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AWS SageMaker vs Keras

AWS SageMaker logo

AWS SageMaker

Software

Build, train, and deploy machine learning models at scale

From
Free
Rated
-
Full reviewWebsite
Keras logo

Keras

Software

Deep learning API for humans

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
  • They diverge on capability: AWS SageMaker covers Jupyter notebooks, Keras covers Sequential and Functional API.

Where they differ

Only the attributes on which AWS SageMaker and Keras actually diverge.

Attributes where AWS SageMaker and Keras differ
AttributeAWS SageMakerKeras
Pricing modelUnknownopen-source
PlatformsWebPython, Google Colab, Jupyter
Founded20062015

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 AWS SageMaker

  • Jupyter notebooks
  • Built-in algorithms
  • Automatic model tuning
  • One-click deployment
  • Model monitoring
  • S3
  • Lambda
  • Step Functions

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.

AWS SageMaker

  • Machine learning
  • Data analysis
  • Model training
  • Predictive analytics

Keras

  • Machine learning
  • Data analysis
  • Model training
  • Predictive analytics

Both are used for machine learning, data analysis, model training, predictive analytics, on those jobs the choice comes down to price and fit rather than capability.

Where each one falls short

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

AWS SageMaker

  • Vendor lock-in to AWS ecosystem makes migration to other platforms difficult
  • Opaque pricing can lead to unexpected expenses like forgotten EBS volume charges
  • Does not include native job scheduling, requiring Lambda or EventBridge integration

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

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

Keras

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

Which should you pick?

Choose AWS SageMaker if

  • You need jupyter notebooks.
  • You want to start without paying.
  • You also want built-in algorithms.
AWS SageMaker 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 AWS SageMaker or Keras better?
Neither clearly leads. AWS SageMaker 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, AWS SageMaker or Keras?
AWS SageMaker starts at Free and Keras at Free.
Does AWS SageMaker or Keras run on more platforms?
AWS SageMaker runs on Web. Keras runs on Python, Google Colab, Jupyter.
Can I use AWS SageMaker for free?
Both have a free tier, so you can try either at no cost before committing.
What is AWS SageMaker best used for?
AWS SageMaker is most often used for machine learning, data analysis, model training, predictive analytics.
What can AWS SageMaker do that Keras cannot?
AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning.

Answered from the vendors’ own pages

AWS SageMaker: What is AWS SageMaker used for?

AWS SageMaker is a machine learning service for building, training, and deploying ML models at scale. It provides tools for data preparation, model training, inference endpoints, and performance optimization.

Source
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
AWS SageMaker: How is AWS SageMaker priced?

SageMaker uses pay-as-you-go pricing with no upfront costs or long-term commitments. Pricing starts at $0.04 per hour for basic notebook instances and scales based on instance type. ML Savings Plans offer up to 64% off with hourly spend commitments.

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
AWS SageMaker: Does AWS SageMaker have a free tier?

Yes, the free tier includes 250 hours of notebook usage, 50 hours of training, and 125 hours of hosting on ml.t3.medium instances during the first two months.

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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