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Keras vs Seldon

Keras logo

Keras

Software

Deep learning API for humans

From
Free
Rated
-
Full reviewWebsite
Seldon logo

Seldon

Software

Deploy, scale, and monitor machine learning models

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; Seldon production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
  • They diverge on capability: Keras covers Sequential and Functional API, Seldon covers Model serving.

Where they differ

Only the attributes on which Keras and Seldon actually diverge.

Attributes where Keras and Seldon differ
AttributeKerasSeldon
Pricing modelopen-sourcefreemium
PlatformsPython, Google Colab, JupyterLinux
Founded20152014

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 Keras

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

Only in Seldon

  • Model serving
  • A/B testing
  • Canary deployments
  • Outlier detection
  • Model explainability
  • Kubernetes
  • Istio
  • Prometheus

Both cover

  • Linux support

What people use each for

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

Keras

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

Seldon

  • Serving and routing machine learning models on Kubernetesnot Keras
  • Building multi-step inference pipelines with A/B tests and explainersnot 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

Seldon

  • Production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
  • The documented components carry both minimum and maximum supported versions, so newer Kubernetes and dependency versions are not automatically supported
  • Dataflow Pipelines need an additional component that the docs recommend avoiding installing when pipelines are not used
  • The Docker Compose install is offered as a lightweight alternative for environments without Kubernetes rather than as a production path

Pricing, plan by plan

Keras

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

Seldon

Free
  • Seldon CoreFree
    • Open source
    • Kubernetes deployment
    • Model serving
  • Seldon DeployFree
    • Enterprise features
    • GUI
    • Monitoring

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 Seldon if

  • You need model serving.
  • You want to start without paying.
  • You work on Linux.
  • You also want a/b testing.
Seldon review

Questions people ask

Is Keras or Seldon better?
Neither clearly leads. Keras starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Keras or Seldon?
Keras starts at Free and Seldon at Free.
Does Keras or Seldon run on more platforms?
Keras runs on Python, Google Colab, Jupyter. Seldon runs on 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 Seldon is typically brought in for.
What can Keras do that Seldon cannot?
Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection. Both handle Linux 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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