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Business Intelligence · head to head

Fabi vs Keras

Fabi logo

Fabi

Business Intelligence

AI notebooks combining SQL, Python and no-code for small data teams

From
Free
Rated
-
Full reviewWebsite
Keras logo

Keras

Machine Learning

Deep learning API for humans

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Fabi fabi is an early-stage company with a small team, so the durability risk is real and there is no obvious migration path for Smartbooks if it stops trading.; Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
  • They diverge on capability: Fabi covers Smartbooks, Keras covers Sequential and Functional API.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Fabi and Keras actually diverge.

Attributes where Fabi and Keras differ
AttributeFabiKeras
Pricing modelPer user per monthopen-source
PlatformsWebPython, Google Colab, Jupyter
CategoryBusiness IntelligenceMachine Learning
FoundedUnknown2015

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 Fabi

  • Smartbooks
  • Smart Reports
  • AI analysis
  • Scheduled runs
  • Database connectors
  • Viewer seats

Only in Keras

  • Sequential and Functional API
  • Pre-built neural network layers
  • Model training and evaluation
  • Transfer learning
  • Model serialization
  • TensorFlow
  • JAX
  • PyTorch

What people use each for

The jobs each tool is most often brought in to do.

Fabi

  • A single analyst at a startup fielding ad hoc questions faster than dashboards can be built for themnot Keras
  • An operations team that needs Python for a one-off analysis but has to hand the result to non-technical colleaguesnot Keras
  • Replacing a set of scheduled Jupyter notebooks that nobody outside the data team can read or rerunnot Keras
  • Exploratory work against a warehouse where building a semantic model first would cost more than the answer is worthnot Keras

Keras

  • Machine learningnot Fabi
  • Data analysisnot Fabi
  • Model trainingnot Fabi
  • Predictive analyticsnot Fabi

Where each one falls short

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

Fabi

  • Fabi is an early-stage company with a small team, so the durability risk is real and there is no obvious migration path for Smartbooks if it stops trading.
  • There is no meaningful governance layer: no data catalogue, no certified metric definitions and limited lineage, so it does not scale to an organisation that needs a single agreed number.
  • The free and Builder tiers cap AI requests, and the daily cap on Starter is reached quickly during genuine exploratory work.
  • Connector counts are limited by tier, so the Builder plan at 39 dollars connects to exactly one data source, which is rarely enough in practice.
  • It overlaps heavily with what Snowflake, Databricks and Hex now ship natively, so a company already paying for one of those is buying a fourth notebook interface.

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

Pricing, plan by plan

Fabi

Free
  • StarterFree
    • One builder seat
    • Ten dashboard viewers
    • Five Smartbooks
  • Builder$39/month
    • Twenty-five Smart Report viewers
    • Ten Smartbooks with workflows
    • One data connector
  • Team$199/month
    • Four builder seats, extra seats at 39 USD
    • Fifty viewers
    • Premium connector
  • Enterprise$undefined/year
    • Unlimited builder seats
    • Full connector access
    • Custom security review

Keras

Free
  • Open SourceFree
    • High-level API
    • Pre-built layers
    • Model serialization

Which should you pick?

Choose Fabi if

  • You need smartbooks.
  • You want to start without paying.
  • You also want smart reports.
Fabi review

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.
Keras review

Questions people ask

Is Fabi or Keras better?
Neither clearly leads. Fabi starts at Free and Keras at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Fabi or Keras?
Fabi starts at Free and Keras at Free.
Does Fabi or Keras run on more platforms?
Fabi runs on Web. Keras runs on Python, Google Colab, Jupyter.
Can I use Fabi for free?
Both have a free tier, so you can try either at no cost before committing.
What is Fabi best used for?
Fabi is most often used for a single analyst at a startup fielding ad hoc questions faster than dashboards can be built for them, an operations team that needs python for a one-off analysis but has to hand the result to non-technical colleagues, replacing a set of scheduled jupyter notebooks that nobody outside the data team can read or rerun, exploratory work against a warehouse where building a semantic model first would cost more than the answer is worth. Of those, a single analyst at a startup fielding ad hoc questions faster than dashboards can be built for them and an operations team that needs python for a one-off analysis but has to hand the result to non-technical colleagues are not what Keras is typically brought in for.
What can Fabi do that Keras cannot?
Fabi covers Smartbooks, Smart Reports, AI analysis, Scheduled runs. Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning.

Answered from the vendors’ own pages

Fabi: Is there a free plan?

Yes, a Starter tier with one builder, ten viewers, five Smartbooks and ten AI requests a day.

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.

Source
Fabi: Do viewers cost money?

Viewers are bundled by tier rather than charged individually; builders are the priced seat.

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.

Source
Fabi: Can I run arbitrary Python?

Yes, Smartbooks include Python cells alongside SQL and no-code steps.

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.

Source
Fabi: How many data connectors does the entry paid plan include?

One. Additional and premium connectors come with the Team tier.

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.

Source
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.

Source
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