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.
Join TBR for a discussion on the most critical elements of a successful hyperscaler partnership and partner perceptions of Amazon Web Services, Microsoft and Google Cloud
Learn how European Union AI and data regulations are impacting staffing and training within GSI practices and see an exclusive look at our newly expanded regional breakdown of GSI headcount and revenue
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.
Oracle’s full-stack approach to analytics makes a compelling case for consolidation, helping partners create value by eliminating disparate integrations and unlocking ROI. This is particularly true for partners that are perhaps willing to abandon the typical tech-agnostic approach and recommend Oracle as the primary choice from a data and analytics perspective. If Oracle engages a broader external data ecosystem in the future, as discussed above, partners will need to make sure they look beyond the applications layer and leverage Oracle’s broad PaaS and IaaS capabilities for custom development use cases.
When discussing Google Cloud’s three key attributes, Kurian first highlighted how Google Cloud Platform (GCP) is optimized for AI. Based on our own conversations with IT decision makers, this claim is valid: many customers enlist GCP services purely for functional purposes, as they believe they cannot obtain the same performance with another vendor. This is particularly true of BigQuery, for large-scale data processing and analytics, and increasingly Vertex AI, which now supports over 200 curated foundation models for developers.
One could argue that many of KPMG’s steps, including launching partner-enabled industry IP, reinforcing trust, developing regionally organized operations, outlining a select few strategic partners, and investing in platform-enabled service delivery capabilities, resonate with the moves taken by many of its Big Four and large IT services peers. We see two differences: KPMG is laser-focused on exactly which of the strategies above to amplify, rather than taking a trial-and-error approach, and KPMG has an opportunity to ride the wave of a once-in-a-century professional services market transformation.
Learn ecosystem best practices of data cloud ISVs, C-Suite priorities regarding data management and GenAI, how hyperscalers are adjusting their partnering strategies to improve the flow of data and win new GenAI workloads, and more