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Software · head to head

Comet ML vs Keras

Comet ML logo

Comet ML

Software

Platform for tracking, comparing, and optimizing ML experiments

From
Free
Rated
-
Keras logo

Keras

Software

Deep learning API for humans

From
Free
Rated
-

The short version

  • Each has a real cost: Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention; Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
  • They diverge on capability: Comet ML covers Experiment tracking, Keras covers Sequential and Functional API.

Where they differ

Only the attributes on which Comet ML and Keras actually diverge.

Attributes where Comet ML and Keras differ
AttributeComet MLKeras
Pricing modelfreemiumopen-source
PlatformsWeb, Linux, Mac, WindowsPython, Google Colab, Jupyter
Founded20172015

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Unknown).

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 Comet ML

  • Experiment tracking
  • Code versioning
  • Model registry
  • Hyperparameter optimization
  • Production monitoring
  • Keras
  • scikit-learn
  • Hugging Face

Only in Keras

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

Both cover

  • PyTorch
  • TensorFlow
  • Linux support
  • Mac support
  • Windows support

What people use each for

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

Comet ML

  • Tracking machine learning experiments, metrics and model versionsnot Keras
  • Monitoring and evaluating LLM applications with tracingnot Keras

Keras

  • Machine learningnot Comet ML
  • Data analysisnot Comet ML
  • Model trainingnot Comet ML
  • Predictive analyticsnot Comet ML

Where each one falls short

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

Comet ML

  • The free cloud tier caps data at 25,000 spans a month with 60 day retention
  • Retention stays at 60 days even on the paid Pro plan, and extending it is a $29 per 100k spans add on
  • Overage on Pro is $5 per additional 100,000 spans
  • The free MLOps tier is a single user with 100 GB of storage and training hours governed by a fair usage policy
  • Pro MLOps is $19 per user per month and caps the team at 10 users

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

Comet ML

Free
  • FreeFree
    • 100 experiments
    • Basic features
    • Community support
  • Team$179/month
    • Unlimited experiments
    • Team collaboration
    • Priority support

Keras

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

Which should you pick?

Choose Comet ML if

  • You need experiment tracking.
  • You want to start without paying.
  • You work on Web, Linux, Mac, Windows.
  • You also want code versioning.

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.

Questions people ask

Is Comet ML or Keras better?
Neither clearly leads. Comet ML 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, Comet ML or Keras?
Comet ML starts at Free and Keras at Free.
Does Comet ML or Keras run on more platforms?
Comet ML runs on Web, Linux, Mac, Windows. Keras runs on Python, Google Colab, Jupyter.
Can I use Comet ML for free?
Both have a free tier, so you can try either at no cost before committing.
What is Comet ML best used for?
Comet ML is most often used for tracking machine learning experiments, metrics and model versions, monitoring and evaluating llm applications with tracing. Of those, tracking machine learning experiments, metrics and model versions and monitoring and evaluating llm applications with tracing are not what Keras is typically brought in for.
What can Comet ML do that Keras cannot?
Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. Both handle PyTorch, TensorFlow, Linux support, Mac support.

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
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
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
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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