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Services03

Paved roads, not ticket queues.

When every new service needs a ticket, a meeting and a week of infrastructure work, the platform team becomes the constraint. We design and build internal platforms that turn common requests into self-service, with delivery, observability and security already wired in.

team ateam bteam cteam dgolden pathsSELF-SERVICEplatformCI/CD · K8S · OBSERVABILITY · POLICYcloudIAC-MANAGED
fig. 03 — platform engineering

Challenges

The problems we usually find.

  1. Infrastructure requests as a bottleneck

    Product teams wait on a small group of infrastructure engineers for databases, environments and pipelines. Lead time is dominated by queueing, not work.

  2. Every team solves the same problems

    Each service has its own deployment approach, logging setup and security posture. Knowledge does not transfer and incidents look different every time.

  3. Kubernetes complexity leaking to developers

    Application engineers are expected to understand manifests, ingress, resource limits and network policies to ship a simple service.

  4. A platform nobody adopted

    An internal platform was built, but it was harder to use than the workaround. Adoption stalled and now there are two ways to do everything.

  5. Alerts nobody trusts

    Hundreds of alerts fire every week, most of them unactionable, while logs, metrics and traces live in separate tools that do not connect.

  6. No shared definition of reliable

    Without service level objectives, reliability debates are subjective and it is unclear when to slow feature work to invest in stability.

Capabilities

What we build and change.

  • Platform strategy and scope

    Identify the interactions worth productizing, define the platform’s interfaces and decide what stays with product teams. The platform is a product with users.

  • Golden paths and service templates

    Opinionated starting points for new services with build, deploy, observability, security and documentation already wired in.

  • Developer self-service

    Databases, queues, buckets and environments requested through reviewed definitions or a portal, backed by the same infrastructure code the platform team maintains.

  • Kubernetes and container orchestration

    Cluster topology, multi-tenancy, ingress, autoscaling, policy and GitOps delivery, with abstractions that keep application manifests small.

  • Observability

    OpenTelemetry instrumentation, Prometheus and Grafana built around service health and user journeys, and telemetry pipelines that keep cost under control.

  • Reliability engineering

    Service level objectives, burn-rate alerting that pages only when it matters, runbooks linked from every alert and failure exercises before a real outage.

  • AI infrastructure

    GPU node pools, inference serving and a governed gateway to hosted and self-hosted models, run on the same platform, pipelines and controls as everything else.

Engagements

Where engagements usually start.

  • A

    Starting a platform team

    An organization has decided to invest in a platform. It needs a scope, a first golden path and a delivery plan that shows value early.

  • B

    Rescuing adoption

    An existing platform is underused. It needs user research, simplification and migration support rather than more features.

  • C

    Standardizing on Kubernetes

    Services run on a mix of virtual machines, PaaS and containers. The organization wants one runtime without making every team a Kubernetes expert.

  • D

    Observability and SLOs from scratch

    A growing system has basic monitoring but no tracing or reliability targets. Incidents take too long to understand and on-call is exhausting.

Technologies

Tools we use here.

  • KubernetesCluster design, multi-tenancy, autoscaling and policy.
  • DockerContainer images that are small, reproducible and secure by default.
  • Argo CDGitOps delivery to Kubernetes with progressive rollouts.
  • TerraformModule design, state architecture and automated plan and apply.
  • OpenTelemetryVendor-neutral traces, metrics and logs with shared context.
  • PrometheusMetrics collection, recording rules and SLO-based alerting.
  • GrafanaService dashboards organized around health and user journeys.
  • GitHub ActionsReusable workflows, OIDC to cloud providers and hardened runners.
  • vLLMSelf-hosted open-weight models with continuous batching on GPU nodes.
  • LLM APIsOpenAI, Anthropic, Amazon Bedrock and Azure OpenAI behind one gateway, with evaluation and cost tracking.

Approach

How we work on it.

  1. 01

    Talk to the users

    Interview product engineers, measure where time goes and identify the few workflows where a platform would remove the most friction.

  2. 02

    Design the interface first

    Decide what developers declare and what the platform decides for them. A small, stable interface matters more than a long feature list.

  3. 03

    Ship a thin slice

    Deliver one complete golden path end to end, with telemetry and alerts included, and move a real service onto it before broadening scope.

  4. 04

    Iterate on adoption

    Track usage, friction and reliability, remove the reasons teams work around the platform, and retire the old paths deliberately.

Other practice areas

Contact

Let’s build better systems

Whether you’re modernizing infrastructure, improving delivery pipelines, or building a new engineering platform, let’s talk.

Start a conversation

Or write to contact@enhanceit.dev