Executive Summary

Cloud-Native AI and Data Platforms

Cloud computing has emerged as a fundamental technology driving the digital transformation of enterprises, irrespective of size or maturity level. It provides a robust and scalable infrastructure that supports the dynamic needs of today’s enterprises, enabling them to stay competitive in a digital world.

Artificial intelligence is also front and center in enterprise IT architectures due to a combination of factors that have dramatically increased awareness of and investment in technologies to support its purpose. Since its inception, AI has provided value no matter how or where it has been applied: helping to prevent credit card fraud, segmenting customers for more effective marketing campaigns, making recommendations for the next best action, predicting maintenance routines to prevent machine failures and many other use cases. However, AI is dependent on data. Large amounts of high-quality data are necessary to feed and train models.

Data platforms provide an environment for organizing and managing the storage, processing, analysis and presentation of data across an enterprise. Data platforms play a critical role in business operations, supporting and enabling applications to run and evaluate the business. Today, many business operations and analyses involve a combination of data and AI, and enterprises are turning to the cloud to access the resources needed to execute AI strategies. As a result, most leading cloud platform vendors have built a portfolio of product capabilities that include AI and data.

ISG defines cloud-native AI and data platforms as a software service that organizations use to access virtualized IT resources, including AI and data capabilities, via the internet on a pay-per-use basis. The IT resources include servers, data processing power, data storage, networking infrastructure and virtualization capabilities. AI capabilities involve the ability to prepare, deploy and maintain AI models. Data capabilities include data persistence, data management, data processing and data query functionality that enables access to—and interaction with—the stored data.

Cloud platforms are an alternative to operating on-premises data centers and compute environments of networking and servers. They can be segmented into five main types: public cloud, private cloud, hybrid cloud, multi-cloud and sovereign cloud. Each segment addresses the unique needs of an enterprise and has gained prominence due to its distinctive attributes. For example:

• Public cloud services are provided by third-party providers over the internet and are available to anyone who wishes to use or purchase them.
• Private cloud computing resources are used exclusively by a single business or organization.
• Hybrid cloud combines a private cloud with one or more public cloud services, using proprietary software for communication between each distinct service.
• Multi-cloud options deploy multiple cloud computing and storage services in a single network architecture. This refers to the distribution of cloud assets, software, applications and more across several cloud environments.
• Sovereign cloud infrastructure operates within the borders of a specific country and is compliant with its data sovereignty laws.

Each of these cloud segments addresses specific enterprise needs and concerns, such as cost efficiency, scalability, control, flexibility and regulatory compliance. As an enterprise continues to change and grow, these cloud approaches will play a crucial role in its journey, intertwining with data and AI architectures designed to support the performance, resilience and compliance requirements enterprises face.

Because AI workloads are highly variable and require large amounts of resources, there are many synergies with cloud and data platforms. The AI model development process requires data preparation and feature engineering to identify and organize data in the way that will produce the most accurate models. The volumes of data necessary for accurate AI models place a significant demand on computing resources, which can often be best met with elastic cloud platforms. The cost of these systems can be significant, and any inefficiencies in the process can exacerbate the costs.

Developing and deploying AI models is a multistep process involving data. It begins with collecting and curating the data that will be used to create the model. Once a model is developed and tuned using the training data, it is tested to determine its accuracy and performance. Then, the model is applied in an operational application or process. For example, in a customer service application, a predictive AI model might make a recommendation for how a representative should respond to the customer’s situation. Similarly, a self-service customer application might use a large language model to provide a chatbot or guided experience to deliver those recommendations.

The increasing importance of intelligent operational applications driven by AI insights is blurring the lines that have traditionally divided the requirements for AI platforms and data platforms. Consumers are increasingly engaged with data-driven services differentiated by personalization and contextually relevant recommendations. Additionally, worker-facing applications are following suit, targeting users based on their roles and responsibilities. The shift to more agile business processes requires ML for more responsive data platforms and applications.

The need for real-time interactivity driven by AI has significant implications for the data platform functionality required to support these applications. While there have always been general-purpose databases that could be used for both analytic and operational workloads, traditional architectures have involved the extraction, transformation and loading of data from the operational data platform into an external analytic or AI platform. This enables the operational and analytic workloads to run concurrently without adversely impacting each other, protecting the performance of both.

Over time, data platforms have evolved differentiated architectural approaches designed to improve workload management and isolation of potentially conflicting workloads. Intelligent applications, while operational in nature, rely on real-time analytic processing to deliver functionality, including contextually relevant recommendations, predictions and forecasting driven by ML and generative AI. While data-driven companies continue to use separate data and AI platforms to train models offline, the need for real-time online predictions and recommendations requires that operational data platforms perform ML inferencing.

Therefore, it is important to coordinate cloud, AI and data efforts. Enterprises can’t waste time or resources applying AI; the risks of being left behind and put at a competitive disadvantage are too great. Cloud platform providers have recognized the opportunity to help enterprises address these needs, and the top providers are all offering platforms that combine AI and data capabilities. You can debate whether it’s cloud first, AI first or data first, but in reality, it’s all three. We assert that through 2026, more than one-third of enterprises will deploy cloud-native AI and data platforms in information architectures.

The Cloud-Native AI and Data Platform Buyers Guide includes an evaluation of platforms that provide three sets of capabilities: cloud, AI and data. To be considered for inclusion in this Buyers Guide, a product must offer services addressing key elements of cloud platforms that:

• Support a combination of public, private and hybrid cloud workloads.
• Include a general-purpose data platform, database, database management system, data warehouse, data lake or data lakehouse.
• Include data persistence, data management, data processing and data query functional areas.
• Support database administrator functionality, developer functionality, data engineering functionality and data architect functionality.
• Support the AI-related capabilities of data preparation, AI/ML modeling, AutoML, GenAI, developer and data scientist tooling, MLOps/LLMOps, model deployment, model tuning and optimization.

This research evaluates the following software providers that offer products that address key elements of cloud-native AI and data platforms as we define it: Alibaba Cloud, Amazon Web Services, Google Cloud, IBM, Microsoft and Oracle.

Buyers Guide Overview

For over two decades, ISG Research has conducted market research in a spectrum of areas across business applications, tools and technologies. We have designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of the business requirements in any enterprise. Utilization of our research methodology and decades of experience enables our Buyers Guide to be an effective method to assess and select software providers and products. The findings of this research contribute to our comprehensive approach to rating software providers in a manner that is based on the assessments completed by an enterprise.

This ISG Research Buyers Guide: Native Cloud and AI Data Platforms is the distillation of over a year of market and product research efforts. It is an assessment of how well software providers’ offerings address enterprises’ requirements for cloud-native AI and data platforms software. The index is structured to support a request for information (RFI) that could be used in the request for proposal (RFP) process by incorporating all criteria needed to evaluate, select, utilize and maintain relationships with software providers. An effective product and customer experience with a provider can ensure the best long-term relationship and value achieved from a resource and financial investment.

In this Buyers Guide, ISG Research evaluates the software in seven key categories that are weighted to reflect buyers’ needs based on our expertise and research. Five are product-experience related: Adaptability, Capability, Manageability, Reliability, and Usability. In addition, we consider two customer-experience categories: Validation, and Total Cost of Ownership/Return on Investment (TCO/ROI). To assess functionality, one of the components of Capability, we applied the ISG Research Value Index methodology and blueprint, which links the personas and processes for cloud-native AI and data platforms to an enterprise’s requirements.

The structure of the research reflects our understanding that the effective evaluation of software providers and products involves far more than just examining product features, potential revenue or customers generated from a provider’s marketing and sales efforts. We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of cloud-native AI and data platforms technology can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its full performance potential. In addition, this approach can reduce the project’s development and deployment time and eliminate the risk of relying on a short list of software providers that does not represent a best fit for your enterprise.

ISG Research believes that an objective review of software providers and products is a critical business strategy for the adoption and implementation of cloud-native AI and data platforms software and applications. An enterprise’s review should include a thorough analysis of both what is possible and what is relevant. We urge enterprises to do a comprehensive job of evaluating cloud-native AI and data platforms systems and tools and offer this Buyers Guide as both the results of our in-depth analysis of these providers and as an evaluation methodology.

How To Use This Buyers Guide

Evaluating Software Providers: The Process

We recommend using the Buyers Guide to assess and evaluate new or existing software providers for your enterprise. The market research can be used as an evaluation framework to establish a formal request for information from providers on products and customer experience and will shorten the cycle time when creating an RFI. The steps listed below provide a process that can facilitate best possible outcomes.

1. Define the business case and goals.
   Define the mission and business case for investment and the expected outcomes from your organizational and technology efforts. 
2. Specify the business needs.
   Defining the business requirements helps identify what specific capabilities are required with respect to people, processes, information and technology.
3. Assess the required roles and responsibilities.
   Identify the individuals required for success at every level of the organization from executives to front line workers and determine the needs of each. 
4. Outline the project’s critical path.
   What needs to be done, in what order and who will do it? This outline should make clear the prior dependencies at each step of the project plan. 
5. Ascertain the technology approach.
   Determine the business and technology approach that most closely aligns to your organization’s requirements. 
6. Establish technology vendor evaluation criteria.
   Utilize the product experience: Adaptability, Capability, Manageability, Reliability and Usability, and the customer experience in TCO/ROI and Validation. 
7. Evaluate and select the technology properly.
   Weight the categories in the technology evaluation criteria to reflect your organization’s priorities to determine the short list of vendors and products.
8. Establish the business initiative team to start the project.
   Identify who will lead the project and the members of the team needed to plan and execute it with timelines, priorities and resources.

The Findings

All of the products we evaluated are feature-rich, but not all the capabilities offered by a software provider are equally valuable to an enterprise’s workers or support everything needed to manage products on a continuous basis. Moreover, the existence of too many capabilities may be a negative factor for an enterprise if it introduces unnecessary complexity. Nonetheless, you may decide that a larger number of features in the product is a plus, especially if some of them match your enterprise’s established practices or support an initiative that is driving the purchase of new software.

Factors beyond features and functions or software provider assessments may become a deciding factor. For example, an enterprise may face budget constraints such that the TCO evaluation can tip the balance to one provider or another. This is where the Value Index methodology and the appropriate category weighting can be applied to determine the best fit of software providers and products to your specific needs.

Overall Scoring of Software Providers Across Categories

The research finds Amazon Web Services atop the list, followed by Microsoft and Google Cloud. Companies that place in the top three of a category earn the designation of Leader. Amazon Web Services, IBM, and Oracle have done so in five categories; Microsoft in four and Google Cloud in two categories.

The overall representation of the research below places the rating of the Product Experience and Customer Experience on the x and y axes, respectively, to provide a visual representation and classification of the software providers. Those providers where Product Experience has a higher weighted performance to the axis in aggregate of the five product categories place farther to the right, while the performance and weighting for the two Customer Experience categories determines placement on the vertical axis. In short, software providers that place closer to the upper-right on this chart performed better than those closer to the lower-left.

The research places software providers into one of four overall categories: Assurance, Exemplary, Merit or Innovative. This representation classifies providers’ overall weighted performance.

Exemplary: The categorization and placement of software providers in Exemplary (upper right) represent those that performed the best in meeting the overall Product and Customer Experience requirements. The providers rated Exemplary are: Amazon Web Services and Oracle.

Innovative: The categorization and placement of software providers in Innovative (lower right) represent those that performed the best in meeting the overall Product Experience requirements but did not achieve the highest levels of requirements in Customer Experience. The provider rated Innovative is: Google Cloud.

Assurance: The categorization and placement of software providers in Assurance (upper left) represent those that achieved the highest levels in the overall Customer Experience requirements but did not achieve the highest levels of Product Experience. The provider rated Assurance is: Microsoft.

Merit: The categorization of software providers in Merit (lower left) represents those that did not exceed the median of performance in Customer or Product Experience or surpass the threshold for the other three categories. The providers rated Merit are: Alibaba Cloud and IBM.

We warn that close provider placement proximity should not be taken to imply that the packages evaluated are functionally identical or equally well suited for use by every enterprise or for a specific process. Although there is a high degree of commonality in how enterprises handle cloud-native AI and data platforms, there are many idiosyncrasies and differences in how they do these functions that can make one software provider’s offering a better fit than another’s for a particular enterprise’s needs.

We advise enterprises to assess and evaluate software providers based on organizational requirements and use this research as a supplement to internal evaluation of a provider and products.

Product Experience

The process of researching products to address an enterprise’s needs should be comprehensive. Our Value Index methodology examines Product Experience and how it aligns with an enterprise’s life cycle of onboarding, configuration, operations, usage and maintenance. Too often, software providers are not evaluated for the entirety of the product; instead, they are evaluated on market execution and vision of the future, which are flawed since they do not represent an enterprise’s requirements but how the provider operates. As more software providers orient to a complete product experience, evaluations will be more robust.

The research results in Product Experience are ranked at 85% of the overall rating using the specific underlying weighted category performance. Importance was placed on the categories as follows: Usability (10%), Capability (35%), Reliability (20%), Adaptability (5%) and Manageability (15%). This weighting impacted the resulting overall ratings in this research. Oracle, Amazon Web Services and Google Cloud were designated Product Experience Leaders. While not a Leader, Microsoft was also found to meet a broad range of enterprise product experience requirements.

Many enterprises will only evaluate capabilities of the product, but the research identified the criticality of Reliability (20% weighting) as a key requirement to ensure that enterprises can depend upon the systems running on these cloud-native AI and data platforms.

Customer Experience

The importance of a customer relationship with a software provider is essential to the actual success of the products and technology. The advancement of the Customer Experience and the entire life cycle an enterprise has with its software provider is critical for ensuring satisfaction in working with that provider. Technology providers that have chief customer officers are more likely to have greater investments in the customer relationship and focus more on their success. These leaders also need to take responsibility for ensuring this commitment is made abundantly clear on the website and in the buying process and customer journey.

The research results in Customer Experience are ranked at 15% using the specific underlying weighted category performance as it relates to the framework of commitment and value to the software provider-customer relationship. The two evaluation categories are Validation (7.5%) and TCO/ROI (7.5%), which are weighted to represent their importance to the overall research.

The software providers that evaluated the highest overall in the aggregated and weighted Customer Experience categories are Microsoft, Oracle and Amazon Web Services. These category Leaders best communicate commitment and dedication to customer needs. While not a Leader, IBM was also found to meet a broad range of enterprise customer experience requirements.

Most software providers we evaluated have sufficient customer experience information available through the website and presentations. While most have customer case studies to promote success, some lack depth in articulating commitment to customer experience and an enterprise’s cloud-native AI and data platforms journey. As the commitment to a software provider is a continuous investment, the importance of supporting customer experience in a holistic evaluation should be included and not underestimated.

Appendix: Software Provider Inclusion

For inclusion in the ISG Research Cloud-Native AI and Data Platforms Buyers Guide for 2024, a software provider must be in good standing financially and ethically, have at least $100 million in annual or projected revenue verified using independent sources, sell products and provide support on at least two continents and have at least 50 customers. The principal source of the relevant business unit’s revenue must be software-related and there must have been at least one major software release in the last 12 months.

The services must include access to virtualized IT resources as well as AI and data capabilities via the internet on a pay-per-use basis. The IT resources include servers, data processing power, data storage, networking infrastructure and virtualization capabilities. AI capabilities cover the ability to prepare, deploy and maintain AI models. Data capabilities span data persistence, data management, data processing and data query functionality that enables access to, and interaction with, the stored data.

The research is designed to be independent of the specifics of software provider packaging and pricing. To represent the real-world environment in which businesses operate, we include providers that offer suites or packages of products that may include relevant individual modules or applications. If a software provider is actively marketing, selling and developing a product for the general market and it is reflected on the provider’s website that the product is within the scope of the research, that provider is automatically evaluated for inclusion.

All software providers that offer relevant cloud-native AI and data platforms products and meet the inclusion requirements were invited to participate in the evaluation process at no cost to them.

Software providers that meet our inclusion criteria but did not completely participate in our Buyers Guide were assessed solely on publicly available information. As this could have a significant impact on classification and ratings, we recommend additional scrutiny when evaluating those providers.

Products Evaluated