Machine Learning · head to head
Keras vs Teable
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: Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs; 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: Keras covers Sequential and Functional API, Teable covers PostgreSQL native storage.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which Keras 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 Keras
- Sequential and Functional API
- Pre-built neural network layers
- Model training and evaluation
- Transfer learning
- Model serialization
- TensorFlow
- JAX
- PyTorch
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.
Keras
- Machine learningnot Teable
- Data analysisnot Teable
- Model trainingnot Teable
- Predictive analyticsnot Teable
Teable
- A team that has hit the record ceiling of a hosted spreadsheet database and does not want to move to raw SQLnot Keras
- Operational data that a business intelligence tool must also read directly, without an export or a sync jobnot Keras
- A regulated or data resident organisation that needs the underlying database inside its own infrastructurenot Keras
- An internal tool where a grid interface and a REST API over the same table are both requirednot Keras
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Keras
- Limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
- Error messages can be vague and unhelpful, making debugging challenging
- Smaller ecosystem and fewer pre-trained models than TensorFlow or PyTorch
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
Keras
Free- Open SourceFree
- High-level API
- Pre-built layers
- Model serialization
Teable
Free- Self hosted open sourceFree
- No licence fee
- Unlimited rows subject to your PostgreSQL capacity
- Docker deployment
Which should you pick?
Choose Keras if
- You need sequential and functional api.
- You want to start without paying.
- You work on Python, Google Colab, Jupyter.
- You also want pre-built neural network layers.
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 Keras or Teable better?
- Neither clearly leads. Keras 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, Keras or Teable?
- Keras starts at Free and Teable at Free.
- Does Keras or Teable run on more platforms?
- Keras runs on Python, Google Colab, Jupyter. Teable runs on Web, Linux.
- Can I use Keras for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Keras best used for?
- Keras is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Teable is typically brought in for.
- What can Keras do that Teable cannot?
- Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. Teable covers PostgreSQL native storage, Multiple views, Linked records and rollups, Generated REST API.
Answered from the vendors’ own pages
Keras: What is Keras?
Keras is a high-level deep learning API built on top of TensorFlow that simplifies building and training neural networks. Keras 3 supports multiple backends including TensorFlow, PyTorch, and JAX, making it backend-agnostic.
SourceTeable: 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.
Keras: What model architectures does Keras support?
Keras supports the Sequential model for linear stacks of layers, the Functional API for arbitrary graph architectures, and model subclassing for custom implementations. All approaches provide access to layers, optimizers, metrics, and callbacks.
SourceTeable: Can I query the data with SQL directly?
Yes. Tables are real PostgreSQL tables, so any SQL client or reporting tool can read them.
Keras: Can Keras models run on TPUs and GPUs?
Yes, Keras models can run on TPU Pods or large GPU clusters, be exported to run in browsers or on mobile devices, and be served via web APIs.
SourceTeable: Is self hosting genuinely free?
The licence is. The database server, backups and the engineer maintaining them are not.
Keras: Does Keras offer pre-trained models?
Yes, Keras provides pre-trained models through KerasHub and Keras Applications for common deep learning tasks like image classification, object detection, and NLP.
SourceTeable: Does it replace Airtable feature for feature?
No. Views and field types are close; automations, apps and the integration catalogue are not.
Keras: Who should use Keras?
Keras is ideal for beginners and rapid prototyping due to its simplicity and user-friendly interface. Advanced users and production deployments may benefit more from lower-level frameworks like TensorFlow or PyTorch for greater customization.
SourceTeable: 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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