As enterprises look to operationalize AI across distributed environments, scale becomes less about where the infrastructure sits and more about how effectively the underlying data can be governed, accessed and turned into value. Teradata’s ability to deliver AI to where customers’ data already lives — without the need to modernize their infrastructure — will become more relevant.
All hyperscalers tout themselves as “full-stack” to a degree, but Google Cloud’s distinct advantage is that it owns a leading frontier model, Gemini. Having Gemini deeply embedded throughout the portfolio creates a powerful flywheel effect that lets Google Cloud monetize AI in ways others cannot. At the same time, the value is shifting from the AI models themselves to how those models work with a growing set of tools and data to create value. From a repackaged PaaS layer to a revamped data stack, announcements at Google Cloud Next 2026 reinforce that this will be the company’s next chapter.
The emerging pattern of multicloud security consolidation has direct implications for both Amazon Web Services (AWS) and Microsoft, as enterprises reassess detection pipelines, governance models and operating frameworks heading into 2026. Although AWS remains well positioned in analytics-heavy workloads, the company needs to reevaluate its long-established “building block” approach, especially as peers deliver more integrated platforms. For Microsoft, its strengths will continue to be with organizations where Microsoft 365 already anchors their identity and collaboration strategies.
Though not immune to the risks and uncertainties of the AI market at large, Oracle is certainly executing, with the bulk of revenue from AI contracts already booked in its multibillion-dollar remaining performance obligations (RPO) balance. And yet, as OCI becomes a more prominent part of the Oracle business, big opportunities remain for Oracle, particularly in how it partners, prices and simply exists within the data ecosystem.
Agentic AI has customers and ecosystem participants reconsidering the role of the database. MongoDB is well positioned to capitalize on the AI opportunity, partly due to its JSON-native document database, as well as its early support for native vector search in the cloud. But for customers using legacy systems, there is still much work to be done in preparing for AI, and this is where MongoDB’s investment priorities lie.
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Red Hat Summit 2025 marked the company’s entry into AI inferencing with the productization of vLLM, the open-source project that has been shaping AI model execution over the past two years.
Oracle’s current business strategy centers on streamlining customer success efforts, enhancing partner collaboration, and expanding multicloud infrastructure. By consolidating its services under the Oracle Customer Success Services (CSS) umbrella, the company has improved life cycle support for clients, reduced overlap with systems integrators, and equipped partners with tools like the Cloud Success Navigator to enhance implementation and renewal outcomes.
Between the technology partners and GSIs, Informatica works with a robust ecosystem of partners in a triparty approach, where resources from a hyperscaler, GSI and Informatica are brought together to help customers modernize their data faster and, by default, hasten AI’s time to value. When we survey and speak to alliance decision makers at IT services firms, data management comes up as one of the top areas for partner-led growth, signaling to the ecosystem that they will continue to invest in resources to guide conversations with customers with the technology maturity to address the data foundations ahead of GenAI.