Platform Engineering Needs a Cloud Engine

Message from the Sponsor Discover more from VMware Cloud Foundation (VCF) Blog Related Articles Model Gallery: How to Use JupyterLab Notebooks to Simplify Model Deployment and Management Mastering Application Migration to VKS: Patterns and Best Practices Automic Automation: Application-Aware Automation for the Private Cloud Sponsored By: Broadcom Guest IDC Blogger: Jim Mercer Date: 02.04.26 Platform engineers are increasingly expected to deliver cloud-like experiences across all environments, including on-premises infrastructure. Public cloud platforms have set a high bar for self-service, automation, and speed, but on-premises environments continue to offer critical advantages, such as predictable performance, data sovereignty, cost control, and deep integration with existing systems, making them indispensable for many organizations. Developers now bring to on-premises platforms expectations shaped by the cloud, such as fast access to environments, consistent configurations, and infrastructure consumed through APIs and automation rather than tickets. To fully realize the value of on-premises platforms, organizations must equip developers with the same modern tools, workflows, and abstractions that enable productivity in the public cloud. The consequence of failing to do so is not just a gap in expectations, but increasing friction that slows delivery, erodes platform trust, and limits the ability to scale. This pressure is intensifying as platform engineering practices mature. Internal developer platforms (IDPs) are no longer experimental; they are becoming foundational. IDC’s latest Platform Engineering and DevOps Survey showed that 93% of organizations are piloting, using, expanding the use of, or planning to use an IDP within the next year. However, building an IDP is rarely about a single portal or tool. It requires integrating infrastructure, orchestration, governance, and life-cycle management into a cohesive product. Kubernetes adds another layer of complexity for platform engineering teams, introducing architectural complexities that require a deep understanding of containers, networking, storage, and cluster security protocols. While it has become the default runtime for modern applications, managing Kubernetes at scale alongside existing VM‑based workloads can overwhelm platform engineering teams.