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Machine Learning · head to head

Keras vs Mode

Keras logo

Keras

Machine Learning

Deep learning API for humans

From
Free
Rated
-
Full reviewWebsite
Mode logo

Mode

Business Intelligence

Collaborative analytics for data teams

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs; Mode free tier limited to 4GB RAM and 1 CPU for SQL notebooks, insufficient for large datasets
  • They diverge on capability: Keras covers Sequential and Functional API, Mode covers SQL Editor.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Keras and Mode actually diverge.

Attributes where Keras and Mode differ
AttributeKerasMode
Pricing modelopen-sourcesubscription
PlatformsPython, Google Colab, JupyterWeb
CategoryMachine LearningBusiness Intelligence
Founded20152013

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 Mode

  • SQL Editor
  • Python/R Notebooks
  • Interactive Reports
  • Version Control
  • Scheduling
  • Snowflake
  • Redshift
  • BigQuery

What people use each for

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

Keras

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

Mode

  • Self-service analyticsnot Keras
  • Data explorationnot Keras
  • Ad-hoc reportingnot Keras
  • Collaborative analysisnot Keras
  • Embedded analyticsnot 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

Mode

  • Free tier limited to 4GB RAM and 1 CPU for SQL notebooks, insufficient for large datasets
  • Requires SQL knowledge for most analysis tasks, creating dependency on technical resources
  • Paid plan pricing not publicly listed; requires sales consultation
  • Recently acquired by ThoughtSpot in 2026, creating product direction uncertainty
  • Limited customization options for visual aspects and embedded analytics

Pricing, plan by plan

Keras

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

Mode

Free
  • FreeFree
    • SQL Editor
    • Python/R Notebooks
    • Basic Charts
  • Business$65/month
    • Advanced Visualizations
    • Collaboration
    • Integrations

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.
Keras review

Choose Mode if

  • You need sql editor.
  • You want to start without paying.
  • You also want python/r notebooks.
Mode review

Questions people ask

Is Keras or Mode better?
Neither clearly leads. Keras starts at Free and Mode at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Keras or Mode?
Keras starts at Free and Mode at Free.
Does Keras or Mode run on more platforms?
Keras runs on Python, Google Colab, Jupyter. Mode runs on Web.
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 Mode is typically brought in for.
What can Keras do that Mode cannot?
Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. Mode covers SQL Editor, Python/R Notebooks, Interactive Reports, Version Control.

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.

Source
Mode: What languages does Mode support for analysis?

Mode notebooks support SQL, Python (3.11 with pandas, NumPy, scikit-learn, matplotlib), and R (4.2.0 with ggplot2, dplyr, tidyr). Both Python and R allow additional library installation at runtime.

Source
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
Mode: Can I integrate Mode notebook results into reports?

Yes. Mode allows adding notebook cell results directly to reports, with synchronized scheduling so reports re-run to keep data current.

Source
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
Mode: Does Mode support collaborative analysis?

Yes. Mode notebooks provide moveable code blocks and markdown cells enabling exploratory analysis and team collaboration on data queries and visualizations.

Source
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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