Platform Engineering for Scale-Ups Without a Dedicated Team

As engineering teams grow past a handful of squads, developers start losing time to things that have nothing to do with the product: figuring out how to provision a database, chasing down which Kubernetes namespace to deploy into, or reverse-engineering a colleague's CI pipeline because there's no standard one. This is the point where many Australian scale-ups start hearing the term "platform engineering" — and assume it means hiring a specialist team they can't yet afford. It doesn't have to.

What is platform engineering?

Platform engineering is the discipline of building and maintaining the internal tooling, infrastructure, and workflows that let product engineers ship software with less friction. It treats the developer experience itself as a product, with the engineering org as the customer. The goal is not infrastructure for its own sake — it's reducing the cognitive load a developer carries just to get code into production safely.

What is an internal developer platform (IDP)?

An internal developer platform (IDP) is the layer of self-service tooling — templates, APIs, dashboards, and automation — that sits between raw infrastructure (cloud accounts, Kubernetes clusters, CI/CD systems) and the engineers who need to use it. A good IDP lets a developer spin up a new service, environment, or database with a few clicks or a single command, without needing to understand the underlying cloud configuration. It doesn't have to be a bespoke product with a dedicated team behind it — for many scale-ups, an IDP starts as a curated set of templates, scripts, and conventions layered on tools you already run.

What are 'golden paths' and why do they matter?

A golden path is the officially recommended, well-supported way of doing a common engineering task — spinning up a new service, adding a database, deploying to production — that is easier to follow than to deviate from. The concept originated at Spotify and has since become a standard pattern in platform engineering. Golden paths reduce decision fatigue: instead of every team choosing its own logging library, deployment strategy, or secrets manager, there's one well-documented, well-supported path that covers most use cases, with room for genuine exceptions.

Golden paths work because they make the right choice the easy choice. A new engineer who follows the path gets a working, secure, observable service by default — without needing tribal knowledge or a Slack message to a senior engineer.

Do you need a dedicated platform team to start?

No — most scale-ups do not need a dedicated platform team to get real benefit from platform engineering practices. What matters more is having one or two senior engineers, often already in a tech lead or staff role, who own the golden paths part-time and treat internal tooling as a first-class responsibility rather than an afterthought squeezed between feature work.

The risk of waiting for a "proper" platform team is that inconsistency compounds. Every month without a golden path is another month of teams solving the same deployment, observability, or environment-provisioning problem slightly differently, which becomes expensive to unwind later. The risk of over-investing too early is building generalised, flexible tooling for problems you don't have yet. The right starting point is usually narrower than teams expect: pick the two or three workflows that cause the most friction — service creation, deployment, and environment provisioning are common starting points — and standardise those first.

How do you build an IDP without a platform team?

You build a thin IDP by codifying your best existing practice into reusable templates and layering self-service automation on top of tools you already run, rather than building new infrastructure from scratch. This is achievable with existing CI/CD, cloud, and infrastructure-as-code tooling — the discipline is in curation and documentation, not new technology.

Practical patterns that work without a dedicated function:

• Service templates, not service builders. A version-controlled starter template (repo structure, CI pipeline, base observability, security defaults) that a new service is cloned from gets you most of the benefit of a platform without building a platform product.
• Infrastructure-as-code modules as the golden path. Standard, reviewed Terraform or equivalent modules for common resources (databases, queues, storage) mean teams provision infrastructure consistently without a ticket-based request process.
• One deployment pipeline pattern. Rather than every team building its own CI/CD from scratch, maintain a shared pipeline template that teams adopt and extend, rather than replace.
• Documentation as the interface. A lightweight internal portal — even a well-maintained wiki or a tool like Backstage — that lists golden paths, ownership, and runbooks does a lot of the work that a bespoke platform team would otherwise be asked to do manually.
• Lean on managed platforms where they fit. For AI and ML workloads specifically, hyperscaler-managed platforms — for example, Google's Vertex AI, which bundles managed runtimes, vector search, feature stores, and MLOps tooling — can remove the need to build bespoke internal infrastructure for that domain entirely. The trade-off is less control and some vendor coupling, but for a scale-up without spare platform capacity, it is often the more defensible choice than building the equivalent in-house.

Build vs. adopt: how should scale-ups think about the trade-off?

The right approach depends on how much platform capacity you have relative to how many teams need to be served. There is no universally correct answer — the table below is a qualitative guide, not a scorecard.

Approach	Best fit	Trade-off
Ad hoc (no golden paths)	Very small teams, early-stage products	Fast short-term, but inconsistency compounds as headcount grows
Thin IDP (templates + IaC modules, part-time ownership)	Most scale-ups, 20-150 engineers	Low investment, meaningful reduction in cognitive load, requires ongoing curation
Managed hyperscaler platform for a specific domain (e.g. Vertex AI for ML)	Teams building AI/ML features without deep MLOps capability	Faster time to value, some vendor coupling, less low-level control
Dedicated platform team + custom IDP product	Large scale-ups and enterprise, high team count	Highest investment and control, only justified once demand from product teams is proven

Most Australian scale-ups sit in the second or third row for longer than they expect. A dedicated platform team typically only earns its keep once you have enough product engineering teams that inconsistency itself has become a measurable drag on delivery — a threshold worth revisiting deliberately rather than assuming.

When does it make sense to hire a dedicated platform function?

It makes sense to formalise a platform team when the informal, part-time ownership model starts to break down — when the engineers maintaining golden paths can no longer keep up with demand, when infrastructure decisions are being made inconsistently across teams, or when onboarding new engineers takes noticeably longer than it should because there's no single, current source of truth for how things are built and deployed.

This is also often the point where scale-ups bring in external help to assess what's actually needed, rather than defaulting to a large hire. A short technical architecture review can clarify whether the gap is genuinely platform capacity, or whether it's a legacy system that needs modernisation before any platform investment will pay off.

Where does this connect to data and AI infrastructure?

As scale-ups add AI features and data products, the same golden-path thinking extends naturally to data infrastructure and AI engineering — standardising how models get deployed, monitored, and retrained is just platform engineering applied to a newer workload type. Teams that already have solid golden paths for standard services tend to extend AI workloads onto the platform far more smoothly than teams starting from scratch.

For more on how to sequence infrastructure investment against AI ambitions, see our related posts on our insights page.

Getting started without overbuilding

If you're weighing up whether your engineering org needs a formal platform team or whether a thin, well-curated set of golden paths will carry you further, we can help — it's exactly the kind of pragmatic architecture question we work through with growing Australian engineering teams. Get in touch for a conversation about where your team actually sits on that curve.