Buyer’s Guide: Enterprise AI Platforms (2025)
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Executive Summary
AI is transforming the way organizations use technology. Nevertheless, many firms struggle to adopt the full breadth and depth of AI technologies. According to the Verdantix global survey of AI decision-makers, a primary reason for this is the complexity of AI technologies, which 89% of respondents consider a significant barrier to enterprise AI adoption (see Verdantix Global Corporate Survey 2024: Artificial Intelligence Budgets, Priorities And Tech Preferences). Enterprise AI platforms with intuitive machine learning operations (MLOps) and data management can provide a mechanism to support scalable business AI integration, with the necessary guardrails. These platforms also create channels for incorporating new AI-related innovation. This report provides executives and IT organizations with an overview of enterprise AI platforms, as well as profiles of 20 prominent providers in the market.
Table of contents
Summary for decision-makersAI platforms provide an enterprise-grade mechanism for integrating AI
Building a business case for an enterprise AI platform
Defining the enterprise AI platform
The ecosystem for enterprise AI platforms
Alibaba Cloud complements its cloud computing portfolio with AI infrastructure
Altair Engineering expands its computational intelligence portfolio with an AI platform
Alteryx builds on its data and analytics heritage with its AI platform and analytics cloud
Amazon Web Services (AWS) broadens its portfolio with comprehensive AI platform innovation and partnerships
Anyscale brings an enterprise AI platform approach to its open-source roots
C3 AI adds its enterprise AI platform to an expanding library of AI applications
Cloudera leverages its data science heritage with continuing innovation in its AI platform
Cohere takes AI into the workplace with its AI platform and own models
Databricks extends its approach to data intelligence through its AI platform and partner network
Dataiku combines data preparation, machine learning and GenAI development in its AI platform
DataRobot accelerates AI innovation for all skill levels and business environments
Domino Data Lab promotes its ability to unify and scale AI in hybrid environments
Glean focuses on deploying AI in the workplace through its centralized agent platform
Google brings AI to everyday work, adding agents to its expansive AI platform capabilities
H2O.ai supports an open-source approach to AI for a wide range of skill levels
IBM updates its AI strategy to serve a broader range of enterprise use cases
Microsoft continues to expand its portfolio through AI innovation and partnerships
Oracle builds on its application and database legacy with AI investments
Palantir Technologies delivers verticalized expertise for organizations to drive automation
SAS leverages its data and analytics heritage to accelerate AI innovation
Table of figures
Figure 1. Key buyer considerations for enterprise AI platformsFigure 2. List of enterprise AI platform and application providers
Figure 3. Categories of enterprise AI platforms
Figure 4. Alibaba Cloud overview
Figure 5. Altair Engineering overview
Figure 6. Alteryx overview
Figure 7. Amazon Web Services (AWS) overview
Figure 8. Anyscale overview
Figure 9. C3 AI overview
Figure 10. Cloudera overview
Figure 11. Cohere overview
Figure 12. Databricks overview
Figure 13. Dataiku overview
Figure 14. DataRobot overview
Figure 15. Domino Data Lab overview
Figure 16. Glean overview
Figure 17. Google overview
Figure 18. H20.ai overview
Figure 19. IBM overview
Figure 20. Microsoft overview
Figure 21. Oracle overview
Figure 22. Palantir Technologies overview
Figure 23. SAS overview
Organisations mentioned
ABB, Accenture, Adobe, Agnostiq, AI21 Labs, Alibaba Cloud, Allstate, Altair Engineering, Alteryx, Amazon, Amazon Web Services (AWS), AMD, Anaconda, Anthropic, Anyscale, App Orchid, AstraZeneca, AT&T, AuditBoard, Baidu, Baker Hughes, BambooHR, Banco BG, Bank of America, Bayer, Beosin, Bespin Global, bmc, BMW, BNP Paribas, BSI, Bupa, C3 AI, Cainiao, Capgemini, Carahsoft, Cargill, Carrier, Cerence, Chanel, Chevron Phillips Chemical, Chipotle, Cisco, Citi, Clarista, Clayton, Clearlake Capital, Cloudera, Cognite, Cognizant, Cohere, Compliance.ai, Confluent, Credo AI, Databricks, Dataiku, Dataloop, DataRobot, DataStax, Deepgram, DeepSeek, Dell, Deloitte, Deutsche Telekom, Divirod, Domino Data Lab, Dow, Dubilier & Rice (CD&R), Exacter, Experian, EY, Facebook, Fujitsu, Gemini, Generali Group, GitHub, Glean, Global Credit, Google, Great Hill Partners, Groq, GSK, H2O.ai, Hang Seng Bank, Hazy, Holistic AI, Honda, Howso, HSBC, Hugging Face, IAPP, IBM, Insight Partners, Instagram, Intel, International Organization for Standardization (ISO), International Paper, IQVIA, John Snow Labs, Johnson & Johnson, Johnson Lambert, Jupyter, KKR, Koch, Kubernetes, Kumo, L3Harris, Lockheed Martin, LVMH, Mabe, McKinsey & Company, Merck Group, Meta, Microsoft, MindBridge, Mistral AI, Moderna, Moody’s, Morgan Stanley, Mountain Fog, NASA, Novartis, Novo Nordisk, NVIDIA, NYSE (New York Stock Exchange), NZ Post, OpenAI, Oracle, Palantir Technologies, Pandata, PayPal, PepsiCo, Prada Group, Predictier, Procter & Gamble, Protopia AI, Python, Red Hat, RSM, Salesforce, Samsara, SAP, SAS, Scale AI, Scuderia Ferrari, Securai, Sephora China, ServiceNow, Shell, Siemens, Sierra, Slack, SmartBridge, Snowflake, Splunk, Springbok AI, Stability AI, StateRAMP, Sweco, SymphonyAI, Tableau, Tangent Works, Telefónica, Telkom Indonesia, Tencent, ThousandEyes, Trustible, Uber, UbiAI, Unilever, Unity, US Department of Defense, US Intelligence Community, US National Institute of Standards and Technology, Veezoo, Verta, Virtualitics, Vodafone, Volvo Cars, Volvo Group, Wayfair, Webflow, Wellington Management, Wells Fargo, Whale Cloud, Workato, Zetta CloudAbout the authors
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