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Machine Learning · head to head

H2O.ai vs Teable

H2O.ai logo

H2O.ai

Machine Learning

AI Cloud for building and deploying AI applications

From
Free
Rated
-
Full reviewWebsite
Teable logo

Teable

Spreadsheets

Spreadsheet interface over real PostgreSQL tables, so the data stays queryable by anything that speaks SQL

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: H2O.ai java is always required to run H2O-3 even when working from R or Python, and only a 64-bit JRE or JDK is supported; 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: H2O.ai covers AutoML, Teable covers PostgreSQL native storage.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which H2O.ai and Teable actually diverge.

Attributes where H2O.ai and Teable differ
AttributeH2O.aiTeable
Pricing modelfreemiumOpen source, no licence fee
PlatformsWeb, CloudWeb, Linux
CategoryMachine LearningSpreadsheets
Founded2011Unknown

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 H2O.ai

  • AutoML
  • Distributed computing
  • Feature engineering
  • Model explainability
  • Time series forecasting
  • Spark
  • Hadoop
  • Python

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.

H2O.ai

  • Distributed in-memory machine learning over large datasetsnot Teable
  • Training and productionising models from R or Python against a shared H2O clusternot Teable

Teable

  • A team that has hit the record ceiling of a hosted spreadsheet database and does not want to move to raw SQLnot H2O.ai
  • Operational data that a business intelligence tool must also read directly, without an export or a sync jobnot H2O.ai
  • A regulated or data resident organisation that needs the underlying database inside its own infrastructurenot H2O.ai
  • An internal tool where a grid interface and a REST API over the same table are both requirednot H2O.ai

Where each one falls short

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

H2O.ai

  • Java is always required to run H2O-3 even when working from R or Python, and only a 64-bit JRE or JDK is supported
  • Supported Java versions stop at Java SE 17; newer versions only run by forcing an unsupported version flag and are guaranteed for experiments rather than production
  • H2O-3 only supports numpy below version 2, so a numpy 2 environment must be downgraded
  • Supported Python versions are limited to 3.7 through 3.11
  • The Flow web UI requires an internet browser and is the only graphical interface

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

H2O.ai

Free
  • H2O-3 Open SourceFree
    • Core algorithms
    • AutoML
    • Community support
  • Driverless AIFree
    • Automatic feature engineering
    • Model explainability
    • Enterprise support

Teable

Free
  • Self hosted open sourceFree
    • No licence fee
    • Unlimited rows subject to your PostgreSQL capacity
    • Docker deployment

Which should you pick?

Choose H2O.ai if

  • You need automl.
  • You want to start without paying.
  • You work on Web, Cloud.
  • You also want distributed computing.
H2O.ai review

Choose Teable if

  • You need postgresql native storage.
  • You want to start without paying.
  • You work on Web, Linux.
  • You also want multiple views.
Teable review

Questions people ask

Is H2O.ai or Teable better?
Neither clearly leads. H2O.ai 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, H2O.ai or Teable?
H2O.ai starts at Free and Teable at Free.
Does H2O.ai or Teable run on more platforms?
H2O.ai runs on Web, Cloud. Teable runs on Web, Linux.
Can I use H2O.ai for free?
Both have a free tier, so you can try either at no cost before committing.
What is H2O.ai best used for?
H2O.ai is most often used for distributed in-memory machine learning over large datasets, training and productionising models from r or python against a shared h2o cluster. Of those, distributed in-memory machine learning over large datasets and training and productionising models from r or python against a shared h2o cluster are not what Teable is typically brought in for.
What can H2O.ai do that Teable cannot?
H2O.ai covers AutoML, Distributed computing, Feature engineering, Model explainability. Teable covers PostgreSQL native storage, Multiple views, Linked records and rollups, Generated REST API.

Answered from the vendors’ own pages

H2O.ai: Is H2O open source and free?

Yes. H2O-3 OSS is free and Apache-licensed, designed for self-managed and experimental workflows. H2O.ai also offers enterprise cloud solutions with additional features.

Source
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.

H2O.ai: How many companies use H2O's open source platform?

Over 18,000 companies across Finance, Insurance, Healthcare, Retail, Telco, Sales, and Marketing use H2O's open-source machine learning platform.

Source
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.

Teable: Is self hosting genuinely free?

The licence is. The database server, backups and the engineer maintaining them are not.

Teable: Does it replace Airtable feature for feature?

No. Views and field types are close; automations, apps and the integration catalogue are not.

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.

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