Keras vs TensorFlow

A comprehensive head-to-head comparison of two leading machine learning & data science solutions in 2026. Compare features, pricing, ratings, and more to find the right fit.

Quick Verdict

Choose Keras if you need Sequential and Functional API and prefer a free starting option. Choose TensorFlow if you prioritize Deep learning framework and want a free tier to start. TensorFlow has a higher user rating (4.7 vs 4.6).

Keras vs TensorFlow: At a Glance

CriteriaKerasTensorFlow
User Rating
4.6
4.7
PricingFreeFree
Pricing Modelopen-sourceopen-source
Free Plan
PlatformsLinux, Mac, WindowsLinux, Mac, Windows, Web, Mobile
CategoryMachine Learning & Data ScienceMachine Learning & Data Science
Founded19981998

Feature Comparison: Keras vs TensorFlow

FeatureKerasTensorFlow
Sequential and Functional API
Pre-built neural network layers
Model training and evaluation
Transfer learning
Model serialization
TensorFlow
JAX
PyTorch
Linux support
Mac support
Windows support
Deep learning framework
Neural network training
Model deployment
TensorBoard visualization
Distributed training
Keras
TensorFlow Lite
TensorFlow.js
Google Cloud
Web support
Mobile support

Keras vs TensorFlow: Pricing Breakdown

Keras Pricing

Model: open-source

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

TensorFlow Pricing

Model: open-source

Open SourceFree
  • Full framework access
  • Community support
  • All algorithms

Pros and Cons

Keras

Pros

  • Highly rated by users (4.6/5)
  • Free plan available to get started
  • Available on 3 platforms (Linux, Mac, Windows)
  • Rich feature set with 11+ capabilities
  • Strong Sequential and Functional API functionality
  • Strong Pre-built neural network layers functionality

Cons

  • May require time to learn advanced features

TensorFlow

Pros

  • Highly rated by users (4.7/5)
  • Free plan available to get started
  • Available on 5 platforms (Linux, Mac, Windows, Web, Mobile)
  • Rich feature set with 14+ capabilities
  • Strong Deep learning framework functionality
  • Strong Neural network training functionality

Cons

  • May require time to learn advanced features

Who Should Use Keras vs TensorFlow?

Choose Keras if you:

  • Need Sequential and Functional API
  • Want to start for free
  • Work primarily on Linux and Mac
  • Value Pre-built neural network layers
View Keras Details

Choose TensorFlow if you:

  • Need Deep learning framework
  • Want to start for free
  • Work primarily on Linux and Mac
  • Value Neural network training
View TensorFlow Details

Frequently Asked Questions: Keras vs TensorFlow

Is Keras better than TensorFlow?

It depends on your needs. Keras has a 4.6/5 user rating while TensorFlow has 4.7/5. Keras excels in Sequential and Functional API and Pre-built neural network layers, while TensorFlow stands out with Deep learning framework and Neural network training. Consider your budget (Free vs Free), platform needs, and specific feature requirements.

Which is cheaper, Keras or TensorFlow?

Keras offers a free plan and starts at Free. TensorFlow offers a free plan and starts at Free. Compare the specific plan features to determine the best value for your use case.

Can I use Keras and TensorFlow together?

While both are machine learning & data science tools, some teams use complementary software together. Check each product's API and integration capabilities for compatibility. However, most users find that one solution covers their core machine learning & data science needs.

What are the main differences between Keras and TensorFlow?

The key differences include: pricing model (open-source vs open-source), platform support (Linux, Mac, Windows vs Linux, Mac, Windows, Web, Mobile), and feature focus. Keras emphasizes Sequential and Functional API, Pre-built neural network layers, Model training and evaluation while TensorFlow focuses on Deep learning framework, Neural network training, Model deployment. User ratings differ slightly: 4.6 vs 4.7 out of 5.

Ready to choose?

Explore detailed reviews, user ratings, and pricing for both Keras and TensorFlow.