AI in finance grows as cloud database market hits $119.5B

AI and cloud databases reshape finance as adoption surges globally

American technology giants Amazon Web Services (AWS), Google, and Microsoft dominate the cloud database management systems (DBMS) market, leading the space with their extensive market presence and reputation, broad selection of cloud DBMS services, and advanced artificial intelligence (AI) capabilities, according to an analysis by Gartner, an American research and advisory firm.

Cloud DBMS are software products designed to store and manipulate data, and which are primarily delivered as platform-as-a-service (PaaS) in the cloud. These systems address diverse use cases, including online transaction processing, high-volume simple transactions, and the management of session states at scale. They also provide rich user profiles, offer variable consistency mechanisms, and manage data from multiple sources within highly structured schemas to meet analytical demands.

Common features include support for multiple data models and types, PaaS delivery with options for on-premises deployment, and integrated AI, machine learning (ML) and generative AI (genAI) capabilities.

With the DBMS industry experiencing significant growth and undergoing a profound transformation driven by AI adoption, real-time processing requirements, and new interaction methods between systems, Gartner has shared a new analysis of vendors.

This analysis is based on the Magic Quadrant, a framework that evaluates providers' capabilities based on their execution and vision in 2024 and early 2025, as well as their future plans. It categorizes DBMS providers into four groups: the Leaders, the Visionaries, the Challengers, and the Niche Players.

DBMS leaders

The Leaders, which comprise nine players, support a broad range of DBMS use cases, lead in advanced features and architecture, and demonstrate strong strategic vision.

AWS stands out as one of these Leaders, recognized for its extensive global infrastructure spanning over 30 regions, its comprehensive suite of purpose-built databases, and its ability to unify data and AI governance through SageMaker, which provides central access to create, govern and share data, analytics and AI assets.

However, AWS also faces limitations. First, the sheer volume of overlapping and sometimes conflicting features can create confusion for customers trying to determine the optimal solution mix. Second, the lack of a unified pricing model can make cost tracking and management challenging. AWS has also limited its focus on multicloud strategies, preferring native connectors and support for open-source engines. This is forcing many customers to rely on third-party solutions for hybrid and multicloud orchestration.

Google is another DBMS leader, recognized for its comprehensive suite of managed database services, including Spanner, BigQuery, AlloyDB, and Cloud SQL. A primary strength of Google's offerings is its deep integration of its proprietary AI models, like Gemini, directly into its database offerings to facilitate agentic AI and complex automated workflows.

However, Google also has limitations, including an ecosystem that can be complex to navigate and which is still maturing, and intricate cost management within its cloud database services.

Another key DBMS leader is Microsoft, offering an extensive portfolio that comprises Azure SQL Database, Azure Cosmos DB, and its converged data, analytics and AI platform, Microsoft Fabric. Other strengths of Microsoft include the company's deep engagement with the PostgreSQL community, driving improvements in I/O and performance, as well as its strong capabilities to support genAI and AI agents.

Limitations include a potential overlap between its unified 'all-in-one' platform and established enterprise strategies, unproven functions in Microsoft Fabric and ongoing customer concerns regarding its data warehouse and data governance functions, and the aggressive integration of operational databases like Azure SQL and Cosmos DB into Fabric which can introduce compatibility challenges such as performance and resource management in the short term.

Besides the Leaders, the Visionaries category includes DBMS vendors with a strong market understanding and a robust roadmap for the cloud DBMS market, but which lack the market presence of Leaders. These players include Cloudera, SAP, and Teradata.

The third category, the Challengers, are vendors with strong, established offerings, but which lack a clear vision for the cloud DBMS market. They include InterSystems, and Huawei Systems.

Finally, the Niche Players are those delivering a highly specialized but restricted range of products with particular, limited market appeal. They include Couchbase, EDB, and SingleStore.

DBMS market growth and trends

The global DBMS market reached US$119.5 billion in 2024, growing 13.4% year-on-year. Gartner expects the industry to expand by 18.4% in 2026 to US$161 billion, driven by rising data volumes and growth of analytic workloads, AI adoption and cloud-native expansion.

Organization across all industries are generating unprecedented amounts of data amid digital transformation initiatives, expanded online operations, and automation of business processes. This changing landscape requires scalable, cost-effective storage and processing solutions that traditional on-premise systems struggle to offer.

At the same time, AI applications demand databases optimized for ML, real-time analytics, and predictive technologies, requiring architecture capable of efficiently handling massive scale, low-latency requirements, and vector operations.

AI penetration in the enterprise is accelerating. An analysis by Andreessen Horowitz, based on internal data and conversations with corporate executives, found that 29% of the Fortune 500 and about 19% of the Forbes Global 2000 are now live, paying customers of a leading AI startup. This rapid adoption is notable because large corporations historically resist being early tech adopters and typically wait years before becoming customers.

According to the research, enterprise adoption is currently dominated by specific use cases and industries. Coding, support, and search represent the lion's share of use cases, while the tech, legal, and healthcare sectors are the industries most eager to adopt AI.

In finance, AI usage is expanding rapidly, with 71% of the companies polled by KPMG in 2024 reporting the use of AI, and 41% utilizing it to a moderate or large degree.

Companies are turning to AI in every area of finance. Accounting and financial planning are currently the furthest ahead, with nearly two-thirds of the respondents piloting or using AI for these functions. In these areas, organizations are aiming for benefits including improved data processing, financial reporting, real-time insights, and predictive analysis.

Treasury and risk management follow suit, with nearly half of respondents piloting or using AI to improve debt management, cash-flow forecasting, fraud detection, credit risk assessment, and scenario analysis.