Platform Engineering Services: Building Your Internal Developer Platform

What Is Platform Engineering as a Service?

Platform engineering as a service is an engagement model where an external team — with deep expertise in developer platforms, cloud-native tooling, and developer experience — designs, builds, and optionally operates an Internal Developer Platform for your organization. It provides the outcomes of a mature platform team without the 12-18 month ramp required to build one from scratch.

The Internal Developer Platform (IDP) sits between your application developers and the underlying infrastructure. Instead of developers writing Terraform, configuring Kubernetes manifests, setting up CI/CD pipelines, and managing secrets for each new service, they interact with a self-service portal that provisions everything through standardized templates — what the industry calls “golden paths.”

Think of it as the difference between buying a car and building one from parts. Application developers should be driving (shipping features), not assembling engines (configuring infrastructure). A platform engineering service builds the factory that produces the cars.

What a Platform Engineering Service Delivers

Platform Engineering vs DevOps vs SRE

These three disciplines are complementary, not competing. Understanding how they relate helps you staff correctly and set expectations for what platform engineering services will and will not cover.

DevOps is a cultural movement that broke down the wall between development and operations. It introduced practices like CI/CD, IaC, and monitoring as code. The limitation: as organizations scale past ~50 engineers, the “you build it, you run it” model creates unsustainable cognitive load. Every developer needs to understand Kubernetes, Terraform, networking, security, and observability — on top of their application domain.

SRE (Site Reliability Engineering) addresses reliability specifically. SRE teams define SLOs, manage error budgets, and absorb operational burden so application teams can focus on features. The limitation: SRE teams often become bottlenecks because every team needs SRE support but there are not enough SREs to go around.

Platform Engineering takes a product-management approach to infrastructure. Instead of embedding DevOps or SRE expertise in every team, a platform team builds a product (the IDP) that encodes that expertise as self-service capabilities. Application developers consume the platform the same way they consume a cloud provider — through well-documented APIs and interfaces. The platform team treats developers as customers, prioritizing usability, documentation, and adoption metrics.

Core Capabilities of an Internal Developer Platform

Every IDP is unique to its organization, but successful platforms share a common set of capabilities organized into four layers. Platform engineering services help you design and build each layer in the right sequence.

1. Developer Portal and Service Catalog

The developer portal is the front door to your platform. Built on Backstage (the CNCF-backed framework originally created by Spotify), it provides a unified catalog of all services, APIs, documentation, team ownership, dependencies, and operational status. Engineers can discover existing services before building new ones, understand who owns what, and access runbooks during incidents.

A well-implemented Backstage portal includes software templates for golden paths, TechDocs for documentation-as-code, Kubernetes and CI/CD plugins for deployment visibility, cost dashboards per team and service, and security scorecards showing vulnerability and compliance status. The portal becomes the single entry point for every developer workflow.

2. Golden Paths and Service Templates

Golden paths are opinionated, pre-configured templates that encode organizational best practices for common workloads. Rather than prescribing a single way to build every service, golden paths provide the “paved road” that makes doing the right thing the easiest thing. Developers can deviate when necessary, but the default path includes everything: project scaffolding, CI/CD configuration, Dockerfile, Kubernetes manifests, Terraform for dependent resources, observability instrumentation, and security controls.

Typical golden paths include: REST API service (Node.js/Go/Python), event-driven consumer (Kafka/SQS), scheduled batch job, ML model serving endpoint, and frontend application. Each template is version-controlled, tested in CI, and documented in the portal. When a best practice changes (e.g., upgrading to a new base image version), the golden path is updated and teams are notified through the portal.

3. Self-Service Infrastructure Provisioning

Self-service infrastructure eliminates the ticket-driven provisioning model where developers wait days or weeks for a database, cache, or environment. Using tools like Crossplane (Kubernetes-native infrastructure provisioning) or Terraform with custom abstractions, the platform exposes infrastructure as simple API objects that developers request through the portal or YAML manifests.

A developer who needs a PostgreSQL database submits a request specifying only the essentials — size class, backup frequency, and environment. The platform handles VPC placement, encryption configuration, IAM roles, monitoring setup, and backup automation behind the scenes. The database appears in the developer's environment within minutes, fully configured and wired up. This is the platform engineering equivalent of ordering from a menu versus cooking from scratch.

4. GitOps Pipelines and Deployment Automation

Every service deployed through the platform uses a standardized GitOps workflow: code changes trigger CI (build, test, scan), successful builds produce immutable artifacts, and ArgoCD continuously reconciles the desired state in Git with the actual state in Kubernetes. Developers merge to main and the platform handles everything else — no manual kubectl commands, no environment-specific scripts, no deployment runbooks.

Advanced platforms implement progressive delivery: canary deployments that route 5% of traffic to the new version, automated rollback if error rates exceed thresholds, and promotion gates that require passing integration tests before reaching production. ArgoCD ApplicationSets enable the same application to be deployed across multiple clusters and environments with a single Git commit.

Platform Engineering Tooling Landscape

The CNCF landscape contains over 1,500 projects. A platform engineering service helps you choose the right combination for your context rather than assembling an overwhelming stack. Here are the tools that form the backbone of most successful platforms:

Backstage: The Developer Portal Standard

Backstage has become the de facto standard for developer portals since its open-source release by Spotify in 2020 and graduation to a CNCF incubation project. Its plugin architecture allows integration with virtually any tool in your stack — from CI/CD and Kubernetes to cost management and security scanning. The software templates feature powers golden paths, enabling developers to scaffold new services with a few clicks. TechDocs converts markdown into searchable documentation hosted directly in the portal. For platform engineering services, Backstage is almost always the starting point.

ArgoCD: GitOps Continuous Delivery

ArgoCD is the GitOps engine that ensures what is defined in Git matches what is running in Kubernetes. It continuously monitors Git repositories and automatically (or manually, depending on your sync policy) reconciles drift. ApplicationSets allow templated deployments across multiple clusters, while the App of Apps pattern enables managing hundreds of applications through a single root application. ArgoCD pairs naturally with Backstage — the ArgoCD plugin shows deployment status directly in the developer portal.

Crossplane: Kubernetes-Native Infrastructure

Crossplane extends Kubernetes with Custom Resource Definitions (CRDs) that represent cloud infrastructure — databases, caches, queues, buckets, and more. Platform teams define Compositions that abstract the complexity of provisioning an RDS instance into a simple, developer-friendly API. Developers request a “Database” with size “medium” and the Composition handles all the AWS-specific configuration, IAM roles, VPC placement, and monitoring setup. Crossplane is the key enabler of self-service infrastructure provisioning on the platform.

Platform Maturity Model: Foundation to Autonomous

Platform engineering is not a one-time project — it is an ongoing product development effort. The following maturity model helps you set expectations and plan investments across four stages:

Stage 1: Foundation (Months 1-3)

Stage 2: Standardized (Months 3-6)

Stage 3: Self-Service (Months 6-12)

Stage 4: Autonomous (Months 12-18+)

Build vs Buy: Decision Framework

One of the most consequential decisions in platform engineering is how much to build in-house versus adopting commercial or managed solutions. The answer is almost always a hybrid — but the ratio depends on your organization's scale, constraints, and strategic priorities.

The Hybrid Approach (Recommended): Use open-source foundations (Backstage, ArgoCD, Crossplane) for the core platform, integrate best-of-breed commercial tools for specific capabilities (Datadog for observability, Snyk for security scanning), and engage platform engineering services to accelerate the build, fill expertise gaps, and provide ongoing operational support. This gives you customization where it matters, speed where you need it, and avoids vendor lock-in on the platform layer.

The Anti-Pattern to Avoid: Building everything from scratch because “we're special.” Every organization thinks their requirements are unique. In practice, 80% of platform capabilities are standard across the industry. Custom-building the standard parts wastes 12-18 months of engineering time that should be spent on the 20% that actually differentiates your developer experience.

Measuring Platform Engineering Success

Platform engineering must be measured like a product — through adoption, satisfaction, and business impact. Vanity metrics like “number of services in the catalog” or “CI/CD pipeline count” are insufficient. Here is a comprehensive measurement framework:

DORA Metrics (Delivery Performance)

The four DORA metrics are the industry standard for measuring software delivery performance and the primary indicator that your platform is working:

Developer Satisfaction (Experience)

If developers do not use the platform or actively work around it, the platform has failed regardless of its technical sophistication. Measure satisfaction quarterly through:

Business Impact (Outcomes)

Case Study: Building an IDP for 100+ Engineers

A Series C fintech company with 120 engineers across 14 product teams was drowning in operational complexity. Each team managed their own infrastructure, CI/CD pipelines, and monitoring — resulting in 14 different Terraform structures, 6 different CI systems, 3 monitoring tools, and zero standardization. New service creation took 2-3 weeks. New engineer onboarding took 3-4 weeks before they could deploy to production. The platform engineering team (2 people) was the bottleneck for everything.

Phase 1: Discovery and Foundation (Months 1-2)

The platform engineering services team conducted developer experience interviews across all 14 teams, mapped every service, dependency, and workflow, and identified the top 5 pain points: slow service creation (2-3 weeks), inconsistent deployments (manual in some teams, GitOps in others), no service catalog (nobody knew what existed), duplicated effort across teams (each team building similar infrastructure), and security compliance (SOC 2 audit required manual evidence collection). The team deployed Backstage with a service catalog that automatically imported all existing services from Kubernetes and GitHub, giving the organization its first complete view of the software estate.

Phase 2: Golden Paths and Standardization (Months 2-5)

Four golden paths were created based on the most common service patterns: REST API (Go), event-driven consumer (Python), scheduled job (Python), and React frontend. Each golden path included a Backstage software template, Dockerfile with hardened base image, GitHub Actions CI pipeline with testing, linting, SAST, and container scanning, ArgoCD application manifests for GitOps deployment, Terraform modules for dependent AWS resources, and pre-configured Prometheus metrics, Grafana dashboards, and PagerDuty integration. The team standardized on a single CI/CD flow (GitHub Actions → ECR → ArgoCD → EKS) and migrated teams in waves of 3-4, starting with the most enthusiastic early adopters.

Phase 3: Self-Service and Automation (Months 5-8)

Crossplane was deployed to enable self-service infrastructure provisioning. Developers could request PostgreSQL databases, Redis caches, SQS queues, and S3 buckets through the Backstage portal — with all security, networking, and monitoring configuration handled by Crossplane Compositions. Ephemeral preview environments were enabled for every pull request, with automatic cleanup after merge. SOC 2 compliance was automated: every golden-path service automatically satisfied 70% of SOC 2 technical controls through built-in encryption, access logging, vulnerability scanning, and audit trails.

Phase 4: Optimization and Handoff (Months 8-10)

The final phase focused on cost optimization (FinOps integration into Backstage showing per-team spend with right-sizing recommendations), performance tuning (Karpenter replaced Cluster Autoscaler, reducing node provisioning time from 5 minutes to 30 seconds), and team handoff. The internal platform team was expanded from 2 to 5 engineers through a combination of new hires and internal transfers, with the platform engineering services team providing mentorship and pair programming during the 2-month transition period.

The platform engineering services engagement cost $350K over 10 months. The return: 120 engineers each saving an average of 5 hours per week on infrastructure tasks (equivalent to 15 full-time engineers of recaptured capacity at $150K fully-loaded cost each = $2.25M/year), plus $180K/year in cloud cost savings and immeasurably faster time-to-market for new product features. The platform paid for itself within the first 3 months of operation.

Frequently Asked Questions