Machine Learning · head to head
DVC vs Teable
DVC
Machine Learning
Git-style versioning for data sets and models, with the files kept in object storage
- From
- Free
- Rated
- -
Teable
Spreadsheets
Spreadsheet interface over real PostgreSQL tables, so the data stays queryable by anything that speaks SQL
- From
- Free
- Rated
- -
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.; Teable the automation builder is far less capable than the established commercial alternatives, so multi step workflows usually end up in an external tool that must be paid for and maintained separately.
- They diverge on capability: DVC covers Pointer-file versioning, Teable covers PostgreSQL native storage.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which DVC and Teable 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 Teable
- PostgreSQL native storage
- Multiple views
- Linked records and rollups
- Generated REST API
- Real time collaboration
- Self hosting via Docker
- Field level permissions
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 Teable
- Keeping large training data out of Git while still having a repository that describes it preciselynot Teable
- Skipping expensive preprocessing stages that have not changed, when iterating on a later stage of a pipelinenot Teable
- Teams that need reproducibility but cannot get approval or budget to stand up a platform for itnot Teable
Teable
- A team that has hit the record ceiling of a hosted spreadsheet database and does not want to move to raw SQLnot DVC
- Operational data that a business intelligence tool must also read directly, without an export or a sync jobnot DVC
- A regulated or data resident organisation that needs the underlying database inside its own infrastructurenot DVC
- An internal tool where a grid interface and a REST API over the same table are both requirednot 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.
Teable
- The automation builder is far less capable than the established commercial alternatives, so multi step workflows usually end up in an external tool that must be paid for and maintained separately.
- Self hosting moves PostgreSQL backups, version upgrades, connection pooling and capacity planning onto your team, and the licence saving disappears if that time is costed honestly.
- The integration catalogue is small, so connecting to a common business system often means writing against the REST API rather than installing a connector.
- The project is young relative to the products it replaces, and interface and API changes still arrive at a pace that requires reading release notes before upgrading.
- Storing every table as a real PostgreSQL table means schema changes are real migrations, so a careless field type change on a large table can lock it far longer than a spreadsheet user would expect.
Pricing, plan by plan
DVC
Free- Open SourceFree
- Data versioning
- Pipeline management
- Experiment tracking
- DVC StudioFree
- Web UI
- Team collaboration
- Visualizations
Teable
Free- Self hosted open sourceFree
- No licence fee
- Unlimited rows subject to your PostgreSQL capacity
- Docker 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 Teable if
- You need postgresql native storage.
- You want to start without paying.
- You work on Web, Linux.
- You also want multiple views.
Questions people ask
- Is DVC or Teable better?
- Neither clearly leads. DVC starts at Free and Teable at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, DVC or Teable?
- DVC starts at Free and Teable at Free.
- Does DVC or Teable run on more platforms?
- DVC runs on Linux, Mac, Windows. Teable runs on Web, Linux.
- 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 Teable is typically brought in for.
- What can DVC do that Teable cannot?
- DVC covers Pointer-file versioning, Remote storage backends, Pipeline definitions, Stage caching. Teable covers PostgreSQL native storage, Multiple views, Linked records and rollups, Generated REST API.
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.
Teable: How many rows can it actually hold?
As many as your PostgreSQL instance can serve. There is no product imposed record limit on the self hosted edition, which is the main reason to choose it.
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.
Teable: Can I query the data with SQL directly?
Yes. Tables are real PostgreSQL tables, so any SQL client or reporting tool can read them.
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.
Teable: Is self hosting genuinely free?
The licence is. The database server, backups and the engineer maintaining them are not.
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.
Teable: Does it replace Airtable feature for feature?
No. Views and field types are close; automations, apps and the integration catalogue are not.
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.
Teable: What happens to my data if the project stops?
It remains in a standard PostgreSQL database that you control, which is a materially better exit than a proprietary export format.
Related pages
Other head to heads
- DVC vs Azure Machine Learning
- DVC vs AWS SageMaker
- DVC vs Google Vertex AI
- DVC vs DataRobot
- DVC vs MLflow
- DVC vs Pachyderm
- DVC vs Kubeflow
- DVC vs Weights & Biases
- DVC vs Seldon
- DVC vs ClearML
- DVC vs Comet ML
- DVC vs Dataiku
- DVC vs Neptune.ai
- DVC vs OpenAI API
- DVC vs Weka
- DVC vs BentoML
- DVC vs Semantic Kernel
- DVC vs BigQuery ML
- DVC vs Baserow
- DVC vs NocoDB
- DVC vs APITable
- DVC vs Mathesar
- DVC vs Rowy
- DVC vs SeaTable
- DVC vs Equals
- DVC vs Looker
- DVC vs Numbers
- DVC vs Cube Software
- DVC vs Quadratic
- DVC vs Tableau
- DVC vs Coefficient
- Teable vs Azure Machine Learning
- Teable vs AWS SageMaker
- Teable vs Google Vertex AI
- Teable vs DataRobot
- Teable vs MLflow
- Teable vs Pachyderm
- Teable vs Kubeflow
- Teable vs Weights & Biases
- Teable vs Seldon
- Teable vs ClearML
- Teable vs Comet ML
- Teable vs Dataiku
- Teable vs Neptune.ai
- Teable vs OpenAI API
- Teable vs Weka
- Teable vs BentoML
- Teable vs Semantic Kernel
- Teable vs BigQuery ML
- Teable vs Baserow
- Teable vs NocoDB
- Teable vs APITable
- Teable vs Mathesar
- Teable vs Rowy
- Teable vs SeaTable
- Teable vs Equals
- Teable vs Looker
- Teable vs Numbers
- Teable vs Cube Software
- Teable vs Quadratic
- Teable vs Tableau
- Teable vs Coefficient