As KubeCon + CloudNativeCon North America 2025 approaches in Atlanta, platform engineering teams are grappling with increasingly complex challenges: managing fragmented infrastructure across hybrid environments, orchestrating GPU workloads for AI deployments, and maintaining cost control while delivering self-service capabilities to developers. Rafay, a silver sponsor at this year’s event, is addressing these pain points head-on with its infrastructure orchestration and workflow automation platform designed to turn disparate resources into unified, enterprise-grade clouds.
In this exclusive VMblog interview, Mohan Atreya, Chief Product Officer at Rafay, shares how the company is helping enterprises, cloud service providers, and sovereign AI clouds move beyond stitching together hundreds of open-source components to achieve true operational efficiency. From enabling organizations to manage thousands of clusters with platform teams as small as 3-4 engineers to becoming the only NVIDIA-certified reference architecture for GPU PaaS, Rafay is demonstrating measurable business impact in the crowded cloud-native marketplace. Attendees can visit booth 1650 to discover how leading organizations are operationalizing platform engineering and AI infrastructure at scale while maintaining security, governance, and cost optimization.
VMblog: Can you give us your elevator pitch? What key message will attendees hear from you at KubeCon NA 2025, and what actionable insights will they take back to influence their management teams?
Mohan Atreya: Rafay is the infrastructure orchestration and workflow automation platform that helps enterprises, cloud service providers, and sovereign AI clouds turn fragmented AI Infrastructure resources into a self-service GPU/AI Cloud delivering a hyperscaler class experience. Rafay is purpose-built for AI.
VMblog: As a sponsor of KubeCon + CloudNativeCon North America 2025, what sponsorship level have you chosen, and what strategic objectives drove this investment?
Atreya: Rafay is a silver level sponsor and we can be located in booth 1650. As a CNCF member and an organization that started strictly in the Kubernetes management space, spending time with the community at KubeCon has always been a priority. Our goal is to make Kubernetes, and really all infrastructure easily manageable, which makes an appearance at KubeCon a natural fit.
VMblog: Can you dive deeper into your company’s core technologies? What specific challenges do you solve for KubeCon attendees in their day-to-day operations?
Atreya: Rafay provides an enterprise-grade platform that simplifies how platform engineering teams manage, secure, and scale clusters across clouds, data centers, and edge environments. Our core technologies automate cluster lifecycle management, enforce governance and security, and enable developer self-service through a design-first platform engineering model. We also help organizations run AI and GPU workloads efficiently with our GPU PaaS capabilities. In short, Rafay removes the operational friction of Kubernetes so teams can focus on delivering applications faster and more securely.
VMblog: In an increasingly saturated cloud-native marketplace, what distinguishes your solution in late 2025? What is your unique value proposition?
Atreya: Rafay stands out in the crowded cloud-native landscape by offering a solution for Kubernetes and AI infrastructure that unifies operations across cloud, data center, and edge. While many tools focus on specific stages of the lifecycle, Rafay delivers an end-to-end platform that enables secure, automated, and compliant platform operations at scale – without requiring teams to stitch together and maintain 100s of open-source components.
Our unique value proposition lies in empowering platform engineering teams to provide self-service, policy-driven environments for developers while maintaining enterprise-grade security, governance, and cost control. In 2025, we’ve extended this leadership into GPU and AI infrastructure orchestration.
VMblog: With GenAI workloads and LLM deployments reshaping cloud-native architectures, how does your solution address these AI infrastructure demands?
Atreya: With a GPU PaaS enabled by the Rafay platform, organizations can automate the deployment of LLM pipelines, manage hybrid GPU fleets, and enforce quotas, cost controls, and security guardrails – all through a unified control plane. This empowers teams to focus on building and optimizing AI models rather than managing complex infrastructure, ensuring agility, efficiency, and compliance in today’s rapidly evolving AI-driven landscape.
VMblog: What is your executive pitch for CTOs and CIOs? How do you demonstrate measurable business impact and ROI?
Atreya: The Rafay platform unifies fragmented infrastructure into secure, self-service environments. Developers and data scientists can consume resources on-demand, enabling enterprises, cloud service providers and Sovereign Clouds to scale infrastructure and the environments within. The result: Infrastructure becomes a launchpad for innovation, not a barrier. Here are some of our customer proof points:
- Improved Productivity: Gained 20�25% of developer time back.
- Optimized Efficiency: The ability to maintain thousands of clusters and pipelines with platform teams as small as 3-4 engineers.
- Moved Faster: Launched a self-service, multi-tenant capable GPU Cloud in less than a quarter.
- Gained Credibility: Adopted an industry-leading solution with the Rafay Platform, the only NVIDIA-certified reference architecture for GPU PaaS.
VMblog: With platform engineering gaining momentum, how do you support organizations building internal developer platforms and improving developer experience?
Atreya: Rafay enables organizations to accelerate their platform engineering journey by providing the foundation for self-service, secure, and scalable internal developer platforms. Instead of building and maintaining complex automation from scratch, teams use Rafay to define standardized blueprints that abstract infrastructure complexity and let developers deploy what they need quickly and safely. The result is a streamlined developer experience – faster environment provisioning, built-in security and policy compliance, and less operational overhead – allowing engineers to innovate without being slowed down by Kubernetes or infrastructure management.
VMblog: How is your company addressing the intensifying focus on cloud cost optimization and FinOps in cloud-native environments?
Atreya: Rafay helps organizations take a proactive, operational approach to cloud cost optimization by embedding FinOps principles directly into platform operations.Through unified visibility across clusters, workloads, and environments, teams can easily identify underutilized resources, right-size infrastructure, and track spend by team, project, or environment.
Rafay’s policy-based automation allows platform teams to enforce cost controls, such as scaling policies, time-based shutdowns, and GPU quota management, without compromising developer agility. By combining cost insights with governance and automation, Rafay enables enterprises to achieve continuous cost efficiency while maintaining the performance and flexibility their applications demand.
VMblog: What is your top recommendation for attendees to maximize their KubeCon experience and learning outcomes?
Atreya: Our top recommendation is to go beyond the sessions and use KubeCon as an opportunity to connect directly with practitioners who are solving real-world challenges similar to yours. Attend hands-on demos, join deep-dive conversations at booths, and seek out birds-of-a-feather or community sessions where platform teams openly share lessons learned. The most valuable insights often come from peer exchange, not just keynotes.
And of course, stop by Rafay’s booth to explore how leading organizations are operationalizing platform engineering, AI infrastructure, and Kubernetes at scale, and walk away with practical strategies you can bring back to your own teams.
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