As organisations add services, teams and cloud accounts, every squad ends up solving the same problems: pipelines, environments, secrets, observability, access, cost tags. Some solve them well. Most solve them differently. Over time, the inconsistency becomes the main source of incidents and onboarding pain.
Platform engineering is the response: a team that builds an internal developer platform so product teams can ship without reinventing infrastructure. Done well, it removes friction. Done badly, it becomes another ticket queue.
Treat the platform as a product
The platform has customers — your developers. That means:
- User research. Talk to teams about where they lose time. Watch a new engineer try to ship their first change.
- A roadmap. Prioritise by developer pain and business risk, not by what the platform team finds interesting.
- Adoption as the metric. If teams route around the platform, it is not solving their problem.
- Documentation and support. A golden path nobody can find is not a path.
Mandates can force adoption, but they hide product problems. Earn usage first; standardise once the paved road is clearly better.
Paved roads, not walls
A paved road is the supported, opinionated way to do a common task: create a service, deploy it, add a database, expose an API. It comes with sensible defaults for security, logging, monitoring and cost allocation already wired in.
Good paved roads:
- Get a new service from template to production quickly, with tests and pipelines included
- Encode policies as code rather than review meetings
- Leave escape hatches for teams with genuine special needs, with clear ownership if they take them
- Are versioned, so improvements reach existing services rather than only new ones
The aim is that the easy way and the right way are the same way.
What an internal developer platform usually includes
The exact shape depends on your stack, but common building blocks are:
- Service templates with build, test and deploy pipelines
- Environment provisioning through infrastructure as code, including short-lived preview environments
- A service catalogue showing ownership, dependencies, runbooks and on-call
- Secrets and identity handled centrally, with no credentials in repositories
- Observability defaults — logs, metrics and traces on from the first deploy
- Policy checks for security, compliance and cost tagging in the pipeline
- A portal or CLI as the front door, though the interface matters less than the workflows behind it
Tools such as Kubernetes, Terraform or a developer portal framework are building blocks. The platform is the set of opinions and workflows you put on top.
Measure developer experience
Pick a few measures and track them over time:
- Time for a new engineer to ship a first change
- Lead time from commit to production
- Deployment frequency and change failure rate
- Time to restore after incidents
- Share of services on the current paved road
Combine numbers with regular developer surveys. Quantitative data shows where; conversations show why.
AI workloads belong on the platform too
Teams building AI features need model endpoints, vector stores, evaluation pipelines, prompt versioning and GPU capacity. If the platform ignores them, each team builds its own, and governance fragments again. Extend paved roads to cover AI services with logging, evaluation and cost tags included.
Common failure modes
- Building a portal before fixing the underlying workflows
- A platform team too small to support what it launched
- No deprecation plan, so old patterns live forever
- Mandating adoption of an immature platform
Where to start
- Interview several product teams and list their top shared pain points
- Pick one high-frequency workflow, such as creating and deploying a new service
- Build a paved road for it with security and observability included
- Measure adoption and time saved, then choose the next workflow
Incubics helps teams design and build internal platforms as products, with handover so your engineers own them.