Platform Engineering Needs a Cloud Engine

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. YAML sprawl, cluster life-cycle management, networking dependencies, and security controls consume time that should be spent improving the developer experience and can lead to costly human error.

As a result, many platform engineering teams find themselves serving as infrastructure integrators rather than product engineering teams. They spend cycles wiring systems together, maintaining custom automation, and resolving edge cases between environments. What’s needed is access to solutions that provide an automated infrastructure layer, allowing platform engineers to focus on productizing the platform rather than assembling it.

Platform engineering teams need the ability to standardize the creation and governance of Kubernetes clusters, namespaces, virtual machines, networks, VPCs, and load balancers using a consistent declarative model. Further guardrails should be built into policy (e.g., policy-as-code) rather than enforced manually. This approach enables self‑service, allowing developers to request what they need, while platform engineering teams retain control over security, compliance, and resource usage.

This abstraction becomes especially important as organizations pursue AI‑powered workloads and agentic applications. Many of these workloads are built on Kubernetes but have requirements that make public cloud less attractive, such as data gravity, latency, regulatory constraints, and cost volatility. At the same time, developers expect the same on‑demand experience they get in public clouds. Further, as organizations experiment with agentic applications, they need secure access to models, runtimes, and supporting services without exposing sensitive data or overburdening platform teams.

Rather than functioning as an internal service desk or an infrastructure assembly line, platform engineering teams can adopt a product-led growth mindset, treating the platform itself as a product with defined outcomes and road maps. Success is measured by platform adoption, developer productivity, and time-to-value via self-service rather than ticket-based metrics or scripts created. IDC’s Platform Engineering and DevOps Survey revealed that developer productivity and velocity were the top metrics platform engineering teams use to measure success.

Many organizations are repatriating latency‑sensitive workloads from public clouds and scaling AI‑driven applications on premises. IDC’s recent PaaS Decision-Maker and Business Value Survey revealed that 71% of enterprises are moving nearly 24% of their public cloud workloads on premises. These organizations need a modern private cloud platform that delivers public-cloud-like self‑service without forcing developers to become infrastructure experts, while also allowing them to customize configurations as needed.

Message from the Sponsor

Effective platform engineering requires a private cloud infrastructure that functions as a reliable engine for Internal Developer Platforms (IDPs). By unifying compute, storage, networking, and Kubernetes management into a single automated vertically integrated stack with a unified consumption experience, VMware Cloud Foundation abstracts the complexity of underlying operations. This approach enables teams to shift from manually integrating components to delivering curated, self-service resources with embedded governance and policy. To learn more about how a modernized private cloud platform supports scalable platform engineering and developer productivity, visit vmware.com/platformengineering

To learn more about how platform engineering teams can overcome Kubernetes complexity and deliver a scalable, secure private cloud experience, read IDC’s spotlight, Enabling Platform Engineers to Overcome Kubernetes Complexity with a Modern Private Cloud Platform.