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AI · head to head

Arize AI vs DVC

Arize AI logo

Arize AI

AI

AI engineering platform for observability and evaluation of agents and LLM apps

From
Free
Free plan
Yes
Full reviewWebsite
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

The short version

  • Each has a real cost: Arize AI the free plan caps trace spans at 25,000 per month with only 15 days of retention.; 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: Arize AI covers End-to-end tracing, DVC covers Pointer-file versioning.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Arize AI and DVC actually diverge.

Attributes where Arize AI and DVC differ
AttributeArize AIDVC
Pricing modelfreemiumopen-source
Platformsweb, apiLinux, Mac, Windows
CategoryAIMachine Learning
FoundedUnknown2018

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 Arize AI

  • End-to-end tracing
  • Evaluation framework
  • Prompt testing and improvement
  • Alyx AI engineering agent
  • Custom dashboards
  • Data warehouse integrations

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.

Arize AI

  • Tracing and debugging production AI agentsnot DVC
  • Running large-scale evaluations across traces and sessionsnot DVC
  • Improving prompts before production rolloutnot DVC
  • Storing and querying GenAI traces alongside a data warehousenot DVC

DVC

  • Making a model reproducible by tying the exact data set version, code commit and parameters together in one Git historynot Arize AI
  • Keeping large training data out of Git while still having a repository that describes it preciselynot Arize AI
  • Skipping expensive preprocessing stages that have not changed, when iterating on a later stage of a pipelinenot Arize AI
  • Teams that need reproducibility but cannot get approval or budget to stand up a platform for itnot Arize AI

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Arize AI

  • The free plan caps trace spans at 25,000 per month with only 15 days of retention.
  • Self-hosted deployment and Data Fabric integration are restricted to the custom-priced Enterprise tier.
  • Pricing beyond the $50/month Pro plan requires a custom quote, making cost planning less transparent at scale.
  • Advanced compliance features like HIPAA are only available on Enterprise.

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

Arize AI

Free
  • AX FreeFree
    • 25k trace spans/month
    • 1 GB storage/month
    • 15-day retention
  • AX Pro$50/month
    • 50k trace spans/month
    • 10 GB storage/month
    • 30-day retention
  • AX Enterprise$undefined/mo
    • Custom trace spans, storage, and retention
    • SaaS or self-hosted deployment
    • Managed agents and Data Fabric

DVC

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

Which should you pick?

Choose Arize AI if

  • You need end-to-end tracing.
  • You want to start without paying.
  • You work on web, api.
  • You also want evaluation framework.
Arize AI review

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

Questions people ask

Is Arize AI or DVC better?
Neither clearly leads. Arize AI 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, Arize AI or DVC?
Arize AI starts at Free and DVC at Free.
Does Arize AI or DVC run on more platforms?
Arize AI runs on web, api. DVC runs on Linux, Mac, Windows.
Can I use Arize AI for free?
Both have a free tier, so you can try either at no cost before committing.
What is Arize AI best used for?
Arize AI is most often used for tracing and debugging production ai agents, running large-scale evaluations across traces and sessions, improving prompts before production rollout, storing and querying genai traces alongside a data warehouse. Of those, tracing and debugging production ai agents and running large-scale evaluations across traces and sessions are not what DVC is typically brought in for.
What can Arize AI do that DVC cannot?
Arize AI covers End-to-end tracing, Evaluation framework, Prompt testing and improvement, Alyx AI engineering agent. DVC covers Pointer-file versioning, Remote storage backends, Pipeline definitions, Stage caching.

Answered from the vendors’ own pages

Arize AI: What does Arize AX cost?

Arize AX offers a free plan, a Pro plan at $50/month, and a custom-priced Enterprise plan, with pricing based on trace spans, storage, and retention needs.

Source
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.

Arize AI: Is there a free plan, and what are its limits?

The AX Free plan includes 25,000 trace spans and 1 GB of storage per month with 15-day retention, plus unlimited users, evaluations, and experiments.

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.

Arize AI: What deployment options are available?

Free and Pro plans are SaaS-only, while Enterprise customers can choose SaaS or self-hosted deployment with custom SLAs.

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.

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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