7 Best Alternatives to Salesforce Einstein in 2026

Why look beyond Salesforce Einstein

Salesforce Einstein is designed as an embedded AI layer within the Salesforce CRM platform, offering capabilities like predictive analytics, automation, and conversational AI tailored for sales, service, and marketing workflows Salesforce Einstein product documentation. While its deep integration with Salesforce data and processes provides a coherent experience for existing Salesforce users, organizations may seek alternatives for several reasons. These include a need for more generalized AI/ML development platforms that support a wider range of data sources beyond CRM, greater control over model training and deployment lifecycles, or a preference for multi-cloud or hybrid-cloud deployments. Furthermore, some enterprises may require AI solutions that integrate seamlessly with non-Salesforce enterprise applications, offer different pricing structures, or provide specialized generative AI capabilities not natively covered by Einstein's core offerings. Evaluating these factors helps determine if an alternative platform aligns more closely with an organization's specific AI strategy and infrastructure.

Top alternatives ranked

1. 1. Google Vertex AI — Unified MLOps platform for custom and generative AI

   Google Vertex AI is a managed machine learning platform that unifies the MLOps lifecycle, enabling developers and data scientists to build, deploy, and scale ML models Google Vertex AI documentation. It provides access to Google's foundational models, including Gemini, and offers tools for custom model training, hyperparameter tuning, and model monitoring. Vertex AI supports a broad range of data types and integrates with other Google Cloud services, making it suitable for end-to-end ML workflows from data ingestion to prediction serving. Unlike Salesforce Einstein, which is primarily focused on CRM-specific AI, Vertex AI provides a general-purpose platform that can be applied across various business domains and data sources, allowing for greater flexibility in AI development and deployment.

   Best for: End-to-end ML lifecycle management, integrating generative AI models, custom model training and deployment, large-scale data processing, and multi-cloud or hybrid-cloud strategies.

   Learn more about Google Vertex AI

2. 2. Azure OpenAI Service — Secure and governed access to OpenAI models within Azure

   Azure OpenAI Service provides organizations with access to OpenAI's powerful language models, including GPT-4, GPT-3.5, and DALL-E 2, within the security and enterprise-grade capabilities of Microsoft Azure Azure OpenAI Service overview. This service allows enterprises to build custom applications using generative AI models while leveraging Azure's compliance, data privacy, and networking features. Compared to Salesforce Einstein, which offers pre-built AI features within a CRM context, Azure OpenAI Service gives developers direct API access to foundational models for building custom AI solutions across a wider array of applications, from content generation to intelligent virtual agents. It is particularly well-suited for organizations that already operate within the Azure ecosystem and require fine-grained control over model deployment and data management.

   Best for: Integrating OpenAI models into enterprise applications, building secure AI solutions within Azure, leveraging existing Azure infrastructure, and developing custom generative AI experiences with robust governance.

   Learn more about Azure OpenAI Service

3. 3. Microsoft Dynamics 365 AI — AI-powered insights and automation across Dynamics 365 applications

   Microsoft Dynamics 365 AI extends the capabilities of Microsoft's business applications (CRM and ERP) with intelligent features for sales, service, marketing, finance, and operations Microsoft Dynamics 365 AI capabilities. This suite includes AI-driven insights for sales forecasting, customer service recommendations, fraud detection, and supply chain optimization. Similar to Salesforce Einstein, Dynamics 365 AI is embedded within a comprehensive business application suite, offering pre-built AI functionalities that leverage the platform's data. It serves as a direct competitor to Einstein for organizations seeking an integrated AI solution within their CRM and ERP systems, particularly those already invested in the Microsoft ecosystem. It offers a comparable level of automation and predictive analytics tailored to specific business processes.

   Best for: Enhancing existing Microsoft Dynamics 365 deployments, integrating AI into ERP and CRM processes, automating business operations, and deriving insights from enterprise data within the Microsoft ecosystem.

   Learn more about Microsoft Dynamics 365 AI

4. 4. Databricks Mosaic AI — Unified platform for building, deploying, and managing generative AI models

   Databricks Mosaic AI is an offering within the Databricks Lakehouse Platform designed to facilitate the development and deployment of generative AI applications, including large language models (LLMs) Databricks Machine Learning documentation. It provides tools for data preparation, model training (including fine-tuning of open-source and proprietary LLMs), prompt engineering, and model serving. Mosaic AI leverages the scalable data processing capabilities of Databricks, making it suitable for organizations with large datasets and complex AI requirements. Unlike Salesforce Einstein's narrowly focused CRM AI, Databricks Mosaic AI offers a more open and flexible environment for building custom generative AI, allowing enterprises to maintain ownership and control over their models and data throughout the entire lifecycle.

   Best for: Building and deploying production-ready generative AI applications, fine-tuning large language models, managing the full ML lifecycle on a unified data platform, and developing custom AI solutions with large datasets.

   Learn more about Databricks Mosaic AI

5. 5. Microsoft Copilot Studio — Low-code platform for creating custom copilots and generative AI experiences

   Microsoft Copilot Studio is a low-code platform that enables users to build custom generative AI experiences, integrate AI into Microsoft 365 and the Power Platform, and automate business processes Microsoft Copilot Studio documentation. It allows for the creation of custom copilots (AI assistants) that can interact with various data sources and applications, extending the capabilities of Microsoft's existing Copilot offerings. While Salesforce Einstein focuses on embedded AI within its CRM suite, Copilot Studio provides a broader framework for developing AI agents that can operate across different enterprise applications and workflows, often without extensive coding. This makes it a suitable alternative for organizations looking to empower citizen developers to create custom AI solutions that integrate deeply with their Microsoft environment.

   Best for: Building custom generative AI experiences, integrating AI into Microsoft 365 and Power Platform, automating business processes with AI, and creating intelligent virtual agents with a low-code approach.

   Learn more about Microsoft Copilot Studio

6. 6. Anthropic Enterprise (Claude for Work) — Secure and responsible large language models for business

   Anthropic Enterprise, featuring Claude for Work, provides secure and scalable access to Anthropic's advanced large language models for enterprise clients Anthropic API documentation. This offering focuses on robust safety and interpretability features, making it suitable for sensitive business applications requiring reliable and responsible AI. Unlike Salesforce Einstein, which embeds AI within a CRM, Anthropic provides foundational generative AI models that can be integrated into various enterprise workflows, such as internal knowledge management, content generation, and coding assistance. Its emphasis on responsible AI development and enterprise-grade security distinguishes it for organizations prioritizing ethical AI implementations and data protection when deploying large language models.

   Best for: Secure enterprise-grade AI, large language model deployment, internal knowledge management, coding assistance, and applications requiring high levels of AI safety and responsibility.

   Learn more about Anthropic Enterprise

7. 7. OpenAI Enterprise — Custom, scalable, and secure AI for large organizations

   OpenAI Enterprise offers large organizations dedicated instances of OpenAI's models, including GPT-4, with enhanced data privacy, security, and performance guarantees OpenAI Platform overview. This service provides higher rate limits, extended context windows, and the ability to fine-tune models with proprietary data, all within an enterprise-grade framework. While Salesforce Einstein offers pre-packaged AI features for CRM, OpenAI Enterprise provides foundational AI models that can be customized and integrated into a vast array of enterprise applications, from advanced data analysis to intelligent automation and customer interactions. It is designed for companies that need direct access to state-of-the-art generative AI technology and require significant customization and control over their AI deployments.

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

   Learn more about OpenAI Enterprise

Side-by-side

How to pick

Selecting an alternative to Salesforce Einstein involves evaluating an organization's specific AI objectives, existing technology stack, and desired level of control over AI development. Consider the following decision-tree style guidance:

• Do you primarily need AI capabilities embedded within a comprehensive business application suite (CRM/ERP)?
  • If yes, and you are already invested in the Microsoft ecosystem, Microsoft Dynamics 365 AI offers similar integrated intelligence features for sales, service, and operations within a familiar environment.
  • If no, proceed to the next question.
• Are you looking for a general-purpose, end-to-end platform for building, deploying, and managing custom machine learning models, including generative AI?
  • If yes, Google Vertex AI provides a unified MLOps platform with extensive tools for custom model training, deployment, and access to Google's foundational models. This is suitable for organizations requiring broad ML capabilities across various data types.
  • If no, proceed to the next question.
• Do you specifically need access to OpenAI's advanced large language models (GPT-4, etc.) with enterprise-grade security, compliance, and customizability within a cloud environment?
  • If yes, and you operate within the Azure ecosystem, Azure OpenAI Service offers secure and governed access to these models, integrating with Azure's services.
  • If yes, and you require dedicated instances, high volume, and direct control over OpenAI models for large-scale deployments, OpenAI Enterprise provides these specialized features.
  • If no, proceed to the next question.
• Is your primary need to build custom generative AI applications and large language models (LLMs), especially with large datasets and fine-tuning capabilities, on a unified data platform?
  • If yes, Databricks Mosaic AI leverages the Databricks Lakehouse Platform to offer tools for the entire lifecycle of generative AI, from data preparation to model deployment.
  • If no, proceed to the next question.
• Are you looking for a low-code platform to empower citizen developers to create custom AI assistants (copilots) and integrate generative AI into existing Microsoft 365 and Power Platform workflows?
  • If yes, Microsoft Copilot Studio is designed for this purpose, enabling the creation of custom AI experiences without extensive coding.
  • If no, proceed to the next question.
• Do you prioritize highly secure, responsible, and interpretable large language models for enterprise applications, particularly for internal knowledge management or sensitive content?
  • If yes, Anthropic Enterprise (Claude for Work) focuses on safety, interpretability, and enterprise-grade security for its LLMs, suitable for organizations with stringent ethical and compliance requirements.