DevOps vs platform engineering

In the last few years, platform engineering has emerged as a distinct practice alongside DevOps. If you work in software delivery, you’ve probably heard both terms used, sometimes interchangeably. They’re related but different, and understanding how they fit together matters for teams that want to ship code faster without creating chaos.

DevOps started around 2008 as a response to the friction between development and operations teams. Before DevOps, developers wrote code and threw it over the wall to ops. Ops teams were responsible for deployment, monitoring, and keeping systems running. The handoff created delays, blame, and slow feedback loops.

DevOps changed this by having developers take ownership of their services from code to production. Teams write code, build deployment pipelines, run tests, deploy to production, and respond when things break. This end-to-end ownership shortens feedback cycles and reduces the distance between writing code and seeing its impact.

DevOps relies on practices like infrastructure as code (IaC), automated testing, CI/CD pipelines, and monitoring. These aren’t just technical tools. They represent a shift in how teams organize work. A DevOps team might write Terraform configs, set up GitHub Actions workflows, configure Prometheus for monitoring, and maintain on-call rotations.

The philosophy is simple: you build it, you run it. Teams that deploy code are also responsible for fixing incidents. This creates accountability and reduces the incentive to ship unstable code.

What platform engineering does

Platform engineering builds internal tooling and infrastructure that makes it easier for application teams to ship code. Instead of every team setting up their own CI/CD pipelines, managing Kubernetes clusters, or configuring observability, platform engineering provides these as standardized services.

Think of platform engineering as building a product for internal developers. The platform team treats application developers as customers. They build self-service tools, abstractions, and APIs that hide infrastructure complexity. The goal is to let developers focus on business logic rather than operational details.

A platform engineering team might build:

• A developer portal where teams can spin up environments without filing tickets
• Reusable CI/CD templates that enforce security policies and best practices
• Abstraction layers over Kubernetes so developers don’t need to be experts in container orchestration
• Centralized logging and monitoring dashboards that work out of the box

The platform becomes a “golden path,” the easiest and safest way to deploy code. Developers can still drop down to lower-level tools when needed, but most of the time, the platform handles the complexity.

How they differ

DevOps is a mindset and set of practices focused on collaboration and automation. It’s about how teams work, not what they build. Platform engineering is about creating infrastructure products that enable DevOps practices at scale.

Here’s where they differ:

• Scope: DevOps applies to how individual teams own and operate their services. Platform engineering provides shared infrastructure that multiple teams use.
• Target audience: DevOps practices are adopted by application teams. Platform engineering serves those application teams as customers.
• Artifacts: DevOps teams produce deployment pipelines, monitoring configs, and runbooks for their own services. Platform teams produce APIs, CLIs, portals, and documentation for other teams.
• Focus: DevOps emphasizes ownership and reducing silos. Platform engineering emphasizes standardization and reducing cognitive load.

In a DevOps model, each team might build their own deployment pipeline. In a platform engineering model, the platform team provides a pipeline-as-a-service that application teams can use.

DevOps and platform engineering are complementary. DevOps provides the cultural foundation. Teams take ownership, automate processes, and eliminate handoffs. Platform engineering scales those practices by providing shared infrastructure.

When organizations adopt DevOps without platform engineering, teams often reinvent solutions. One squad uses Jenkins, another uses CircleCI, a third writes custom scripts. Each team solves the same problems independently. This creates inconsistency, duplicated effort, and operational overhead.

Platform engineering addresses this by encoding best practices into reusable tools. Instead of every team figuring out how to deploy safely to production, the platform provides feature flags, progressive rollouts, and automated rollback mechanisms. This is where tools like Unleash fit in. Feature management becomes part of the golden path for releasing features safely.

The relationship works in both directions. Platform engineering can support DevOps by providing tools that make it easier for teams to own their services. DevOps culture ensures that platform teams stay focused on what developers actually need rather than building ivory-tower solutions.

When each approach makes sense

Smaller organizations (under 20-30 engineers) usually don’t need dedicated platform teams. Application teams can handle their own infrastructure. The overhead of maintaining shared platforms outweighs the benefits.

As headcount grows, duplicated effort becomes a problem. Teams waste time on undifferentiated work: setting up monitoring, configuring CI pipelines, managing secrets. This is when platform engineering becomes valuable.

Organizations with strict compliance or security requirements often need platform engineering earlier. If application teams can’t have direct production access, a platform layer with controlled APIs provides the necessary abstraction while maintaining governance.

The transition happens when:

• Multiple teams maintain similar tooling independently
• Onboarding new engineers takes weeks because every team’s setup is different
• Security or compliance requirements demand standardization
• Operational complexity grows faster than the team’s ability to manage it

What this means in practice

Consider a company shipping a SaaS product. Early on, a small engineering team writes code, deploys to AWS, and handles incidents. They adopt DevOps practices: everyone knows how to deploy, everyone shares on-call duties.

As the team grows to 50 engineers across five squads, inconsistencies emerge. Each squad has different deploy scripts, monitoring setups, and incident response procedures. When someone moves between squads, they need to learn a new way of working.

The company forms a platform team. This team builds:

• Standardized deploy tooling that works across all services
• A service template that includes logging, metrics, and alerting
• A developer portal for requesting resources and viewing system status
• Integration with Unleash for feature flag management

Application teams now follow the golden path. They scaffold new services from the template, deploy through the standard pipeline, and use feature flags for gradual rollouts. The platform team maintains the underlying infrastructure while application teams focus on product features.

The DevOps culture remains. Teams still own their services and respond to incidents. But platform engineering removed the operational toil that distracted from building features.

Common mistakes

Treating platform engineering as a replacement for DevOps fails because you lose the cultural benefits. A platform team that doesn’t understand what application developers need builds tools that get ignored.

Building platforms without feedback loops creates the ivory tower problem. Platform teams must treat internal developers as real customers. Gather requirements, iterate based on usage, and measure adoption. If teams work around your platform instead of using it, you’ve failed.

On the flip side, pursuing DevOps without investing in shared tooling leads to chaos. Teams become overwhelmed by operational complexity. Engineers spend more time on infrastructure than features. Technical debt accumulates as every team solves problems differently.

The balance is letting teams own their services (DevOps) while providing high-quality, standardized tools that make that ownership easier (platform engineering).

The role of feature management

Feature flags bridge DevOps and platform engineering. They decouple deployment from release, which is fundamental to both practices.

In a DevOps context, feature flags let teams deploy code to production safely. You can ship half-finished features behind a flag, test in production with internal users, and roll back instantly if something breaks. This reduces risk and enables continuous delivery.

For platform teams, feature management is a service to provide. Rather than every team building flag systems, the platform offers a centralized solution. This standardizes how releases happen across the organization.

Unleash fits here as part of the platform’s golden path. Teams get self-service feature flag management without running their own infrastructure. The platform team can manage Unleash through infrastructure as code using tools like Terraform or Helm Charts, while application teams use it through SDKs and APIs. This is platform engineering enabling DevOps practices.

Moving forward

Most organizations need both DevOps practices and platform engineering, just at different times. Start with DevOps. Get teams owning their services and automating their workflows. When operational complexity or team size makes shared tooling valuable, invest in platform engineering.

The goal is not choosing between them but understanding how they work together. DevOps provides the culture and practices. Platform engineering provides the infrastructure that scales those practices.

If you’re building platforms, treat your internal developers like users. Measure adoption, gather feedback, and iterate. If you’re on an application team, use the platform when it makes your job easier and provide feedback when it doesn’t.

Both approaches aim for the same outcome: shipping software faster with less risk. DevOps does this through culture and ownership. Platform engineering does this through standardization and reducing cognitive load. When they work together through practices like FeatureOps, teams can move quickly without creating operational chaos.