By Mohan Atreya, Chief Product Officer, Rafay Systems

As technical leaders navigate AI's increasingly complex infrastructure landscape, two key transformations are emerging for enterprises to remain competitive: the strategic use of GPU resources and the evolution toward true, self-service experiences for developers and data scientists. AI and compute consumption demands are on the rise and legacy infrastructure management solutions can't keep up. At the same time, building a platform that can enable self-service consumption of accelerated computing hardware can take up to two years, and organizations don't have time to sit around and wait for innovation to happen.

The success of AI and GenAI initiatives lies in the platforms and tools that can maximize existing computational resources while creating frictionless, self-service experiences that abstract complexity - specifically for the developers creating and managing these new workflows, software and applications. A product-led approach to platform engineering can strategically lead organizations toward self-service experiences, providing a path to efficient GPU consumption. Below, I share the top trends I anticipate will accelerate AI innovation in 2025.

Optimize GPU Infrastructure Now or Risk Being Left Behind

Current GPU utilization rates remain strikingly low across enterprises - nearly a third of enterprises are utilizing less than 15% of GPU capacity. With organizations pouring tens or hundreds of millions of dollars into AI projects, they can't afford impacts to efficiency. Emerging optimization platforms will combine workload-aware scheduling, dynamic resource allocation and AI-driven optimization to turn idle GPUs into scalable and elastic resources for developers and data scientists.

Early adopters of these optimization technologies will gain significant cost and capability advantages, while those focusing solely on hardware acquisition will fall behind in both economics and AI capabilities. CIOs should prioritize optimization of existing GPU infrastructure over pursuing additional hardware capacity at premium prices. While many organizations are fixated on acquiring more GPUs, the real competitive advantage will come from maximizing the efficiency of existing resources through advanced optimization technologies.

Organizations Will Embrace a Product-led Approach

Every enterprise is progressing toward a true self-service platform experience where infrastructure becomes invisible to end users. The journey follows a clear evolution: from basic infrastructure or Terraform, to automated workflows, standardized deployments and ultimately, centralized platform operations delivering true self-service capabilities. The goal is to get to a point where developers and data scientists can simply click a button to get a result, and that is self-service. That is nirvana.

Currently, most organizations are still in early stages, focused on basic automation rather than comprehensive self-service delivery. Many companies have fragmented their platform efforts by creating distributed DevOps teams, but this approach needs to be consolidated into centralized platform engineering teams delivering standardized self-service capabilities. Success requires organizations to adopt a product-led approach to platform engineering to efficiently build and deliver internal platforms as a service for developers and data scientists that accelerate application deployment across diverse cloud-native and AI/ML infrastructures.

Many organizations start their respective automation journey, but look at each step in the process as an individual action to automate, versus holistically thinking about automation. This nuance results in teams having many distinct steps, each of which is automated, but no uber layer to execute end-to-end workflows. Organizations need to step back and think about the end-to-end workflows that developers and data scientists need; this is the only way to deliver "products" versus "technical features."

Reimagining Enterprise AI Infrastructure through a Self-Service Compute Revolution

Heading into the new year, it's clear that a holistic, product-oriented approach to platform engineering will transcend traditional infrastructure while providing developers and data scientists with the tools they need to efficiently leverage GPU resources. By transforming underutilized GPU infrastructure into dynamically allocated resources and creating self-service platforms that abstract AI and infrastructure complexities, companies are well-positioned to be powerful engines of innovation in 2025.

##

ABOUT THE AUTHOR

Mohan is the CPO at Rafay Systems, a leading provider of Platform-as-a-Service (PaaS) capabilities for cloud-native and GPU and AI consumption. He is an avid human psychology practitioner and astronomy enthusiast who has spent serious money chasing stardust. Unlike many B2B Product Managers, Mohan's path to product management has been non-traditional perhaps because of ideal growing years spent playing soccer right next to the beach. He started his journey as a Sales Engineer, sold a lot of enterprise security products at RSA and then pivoted to Product Management. Since then he has launched and grown products at OKTA, Neustar, McAfee, Cisco and now at Rafay Systems.