At the end of last year, I had the privilege of attending a session organised by Red Hat where they shared their Asia Pacific roadmap with the tech analyst community. The company’s approach of providing a hybrid cloud application platform centred around OpenShift has worked well with clients who favour a hybrid cloud approach. Going forward, Red Hat is looking to build and expand their business around three product innovation focus areas. At the core is their platform engineering, flanked by focus areas on AI/ML and the Edge.
The Opportunities
Besides the product innovation focus, Red Hat is also looking into several emerging areas, where they’ve seen initial client success in 2023. While use cases such as operational resilience or edge lifecycle management are long-existing trends, carbon-aware workload scheduling may just have appeared over the horizon. But two others stood out for me with a potentially huge demand in 2024.
GPU-as-a-Service. GPUaaS addresses a massive demand driven by the meteoric rise of Generative AI over the past 12 months. Any innovation that would allow customers a more flexible use of scarce and expensive resources such as GPUs can create an immediate opportunity and Red Hat might have a first mover and established base advantage. Particularly GPUaaS is an opportunity in fast growing markets, where cost and availability are strong inhibitors.
Digital Sovereignty. Digital sovereignty has been a strong driver in some markets – for example in Indonesia, which has led to most cloud hyperscalers opening their data centres onshore over the past years. Yet not the least due to the geography of Indonesia, hybrid cloud remains an important consideration, where digital sovereignty needs to be managed across a diverse infrastructure. Other fast-growing markets have similar challenges and a strong drive for digital sovereignty. Crucially, Red Hat may well have an advantage where onshore hyperscalers are not yet available (for example in Malaysia).
Strategic Focus Areas for Red Hat
Red Hat’s product innovation strategy is robust at its core, particularly in platform engineering, but needs more clarity at the periphery. They have already been addressing Edge use cases as an extension of their core platform, especially in the Automotive sector, establishing a solid foundation in this area. Their focus on AI/ML may be a bit more aspirational, as they are looking to not only AI-enable their core platform but also expand it into a platform to run AI workloads. AI may drive interest in hybrid cloud, but it will be in very specific use cases.
For Red Hat to be successful in the AI space, it must steer away from competing straight out with the cloud-native AI platforms. They must identify the use cases where AI on hybrid cloud has a true advantage. Such use cases will mainly exist in industries with a strong Edge component, potentially also with a still heavy reliance on on-site data centres. Manufacturing is the prime example.
Red Hat’s success in AI/ML use cases is tightly connected to their (continuing) success in Edge use cases, all build on the solid platform engineering foundation.
