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Machine Learning · head to head

DVC vs Poolside

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
Full reviewWebsite
Poolside logo

Poolside

AI

Open-weight foundation models built for agentic software engineering

From
$0.06/month
Free plan
No
Full reviewWebsite

The short version

  • Only DVC has a free tier, so it costs nothing to try first.
  • 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.; Poolside no self-serve public application; access is largely through enterprise sales or third-party model gateways
  • They diverge on capability: DVC covers Pointer-file versioning, Poolside covers Laguna S 2.1.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which DVC and Poolside actually diverge.

Attributes where DVC and Poolside differ
AttributeDVCPoolside
Starting priceFree$0.06/month
Pricing modelopen-sourceusage-based
Free tierYesNo
PlatformsLinux, Mac, Windowsweb, api, mac, windows, linux
CategoryMachine LearningAI
Founded20182023

Identical on both: 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 Poolside

  • Laguna S 2.1
  • Laguna XS 2.1
  • Desktop and CLI tools
  • Open-weight models
  • Third-party gateway access

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

Poolside

  • Enterprises deploying AI coding assistance across large developer organizationsnot DVC
  • Government and defense agencies needing on-premise or sovereign coding modelsnot DVC
  • Teams building custom coding agents on open-weight foundation modelsnot DVC
  • Organizations wanting on-device lightweight coding 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.

Poolside

  • No self-serve public application; access is largely through enterprise sales or third-party model gateways
  • Pricing for the flagship enterprise and government offering is not public and requires a sales conversation
  • Targets organizations with large (5,000+) developer headcounts, making it a poor fit for individuals or small teams
  • Documentation and product surface area are thin compared to consumer-facing coding assistants

Pricing, plan by plan

DVC

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

Poolside

$0.06/month
  • Laguna XS 2.1$undefined/month
    • $0.06 per 1M input tokens
    • $0.12 per 1M output tokens
    • Available via OpenRouter and Vercel AI Gateway
  • Laguna S 2.1$undefined/month
    • $0.10 per 1M input tokens
    • $0.20 per 1M output tokens
    • 1M token context window
  • Enterprise / Government$undefined/month
    • Custom contracts for large engineering organizations
    • Sales-quoted pricing
    • On-premise or dedicated deployment options

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.
DVC review

Choose Poolside if

  • You need laguna s 2.1.
  • You work on web, api, mac, windows, linux.
  • You also want laguna xs 2.1.
Poolside review

Questions people ask

Is DVC or Poolside better?
Neither clearly leads. DVC starts at Free and Poolside at $0.06/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DVC or Poolside?
DVC has a free tier; the other does not. Paid plans start at Free for DVC and $0.06/month for Poolside.
Does DVC or Poolside run on more platforms?
DVC runs on Linux, Mac, Windows. Poolside runs on web, api, mac, windows, linux.
Can I use DVC for free?
Yes. DVC has a free tier, so you can try it without paying. Poolside starts at $0.06/month.
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 Poolside is typically brought in for.
What can DVC do that Poolside cannot?
DVC covers Pointer-file versioning, Remote storage backends, Pipeline definitions, Stage caching. Poolside covers Laguna S 2.1, Laguna XS 2.1, Desktop and CLI tools, Open-weight models.

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.

Poolside: How is Poolside's Laguna model usage priced?

Laguna XS 2.1 is priced at $0.06 per 1M input tokens and $0.12 per 1M output tokens; Laguna S 2.1 is $0.10 per 1M input tokens and $0.20 per 1M output tokens.

Source
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.

Poolside: Where can I access Poolside's models?

Laguna models are available through third-party gateways OpenRouter and Vercel AI Gateway, as well as via Poolside's own desktop and CLI tools.

Source
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.

Poolside: Does Poolside offer enterprise or government pricing?

Yes, Poolside offers custom, sales-quoted contracts for large enterprises and government/defense customers, including on-premise or dedicated deployment options.

Source
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.

Poolside: How do I get started building with Poolside's models?

Poolside's documentation provides a quickstart guide for installing the `pool` CLI and running an agent task, alongside API documentation for custom integrations.

Source
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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