Market Perspectives

ISG Research is happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The Cloud-Native AI and Data Platforms: ISG Research Buyers Guide is the distillation of a year of market and product research by ISG Research.

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.

This research-based index evaluates the full business and information technology value of artificial intelligence software offerings. We encourage you to learn more about our Buyers Guide and its effectiveness as a provider selection and RFI/RFP tool.

We urge enterprises to do a thorough job of evaluating cloud-native AI and data platforms offerings in this Buyers Guide as both the results of our in-depth analysis of these software providers and as an evaluation methodology. The Buyers Guide can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP and the definition of an RFI.

The Buyers Guide for Cloud-Native AI and Data Platforms in 2024 finds AWS first on the list, followed by Microsoft and Google Cloud.

Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader.

The Leaders in Product Experience are:

• Oracle
• AWS
• Google Cloud.

The Leaders in Customer Experience are:

• Microsoft
• Oracle
• AWS

The Leaders across any of the seven categories are:

• AWS, IBM and Oracle, which has achieved this rating in five of the seven categories.
• Microsoft in four categories.
• Google Cloud in two categories.

The overall performance chart provides a visual representation of how providers rate across product and customer experience. Software providers with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, providers that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as providers of “merit.” Software providers that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating.

Note that close provider scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every enterprise or process. Although there is a high degree of commonality in how organizations handle cloud-native AI and data platforms, there are many idiosyncrasies and differences that can make one provider’s offering a better fit than another.

ISG Research has made every effort to encompass in this Buyers Guide the overall product and customer experience from our cloud-native AI and data platforms blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which software provider and products best fit an enterprise’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects.

You can find more details on our community as well as on our expertise in the research for this Buyers Guide.