Internal Developer Platform vs. More DevOps Engineers: The Build-or-Hire Decision

Gartner projects that 80% of software engineering organisations will have established platform engineering teams by 2026, positioning internal developer platforms (IDPs) as the default answer to developer friction. That number is now the justification on a hundred hiring plans. The trouble is that it answers the wrong question. "Should we adopt platform engineering?" is settled. "Should we build a permanent team to build our platform?" is not — and that is the decision actually sitting in front of most Heads of Engineering.

The two questions have very different cost profiles. A capable IDP team at enterprise scale — a platform lead, two or three senior platform engineers, someone who owns the developer experience — lands somewhere between $500k and $2M per year fully loaded, and it is a permanent line on the budget. You are not buying a platform. You are buying the standing capacity to build and run one indefinitely. That may be the right call. But a large share of teams reach for the headcount because they have conflated the artefact with the org design.

Finding 1: Most of the platform value is in the first eight weeks, not the standing team

The hard, high-value work of an IDP is front-loaded: choosing the golden paths, wiring a service catalogue, standardising CI templates, getting telemetry flowing so developers can see their own services. A modern stack — Backstage for the catalogue and developer portal, OpenTelemetry for instrumentation, Grafana for the dashboards on top — is well-trodden ground with strong documentation and reference architectures. The scarce ingredient is senior judgement about *which* paths to pave for *your* codebase, not raw platform-engineering headcount held forever.

The standing team's real job, once the platform exists, is operation and incremental improvement — genuine work, but a different and usually smaller shape than the build. When leaders size the permanent team against the build effort, they over-hire for a phase that is nearly over by the time the new joiners have onboarded.

Action: Before you open the requisitions, separate the two budgets explicitly. Write down what the *build* costs (a fixed-duration effort with a defined output) and what the *run* costs (the steady-state team). If the run cost is small relative to the build, you have a project to deliver, not necessarily a team to grow.

Finding 2: Platform value is realised only when developers actually use the golden path

An IDP that ships and then sits unused is a common and expensive failure. The evidence that platform engineering works is inseparable from adoption: the DORA research programme repeatedly ties improved delivery outcomes to reducing friction developers *experience*, not to the existence of tooling. If your engineers keep hand-rolling their own pipelines and bypassing the catalogue, you have built a museum, not a platform.

Cloudflare's account of migrating cdnjs — nine billion requests a day — onto its own Developer Platform is instructive here precisely because it is a dogfooding story. The platform earned its place by carrying a real, demanding workload owned by the team that built it. That is the bar: a platform proves out by absorbing production traffic and real developer workflows, not by passing an internal demo. Adoption is a design constraint from day one, not a rollout afterthought.

Action: Pick one real service — ideally one your best engineers respect — and make it the first tenant of the golden path this quarter. Measure onboarding time and change lead time for that service before and after. If the path does not visibly beat the status quo for that team, fix the path before you scale the effort or the headcount.

Finding 3: Predictive infrastructure optimisation is now part of the platform, and it needs guardrails

The cost story does not end at team salaries. The platform sits on infrastructure you are almost certainly over-provisioning. Kubernetes resource management is the classic offender: to stay safe, engineers reserve CPU and memory they never use, and the built-in autoscalers are reactive — they respond only after a threshold is crossed, adding lag and, worse, sometimes masking a leaking workload by simply granting it more memory. Recent work on safety-gated autoscaling for Kubernetes vertical resource optimisation frames the fix well: predictive right-sizing is valuable, but it needs a multi-layered safety gate so the optimiser cannot paper over a defect or starve a workload in pursuit of savings.

The point for a build-or-hire decision is that cost control is a platform capability, not a separate FinOps afterthought. An IDP that bakes in sensible resource defaults and observable spend gives you leverage a pile of extra DevOps hires never will. This is where platform reliability and cloud cost work belong together — the same telemetry that tells developers how their services behave tells finance where the money goes.

Action: Instrument spend at the service level before you optimise it. Turn on cost visibility per team and per service using the telemetry the platform already emits, and treat any predictive right-sizing as gated — bounded by safety limits and reviewed — rather than fully automatic. You cannot govern what you cannot see, and you should not automate what you cannot yet trust.

Reframing the decision

The honest version of the build-or-hire question is: do you need a permanent team, or do you need the platform built well and handed back to a smaller team that runs it? For a lot of 50-to-500-engineer organisations, the answer is the latter. The build is a bounded, senior effort. The run is a modest standing responsibility that your existing infrastructure engineers can absorb once the golden paths are clear and the ownership is transferred cleanly.

The risk in the hire-first approach is not just cost. It is that a green team learning platform engineering on your dime builds the platform they can build, not the one your codebase needs, and then the sunk cost of their salaries keeps a mediocre platform alive. The risk in the naive buy approach — a tool vendor's reference deployment dropped in and abandoned — is the unused museum. What you want sits between: senior delivery, adoption designed in, and a deliberate handover.

How Anystack approaches this

This is a natural fit for a fixed-duration engagement rather than a permanent hire. A qualified engineering pod — senior engineers only, delivering into your codebase — installs a Backstage, OpenTelemetry and Grafana stack against your golden paths over roughly eight weeks, migrates one real service onto it as the first tenant, and transfers ownership to your team with the operational runbook. Because the pod measures test effectiveness and puts changes through adversarial review inside delivery, what reaches your production is evidenced against your bar, not just handed over as configured tooling. When the eight weeks are done, you are left with a platform your engineers use and a run cost you sized on purpose — not a permanent team you hired to answer a question you had not yet finished asking.