Platform Engineering Explained

Do your developers build product features, or do they also spend days maintaining pipelines and troubleshooting infrastructure? For years, this was normal in many companies. There was no dedicated platform engineering function. Therefore, when a pipeline failed or a cluster became unstable, a developer from the product side would get pulled in. The same engineers who wrote application code were suddenly handling SRE tasks they were never formally trained for.

Now that the model is starting to change.

Recent State of Platform Engineering data from over 500 practitioners shows steady growth in dedicated platform investment, formalized platform teams, and internal adoption of developer platforms. At the same time, 94% say AI is important to their platform strategy.

What is platform engineering?

Platform engineering is what organizations move towards after a few years of scaling DevOps in the real world. At first, giving your engineers autonomy works well. But with time, though, every squad builds pipelines differently, provisions infrastructure in its own way, and interprets governance rules slightly differently.

That is when things start to feel heavier than they should.

Platform engineering is the discipline of building an Internal Developer Platform that becomes the shared foundation for all that work. It is not another tool layered on top. It is a collective effort to design the toolchains, workflows, and operational standards that engineers use every day.

An Internal Developer Platform typically brings together:

• CI/CD workflows that you can reuse without rebuilding
• Infrastructure provisioning and orchestration patterns
• Role-based access control and tenant isolation
• Security, compliance, and governance policies
• Configuration management and automation (as default behavior)

In large organizations, this model is becoming standard. Analysts expect most enterprise engineering groups to formalize platform engineering teams over the next few years as systems continue to grow in scale and complexity.

Who belongs to the platform engineering team?

A platform engineering team is usually made up of engineers who understand both infrastructure and software delivery. You will often find senior DevOps engineers, SREs, cloud infrastructure specialists, and sometimes backend engineers who have worked close to runtime operations. Sometimes you’ll see dedicated platform engineering roles.

What matters most is systems thinking. These engineers design CI/CD standards, manage infrastructure patterns, define access controls, and maintain automation frameworks. They operate the Internal Developer Platform as a product, with developers across the organization as their primary users.

DevOps vs platform engineering: are they the same?

This question comes up a lot. At first glance, they seem similar. Both deal with automation and delivery pipelines. Both aim to improve how software gets promoted from code to production. Still, they are not the same.

DevOps is first a way of working. It focuses on how development and operations collaborate. The model promotes shared ownership of production. It also supports continuous integration and continuous delivery. Faster feedback loops are part of that approach. In many organizations, DevOps engineers work directly inside product teams and help release features in a reliable way.

Platform engineering operates at a different level. It does not center on a single team’s workflow. Instead, it creates a common foundation that many can depend on.

The platform team defines infrastructure patterns and maintains reusable workflows. Moreover, they set governance models and supported deployment paths. This prevents rebuilding the same operational flows again and again.

In simple terms:

• DevOps improves how teams deliver software.
• Platform engineering improves the environment in which they deliver it.

In most organizations, the two strengthen each other rather than compete.

(Read our full explainer: DevOps vs. SRE vs. Platform Engineering.)

What key pain points can platform engineering solve?

As organizations grow, complexity grows with them. More services, more environments, more compliance requirements. At some point, delivery slows down not because your engineers lack skill, but because the system around them becomes harder to manage.

Platform engineering focuses on removing that friction.

Infrastructure span multi cloud and on-prem

Now environments often span multiple clouds and on-prem systems. Without clear standards, configurations drift, and deployments fail in unpredictable ways. Platform engineering introduces consistent infrastructure patterns that reduce that variability.

Operational dependency on central teams

When every deployment requires manual Ops involvement, queues form quickly. Platform engineering creates controlled self-service, so you can move without waiting.

Developer cognitive load

Developers are often expected to understand pipelines, networking, access rules, and runtime behavior on top of writing code. That context switching slows delivery. Having an internal platform reduces that mental overhead.

Risky security and compliance controls

As systems expand, enforcing security controls manually becomes risky and inconsistent. Platform engineering embeds governance into workflows so compliance is part of the path.

Multiple fragmented tools

Different teams adopting different tools leads to workflow sprawl. A unified platform brings visibility and standardization back into the delivery process.

Core principles of platform engineering

Platform engineering works only when it is surrounded by proper principles. Without them, a platform turns into another internal tool that nobody fully trusts.

Internal Developer Platform

An IDP unifies infrastructure, CI/CD, security controls, and observability into a single supported environment.

In practice, that means developers do not configure infrastructure or pipelines manually. Instead, they use a centralized portal or CLI to create services through approved templates. Those templates automatically provision infrastructure, set up CI/CD workflows, apply security policies, and enable monitoring by default. The platform brings all of these capabilities together behind one consistent interface.

Automation

Manual ticketing and approval chains slow everything down. Automation should handle provisioning, deployments, scaling, and policy checks.

A common use case is automated environment creation through Infrastructure as Code, where developers trigger provisioning directly from a Git workflow.

Security by design

Security must be embedded into the platform. Least-privilege access, policy enforcement, and audit logging are part of the system. In practice, this could mean enforcing RBAC through SSO and automatically validating infrastructure changes against compliance rules before deployment.

Multi-tenancy along with isolation

When many teams share infrastructure, isolation prevents risk and performance issues.

For example, Kubernetes namespaces with defined quotas and network policies prevent one team’s workload from disrupting another team’s production service.

Observability

The platform must expose its own health and the health of hosted services. Therefore, logging, metrics, and tracing are not optional—the goal is unified observability. A standard service template might automatically include monitoring dashboards and alert configurations so you can start with visibility from the beginning.

(Check out this example: Managing Metrics at Scale in Splunk Observability Cloud.)

Scalability

The platform should scale as usage grows and handle failures predictably. This often includes horizontal scaling strategies, defined SLOs, and error budgets that guide release decisions.

Standards with governance

Consistency helps reduce configuration inconsistency and differences in how environments are provisioned and managed.

For example, the platform team might standardize on a specific container orchestration model and provide linting rules to validate configurations before merging Pull requests sent to a certain repository.

Cost awareness

Infrastructure costs can grow quickly if left unmanaged. The platform should expose usage and enforce fair allocation. In many organizations, cost dashboards and rightsizing policies are built directly into the platform workflow.

Architectural characteristics of platform engineering

Platform engineering is also about how the platform is structurally designed so it can support dozens of teams without becoming fragile. Having proper architectural characteristics determines whether the platform is kept stable when more users adopt it and more services move onto it.

Layered platform design

In most production environments, the platform uses Infrastructure as Code. Tools such as Terraform or Pulumi define VPCs, Kubernetes clusters, IAM roles, and network configuration in a repeatable way.

On top of that, Kubernetes manages container orchestration. Shared services are then integrated. ArgoCD handles GitOps-based deployments. Identity federation is configured through OIDC. Policy enforcement runs through engines such as OPA.

Developers do not interact directly with infrastructure primitives. They access platform capabilities through controlled interfaces such as an internal developer portal or CLI. The lower layers remain visible but abstracted.

API-driven platform capabilities

Provisioning and deployment workflows are exposed programmatically rather than through manual setup.

Creating a new service typically begins with a repository template that includes CI configuration and deployment manifests. A code push triggers the CI pipeline. Then, GitOps controllers compare the declared configuration in Git with the running cluster and apply changes until they match.

Infrastructure as Code

All core infrastructure is managed with version control—this is known as Infrastructure as Code (IaC). Cluster creation, network layout, and access permissions are captured in configuration files.

When someone proposes a change, it is reviewed the same way application code is reviewed. If the company expands into another region, the team reuses the existing setup and updates only what is specific to that environment. Nobody has to rebuild the whole infrastructure manually.

Modular and loosely coupled components

A production platform avoids tight coupling between subsystems:

• Kubernetes handles orchestration.
• ArgoCD manages the deployment state.
• Vault or a cloud-native secrets manager manages credentials.
• Splunk, Prometheus, and Grafana provide metrics and visualization.

Each subsystem integrates through defined interfaces. If the CI engine or deployment workflow changes, other components continue operating without structural redesign.

Central governance with distributed usage

Governance policies are defined in code and enforced centrally. RBAC policies, network segmentation, and resource quotas are configured at the cluster or account level.

Service teams deploy workloads within assigned namespaces and approved deployment models. They retain ownership of their services while operating within enforced policy controls that maintain platform-wide consistency.

Does platform engineering apply to serverless?

Platform engineering is often associated with Kubernetes environments because Kubernetes introduces infrastructure complexity that most product teams are not meant to manage directly. Concepts such as deployments and custom resource definitions require platform-level expertise. In large organizations, it is not practical to expect every developer to handle cluster-level responsibilities alongside application development.

Serverless services such as AWS Lambda, Azure Functions, Google Cloud Functions, AWS Fargate, and Google Cloud Run remove the need to manage virtual machines or Kubernetes clusters. These are serverless runtimes and managed compute services where the cloud provider handles provisioning and scaling.

However, core platform responsibilities still exist:

• Defining IAM policies and role boundaries.
• Configuring API Gateway integrations.
• Managing event routing through EventBridge or Pub/Sub.
• Implementing monitoring, logging, and cost allocation controls.

Even without managing servers, teams must still design and govern how these components work together in a controlled way.

As more people adopt serverless, differences emerge in how IAM policies are defined, how functions are deployed, and how services integrate with event systems. These variations introduce operational inconsistency across the environment.

Therefore, serverless does not remove the need for platform engineering. It just hides a different layer of the stack. You still need guidance on how functions are structured, how IAM roles are defined, how deployments move to production, and how integrations are handled.

How does AI platform engineering improve developer experience?

In many companies, platform teams are starting to add AI capabilities to their Internal Developer Platforms. The goal is not to replace engineers. The goal is to remove repetitive work and reduce the time spent digging through tools.

Recent industry data shows how quickly this change is happening. According to the 2025 State of AI in Platform Engineering report, 89% of platform engineers now use AI daily for coding and documentation. At the same time, 40% already own AI platform responsibilities, while 35% still do not orchestrate AI workloads. More than half say they want clearer blueprints for AI infrastructure. This shows that adoption is real, but operational maturity is still catching up.

One clear example is log and metrics analysis. Instead of manually scanning dashboards, engineers can get summarized incident reports or anomaly detection based on recent deployments. This helps to understand what changed without checking five different systems.

AI can also assist with CI/CD optimization. It can suggest pipeline improvements, detect inefficient build steps, or detect risky configuration changes before they are merged to the master.

Another practical improvement is natural language querying. Engineers can ask questions about cluster usage, deployment history, or error rates and receive responses across logs and metrics.

There are risks, too. For example, AI systems can generate incorrect outputs. For that reason, strong RBAC controls and human approval remain critical.

Best practices in platform engineering

A platform must be operated intentionally, measured regularly, and treated as an evolving internal product. The following practices separate successful platform initiatives from internal projects that lose adoption.

• Treat the platform as a product. Similar to a product, manage roadmap priorities based on developer adoption and platform usage metrics.
• Define proper service boundaries. Clearly separate responsibilities between the platform function and service owners.
• Measure platform adoption. Track indicators such as template consumption, deployment frequency through the IDP, and reduction in manual service requests to validate platform impact.
• Version and deprecate responsibly. Maintain backward compatibility strategies and communicate deprecations to dependent services to prevent disruption.
• Model-driven infrastructure contracts. Use validated templates and predefined blueprints to enforce architectural consistency without blocking delivery.
• Platform change management discipline. Roll out platform updates incrementally using canary strategies or phased releases to minimize production risk.
• Establish internal enablement programs. Provide onboarding sessions and technical enablement resources so engineering groups understand platform capabilities.
• Maintain provider independence where possible. Design the platform to avoid tight coupling to a single cloud provider or tooling ecosystem.

Why should platform engineering and observability not exist without the other?

You cannot improve what you cannot see. And you cannot scale what you cannot control. That is exactly why platform engineering and observability belong together.

When delivery workflows are standardized, shadow IT is reduced. Services are deployed through approved templates. Those templates can enforce logging, metrics, and tracing from the beginning.

An Internal Developer Portal makes observability easier to use. When an alert comes in, engineers should not waste time figuring out where the data is. The service page can show connected dashboards, recent deployments, on-call ownership, and related services in one view.

If memory usage increases or latency goes up, the engineer can quickly check which version was deployed and who approved it. They can also see which other services depend on it. The logs, metrics, and traces are already connected to that service.

For SREs, this matters most during incidents. Platform engineering without observability lacks runtime visibility. Observability without platform engineering lacks consistency. Together, they create reliability that scales.

Platform engineering with Splunk

Platform engineering only proves its value when it can be measured. Leadership does not invest in pipelines and templates alone. They care about uptime, release stability, and how quickly issues are resolved. Splunk helps connect day-to-day platform activity to those outcomes.

Splunk supports capabilities that match directly with platform engineering:

• Unified observability: logs, metrics, and traces in one place so services and infrastructure can be viewed end to end.
• OpenTelemetry-native ingestion: consistent telemetry collection across cloud environments.
• Trace-to-log correlation: faster root cause analysis without switching tools.
• Real-time alerting: immediate visibility into service health and deployment impact.
• Workflow integration: integrate with CI/CD pipelines along with incident management processes.

Splunk provides a real-time view of how the platform performs across environments. It connects deployment activity and service reliability into measurable outcomes that matter to the business. If you want platform engineering backed by real metrics, learn how Splunk can support your organization.