Keras
Keras is a high-level deep learning API developed for humans that enables you to build, train, and deploy machine learning models with speed and simplicity across multiple frameworks.
SAS Viya
SAS Viya is a cloud-native data management and analytics platform that helps you build, deploy, and manage artificial intelligence and machine learning models to solve complex business problems efficiently.
Quick Comparison
| Feature | Keras | SAS Viya |
|---|---|---|
| Website | keras.io | sas.com |
| Pricing Model | Free | Custom |
| Starting Price | Free | Custom Pricing |
| FREE Trial | ✘ No free trial | ✓ 14 days free trial |
| Free Plan | ✓ Has free plan | ✘ No free plan |
| Product Demo | ✓ Request demo here | ✓ Request demo here |
| Deployment | ||
| Integrations | ||
| Target Users | ||
| Target Industries | ||
| Customer Count | 0 | 0 |
| Founded Year | 2015 | 1976 |
| Headquarters | Mountain View, USA | Cary, USA |
Overview
Keras
Keras is a deep learning framework designed to reduce your cognitive load when building complex neural networks. It acts as a high-level interface that runs on top of powerful backends like TensorFlow, JAX, or PyTorch, allowing you to switch between them seamlessly based on your project needs. You can move from an initial idea to a functional model faster because the syntax is consistent, readable, and minimizes the number of user actions required for common tasks.
Whether you are a researcher developing new deep learning layers or an engineer deploying models to production, Keras provides the tools to scale your work. You can run your code on CPUs, GPUs, or TPUs without changing your implementation. It is widely used across industries for tasks like image recognition, natural language processing, and forecasting, making it a versatile choice for teams that value developer experience and rapid iteration.
SAS Viya
SAS Viya is a cloud-native analytics platform designed to help you manage the entire data lifecycle in one place. You can move from raw data to production-ready AI models using a unified interface that supports both visual drag-and-drop tools and popular programming languages like Python and R. This flexibility allows your data scientists and business analysts to collaborate effectively on the same projects.
The platform handles massive datasets with ease thanks to its distributed, in-memory processing engine. You can deploy it on any cloud provider or on-premises environment to maintain control over your infrastructure. By automating repetitive data preparation and model tuning tasks, you can focus on uncovering insights that drive better business decisions and operational efficiency across your entire organization.
Overview
Keras Features
- Multi-Backend Support Choose the best engine for your task by running your Keras code on JAX, TensorFlow, or PyTorch without rewriting anything.
- Sequential Model API Create simple stacks of layers quickly for standard deep learning architectures where each layer has exactly one input and output.
- Functional API Build complex model topologies including multi-output models, directed acyclic graphs, and models with shared layers for advanced research.
- Keras Tuner Automate the search for the best hyperparameters in your deep learning models to achieve higher accuracy with less manual effort.
- Built-in Preprocessing Prepare your raw images, text, and structured data for training directly within your model pipeline for easier deployment.
- Mixed Precision Training Speed up your training times and reduce memory usage by using 16-bit floating-point types on modern GPU and TPU hardware.
SAS Viya Features
- Visual Data Preparation. Clean and transform your data using an intuitive visual interface that automatically generates code in the background.
- Automated Machine Learning. Build high-quality models faster by letting the system automatically handle feature engineering, algorithm selection, and hyperparameter tuning.
- Model Management. Register, track, and monitor all your analytical models in a central repository to ensure they perform reliably over time.
- Open Language Support. Write code in Python, R, or Lua while still taking advantage of the platform's powerful distributed processing capabilities.
- Real-Time Decisioning. Embed your analytical models directly into business processes to trigger automated actions and responses the moment data arrives.
- Natural Language Generation. Receive automated text explanations of your data visualizations so you can understand the 'why' behind your business trends.
Pricing Comparison
Keras Pricing
- Full API access
- Commercial usage allowed
- Community-led support
- Multi-backend compatibility
- Regular security updates
- Access to Keras Ecosystem
- Everything in Open Source, plus:
- Public GitHub issue tracking
- Extensive documentation
- Community discussion forums
- Open-source contributions
- Pre-trained model library
SAS Viya Pricing
Pros & Cons
Keras
Pros
- Extremely flat learning curve for beginners
- Excellent documentation and massive community support
- Consistent and simple API reduces coding errors
- Seamless integration with the TensorFlow ecosystem
Cons
- Debugging custom layers can be challenging
- Higher-level abstractions may limit low-level control
- Performance overhead compared to pure low-level code
SAS Viya
Pros
- Handles extremely large datasets with high performance
- Unified environment for both coding and visual users
- Strong governance features for regulated industries
- Excellent technical support and extensive documentation
- Seamless integration with major cloud providers
Cons
- Higher price point compared to open-source alternatives
- Significant learning curve for the full feature set
- Initial setup and configuration can be complex