2,000+ hardened container images.
SourceDVC: 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.
Chainguard: What SLAs does Chainguard offer?
Chainguard provides contractual CVE remediation SLAs: 7 days for critical vulnerabilities, 14 days for high/medium/low severity, all with priority support.
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.
Chainguard: Can I try Chainguard before purchasing?
Yes. The free tier includes five container images for testing and deployment, allowing hands-on evaluation.
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.
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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