7 Best Alternatives to SAS AI in 2026

Why look beyond SAS AI

SAS AI has established itself as a significant platform for enterprise analytics and machine learning, particularly within sectors requiring stringent regulatory compliance and robust statistical capabilities [1]. Its core offerings, such as SAS Viya and SAS Visual Data Mining and Machine Learning, provide integrated environments for data preparation, model development, and deployment. However, organizations may explore alternatives for several reasons.

One common driver is the desire for greater flexibility in cloud deployment models, especially for those prioritizing cloud-native architectures or multi-cloud strategies. While SAS supports hybrid cloud, some alternatives offer deeper integration with specific public cloud ecosystems. Another factor is the increasing demand for open-source toolchain compatibility. While SAS offers Python and R SDKs, some platforms are built around open-source frameworks from the ground up, potentially offering more direct access to community-driven innovation. Furthermore, the rise of advanced generative AI capabilities has led some enterprises to seek platforms specialized in large language models (LLMs) and their deployment, which may not be the primary focus of traditional analytical platforms like SAS. Finally, pricing structures and the need for more granular control over infrastructure costs can lead organizations to evaluate alternatives with different commercial models.

Top alternatives ranked

1. 1. Databricks — Unified platform for data, analytics, and AI

   Databricks offers a Lakehouse Platform designed to unify data warehousing and data lakes, enabling data engineering, machine learning, and data science on a single platform [2]. It is built on Apache Spark and provides an environment for collaborative data workloads, supporting Python, SQL, R, and Scala. For organizations accustomed to SAS's integrated workflow, Databricks presents an alternative that emphasizes scalability, open-source compatibility, and a columnar format that enables high-performance analytics. Its strengths lie in large-scale data processing, MLOps, and the ability to handle diverse data types, making it suitable for enterprises looking to modernize their data and AI infrastructure with a cloud-native approach.

   Best for: Large-scale data engineering, collaborative machine learning, cloud-native data and AI platforms, MLOps.

   Read more about Databricks

2. 2. Google Vertex AI — Unified MLOps platform for building and deploying ML models

   Google Vertex AI is a managed machine learning platform that unifies the ML workflow from data preparation to model deployment and monitoring [3]. It provides access to Google's advanced AI models, including generative AI capabilities, and supports custom model training with various open-source frameworks. Compared to SAS, Vertex AI offers a cloud-native environment deeply integrated with Google Cloud services, providing scalability and flexibility for MLOps. Its focus on end-to-end ML lifecycle management and access to Google's specialized hardware (TPUs) makes it a strong contender for organizations prioritizing cloud infrastructure, advanced AI research, and rapid deployment of machine learning applications.

   Best for: End-to-end ML lifecycle management, integrating generative AI models, custom model training and deployment, large-scale data processing on Google Cloud.

   Read more about Google Vertex AI

3. 3. H2O.ai — Open-source and commercial AI platform for automated machine learning

   H2O.ai provides open-source and commercial platforms for automated machine learning, including H2O-3 and H2O Driverless AI [4]. H2O Driverless AI automates feature engineering, model selection, and hyperparameter tuning, aiming to accelerate the development of high-performing machine learning models. For SAS users, H2O.ai offers an alternative focused on enabling data scientists to build and deploy models faster, often with less manual effort. Its emphasis on explainable AI (XAI) and model interpretability aligns with regulatory requirements common in industries served by SAS. The platform supports various data sources and integrates with popular data science tools, providing a bridge between traditional analytics and modern ML practices.

   Best for: Automated machine learning (AutoML), explainable AI, rapid model development and deployment, hybrid cloud deployments.

   Read more about H2O.ai

4. 4. DataRobot — Enterprise AI platform for automated machine learning and MLOps

   DataRobot offers an enterprise AI platform that automates the end-to-end machine learning lifecycle, from data preparation and model building to deployment and monitoring [5]. Its platform is designed for both data scientists and citizen data scientists, providing tools for automated machine learning (AutoML), MLOps, and AI governance. For organizations evaluating SAS alternatives, DataRobot provides a strong option for accelerating AI initiatives, particularly in environments where speed to insight and model operationalization are critical. It emphasizes ease of use, scalability, and the ability to integrate AI into existing business processes, making it a suitable choice for enterprises seeking to operationalize machine learning across various departments.

   Best for: Automated machine learning (AutoML), MLOps, AI governance, democratizing AI for business users.

   Read more about DataRobot

5. 5. Azure OpenAI Service — Integrating OpenAI models into enterprise applications

   Azure OpenAI Service provides access to OpenAI's powerful language models, including GPT-4, GPT-3.5 Turbo, and embeddings, combined with the enterprise-grade security and capabilities of Azure [6]. This service allows organizations to build generative AI applications with features like virtual networks and private endpoints for enhanced data privacy. While SAS excels in traditional statistical modeling, Azure OpenAI Service addresses the growing demand for large language models within an enterprise context. It appeals to organizations that are heavily invested in the Microsoft ecosystem and require secure, scalable deployment of generative AI for use cases such as content generation, summarization, and conversational AI.

   Best for: Integrating OpenAI models into enterprise applications, building secure AI solutions within Azure, generative AI use cases, leveraging existing Microsoft cloud investments.

   Read more about Azure OpenAI Service

6. 6. OpenAI Enterprise — Custom large language model deployments for businesses

   OpenAI Enterprise offers direct access to OpenAI's most advanced models, including GPT-4, with enhanced performance, dedicated infrastructure, and extended context windows [7]. It provides enterprise-grade data privacy and security, allowing organizations to fine-tune models on their private data. While SAS focuses on comprehensive analytics suites, OpenAI Enterprise caters specifically to organizations requiring custom, large-scale generative AI deployments. It is suitable for businesses that need to integrate advanced natural language processing capabilities into their core products or internal operations, with a focus on data control and performance at scale.

   Best for: Large-scale enterprise AI deployments, custom model training and fine-tuning, enhanced data privacy and security needs, high-volume API access for generative AI.

   Read more about OpenAI Enterprise

7. 7. Salesforce Einstein — AI embedded within the Salesforce platform

   Salesforce Einstein is an AI layer embedded across the Salesforce platform, providing predictive and generative AI capabilities to enhance CRM workflows [8]. It offers features like sales forecasting, service automation, marketing personalization, and generative AI assistants. For organizations using Salesforce extensively, Einstein provides a native AI solution that leverages existing customer data within the CRM ecosystem. While SAS is a standalone analytics platform, Einstein's value lies in its seamless integration with sales, service, and marketing operations, offering a direct path to AI-driven insights and automation within the customer relationship management context.

   Best for: Automating sales workflows, personalizing customer service, predictive analytics in CRM, integrating AI directly into Salesforce operations.

   Read more about Salesforce Einstein

Side-by-side

How to pick

Choosing an alternative to SAS AI involves evaluating your organizational priorities, existing infrastructure, and specific AI/ML use cases. Consider the following decision points:

• Cloud Strategy and Infrastructure Alignment:

  • If your organization is deeply invested in a specific public cloud (e.g., Google Cloud, Azure), platforms like Google Vertex AI or Azure OpenAI Service offer native integrations, leveraging your existing cloud investments and expertise.
  • For organizations seeking a unified platform across data and AI, especially those with large data volumes and a preference for open-source foundations, Databricks provides a compelling Lakehouse architecture approach.
  • If you require hybrid or on-premises deployment flexibility similar to SAS, consider H2O.ai or DataRobot, which offer deployment options beyond pure cloud-native.

• Generative AI and Large Language Models (LLMs):

  • If your primary driver is to build and deploy advanced generative AI applications, such as custom chatbots, content generation, or sophisticated summarization, OpenAI Enterprise or Azure OpenAI Service are highly specialized for these capabilities, offering direct access to leading LLMs.
  • Google Vertex AI also offers strong generative AI capabilities, particularly if you are within the Google Cloud ecosystem and want to integrate with their broader AI services.

• Automation and MLOps Maturity:

  • For accelerating model development and operationalization, particularly if you aim to democratize AI across your organization, DataRobot and H2O.ai excel with their automated machine learning (AutoML) and MLOps features. These are designed to streamline the entire ML lifecycle.
  • Google Vertex AI is also a strong contender for comprehensive MLOps, providing a managed platform for model training, deployment, and monitoring.

• Domain-Specific AI and Integration:

  • If your organization is heavily reliant on Salesforce and wants to embed AI directly into CRM workflows (sales, service, marketing), Salesforce Einstein is the most direct and integrated solution, leveraging your existing customer data.

• Open Source vs. Commercial Ecosystem:

  • If your team leans towards open-source technologies and frameworks, Databricks (built on Apache Spark) and H2O.ai (with its open-source H2O-3 platform) offer environments that embrace these principles.
  • For highly regulated industries or those preferring comprehensive commercial support and enterprise-grade features, platforms like DataRobot, Google Vertex AI, and the enterprise offerings from OpenAI or Azure OpenAI Service provide robust commercial solutions.

By carefully mapping these considerations to the strengths of each alternative, organizations can identify the platform that best aligns with their technical requirements, strategic goals, and operational model.