7 Best Alternatives to SAS Viya in 2026

Why look beyond SAS Viya

SAS Viya provides an integrated platform for data science and AI lifecycle management, offering capabilities for advanced analytics, machine learning, and data visualization within a governed environment [source]. Its emphasis on enterprise-grade governance, compliance (including GDPR, HIPAA, ISO 27001), and a comprehensive suite of analytical tools makes it suitable for organizations with stringent regulatory requirements and complex analytical workloads [source].

However, specific organizational needs might lead teams to explore alternatives. Some may seek platforms with a stronger native integration into public cloud ecosystems (AWS, Azure, GCP) for seamless scalability and infrastructure management. Others might prioritize open-source frameworks for greater flexibility, community support, and avoidance of vendor lock-in, especially for custom deep learning development. Teams focusing on cutting-edge generative AI applications might look for platforms with direct access to large language models (LLMs) and specialized tools for prompt engineering and fine-tuning. Additionally, pricing models, developer experience with specific programming languages (e.g., Python, R), and the breadth of available pre-built models or services can influence the decision to consider alternative solutions.

Top alternatives ranked

1. 1. Databricks — Unified Analytics and AI Platform

   Databricks offers a unified platform for data engineering, machine learning, and data warehousing, built on Apache Spark. It provides a collaborative environment for data scientists, engineers, and analysts, supporting various workloads from ETL to ML model training and deployment [source]. The Lakehouse architecture aims to combine the benefits of data lakes and data warehouses, enabling structured and unstructured data processing with SQL, Python, R, Scala, and Java. Databricks emphasizes MLOps capabilities, including MLflow for experiment tracking, model registry, and deployment.

   Best for

   • Organizations seeking a unified platform for data engineering and machine learning.
   • Teams leveraging Apache Spark for large-scale data processing.
   • Collaborative data science workflows and MLOps.
   • Enterprises looking for a Lakehouse architecture.

   For more details, visit the Databricks profile page.

2. 2. Amazon SageMaker — Fully Managed Machine Learning Service

   Amazon SageMaker is a fully managed service from AWS designed to help developers and data scientists build, train, and deploy machine learning models at scale [source]. It provides a comprehensive set of tools and features across the entire ML lifecycle, including data labeling, data preparation, feature store, notebooks, integrated development environments (IDEs), model training, tuning, deployment, and monitoring. SageMaker supports popular deep learning frameworks like TensorFlow and PyTorch and offers built-in algorithms and pre-trained models. Its integration with other AWS services enables scalable and secure ML workflows.

   Best for

   • AWS-centric organizations seeking integrated ML capabilities.
   • Teams requiring a fully managed service for end-to-end ML workflows.
   • Large-scale model training and deployment.
   • Compliance with AWS security and governance standards.

   For more details, visit the Amazon SageMaker profile page.

3. 3. Azure OpenAI Service — Integrating OpenAI Models into Azure

   Azure OpenAI Service provides access to OpenAI's powerful language models, including GPT-3, GPT-4, Codex, and DALL-E models, within the security and enterprise capabilities of Microsoft Azure [source]. This service allows developers to integrate advanced generative AI capabilities into their applications while benefiting from Azure's infrastructure, compliance, and responsible AI practices. It supports fine-tuning models with custom data, deploying models in private network environments, and managing access control. The service is designed for enterprise-grade AI solutions, offering Python, Go, Java, JavaScript, and C# SDKs.

   Best for

   • Microsoft Azure customers leveraging OpenAI's generative models.
   • Enterprises requiring robust security and compliance for AI applications.
   • Developing applications with advanced natural language understanding and generation.
   • Building custom AI solutions with fine-tuned LLMs.

   For more details, visit the Azure OpenAI Service profile page.

4. 4. TensorFlow — Open-Source Machine Learning Framework

   TensorFlow is an open-source end-to-end platform for machine learning, developed by Google [source]. It provides a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications. TensorFlow is widely used for numerical computation and large-scale machine learning, particularly deep learning, supporting various tasks such as image recognition, natural language processing, and predictive analytics. It offers APIs for Python, JavaScript, Java, Go, Swift, and C++.

   Best for

   • Developers and researchers focused on deep learning model development.
   • Organizations requiring open-source flexibility and customizability.
   • Large-scale machine learning deployments across various platforms.
   • Projects benefiting from a large community and extensive resources.

   For more details, visit the TensorFlow profile page.

5. 5. OpenAI API — Access to Foundation Models

   The OpenAI API provides programmatic access to OpenAI's AI models, including GPT series for natural language processing, DALL-E for image generation, and Whisper for speech-to-text transcription [source]. It allows developers to integrate sophisticated AI capabilities into their own applications without needing extensive ML expertise. The API supports various use cases, from content generation and summarization to code generation and intelligent chatbots. It offers Python and Node.js SDKs and focuses on providing powerful foundation models for a broad range of AI tasks.

   Best for

   • Developers integrating advanced generative AI into applications.
   • Projects requiring state-of-the-art natural language and image generation.
   • Rapid prototyping and deployment of AI features.
   • Organizations that need flexible access to powerful foundation models.

   For more details, visit the OpenAI API profile page.

6. 6. DeepMind — AI Research and Solutions

   DeepMind, part of Google, is a leader in AI research and development, known for breakthroughs in areas like reinforcement learning, game-playing AI (AlphaGo), and protein folding (AlphaFold) [source]. While not a direct commercial platform in the same vein as SAS Viya, DeepMind's research often leads to technologies that are integrated into Google's broader AI offerings or inspire new approaches in the field. Organizations looking for cutting-edge AI solutions, particularly those involving complex problem-solving or foundational AI research, might find value in exploring the applications and insights emerging from DeepMind's work.

   Best for

   • Organizations interested in advanced AI research and foundational models.
   • Seeking solutions for highly complex and novel AI problems.
   • Leveraging state-of-the-art AI for scientific discovery and simulation.
   • Collaboration opportunities in cutting-edge AI development.

   For more details, visit the DeepMind profile page.

7. 7. ClearML — MLOps and Experiment Management

   ClearML is an open-source MLOps platform that provides tools for experiment tracking, MLOps automation, and model management [source]. It helps data scientists and ML engineers manage the entire machine learning lifecycle, from development to production. ClearML assists with experiment reproducibility, resource optimization, and seamless model deployment. It offers a suite of components for orchestrating ML pipelines, tracking datasets, and monitoring models in production, aiming to streamline MLOps workflows and enhance collaboration within ML teams.

   Best for

   • Teams prioritizing MLOps automation and experiment tracking.
   • Organizations seeking an open-source solution for ML lifecycle management.
   • Ensuring reproducibility and versioning of ML experiments.
   • Streamlining model deployment and monitoring in production.

   For more details, visit the ClearML profile page.

Side-by-side

How to pick

Choosing an alternative to SAS Viya depends on your organization's specific priorities, existing technology stack, and budget considerations. Consider these decision points:

• Cloud Integration vs. On-premises/Hybrid: If your organization is heavily invested in a specific public cloud (AWS or Azure), Amazon SageMaker or Azure OpenAI Service might offer deeper integration and leverage existing cloud infrastructure and expertise. Databricks also offers strong cloud-native capabilities across major cloud providers. If you require significant on-premises deployment flexibility or a hybrid approach, open-source frameworks like TensorFlow, coupled with an MLOps platform like ClearML, can be adapted.
• Focus on Generative AI and LLMs: For applications heavily reliant on cutting-edge natural language processing, image generation, or conversational AI, OpenAI API or Azure OpenAI Service provide direct access to powerful foundation models. These are distinct from traditional analytics platforms like SAS Viya, which focus more on predictive modeling and classical machine learning.
• Open-Source Flexibility vs. Managed Service: If your team values the flexibility, transparency, and community support of open-source technologies, TensorFlow provides a robust framework for deep learning development, and ClearML offers open-source MLOps capabilities. Managed services like Amazon SageMaker and Azure OpenAI Service reduce operational overhead but might offer less customization at the infrastructure level.
• Unified Data and ML Platform: If your primary need is a unified environment for data engineering, data warehousing, and machine learning, Databricks' Lakehouse architecture presents a compelling alternative, aiming to streamline the entire data-to-AI lifecycle.
• Existing Skillset and Ecosystem: Evaluate your team's proficiency with different programming languages (Python, R, Java) and existing vendor ecosystems. Migrating to a platform that aligns with your team's current skills can accelerate adoption and reduce training overhead. For example, teams proficient in Python might find TensorFlow, Databricks, or SageMaker more accessible.
• Governance and Compliance Requirements: For enterprises with strict regulatory requirements, vendor-managed solutions like Amazon SageMaker and Azure OpenAI Service often come with established compliance certifications and security features. While open-source solutions can be hardened for compliance, this typically requires more internal effort.
• Cost Model: SAS Viya operates on custom enterprise pricing. Alternatives offer various models, including usage-based pricing for cloud services (SageMaker, Azure OpenAI, OpenAI API), subscription models (Databricks), or being free for the core software (TensorFlow, ClearML, though infrastructure costs apply). Consider total cost of ownership, including infrastructure, support, and specialized personnel.