Platform Engineering vs. DevOps: Differences, Examples, and Synergies

What Is Platform Engineering?

Platform engineering involves creating and managing a shared platform that developers use to streamline their work. These internal developer platforms (IDPs) focus on enhancing developer productivity and operational efficiency through self-service. Their objective is to abstract complexities involved in infrastructure management and provide a set of tools and services that enable developers to deploy, monitor, and scale applications.

This field requires a deep understanding of both software development and operational challenges, incorporating practices from DevOps and product management to build a robust, scalable, and secure platform. 

By handling the underlying infrastructure, platform engineering allows developers to focus more on writing code and less on the operational aspects of the product lifecycle, thus accelerating development.

What Is DevOps? 

DevOps is a set of practices that combines software development (Dev) and IT operations (Ops) aimed at shortening the development life cycle and providing continuous delivery with high software quality. It emphasizes communication, collaboration, and integration between software developers and IT professionals.

DevOps aims to make the process of developing, testing, deploying, and operating software more efficient and automated. By fostering a culture of collaboration and leveraging automation tools, DevOps helps organizations to rapidly deliver products and services, respond to market demands, and improve overall customer satisfaction.

In this article:

Platform Engineering vs. DevOps: 5 Key Differences

Let’s look at some of the ways that platform engineering differs from DevOps.

1. Approach

DevOps focuses on integrating development and operations through continuous automation of the software development lifecycle. It promotes a culture of collaboration, breaking down silos between developers and operations teams to enhance communication and problem-solving. Agile processes are implemented to quickly adapt to changes, emphasizing continuous improvement and flexibility.

Platform engineering utilizes Infrastructure as Code (IaC) for consistent and error-reduced management of infrastructure, supporting scalable and repeatable development environments. It relies on self-service platforms and tools, where developers deploy infrastructure and services autonomously. A key focus is on creating standardized environments and governance models to ensure compliance, security, and operational efficiency.

2. Focus and Goals

DevOps emphasizes continuous integration and delivery (CI/CD) practices with the aim of shortening the software development lifecycle. It covers the entire lifecycle from planning to monitoring of applications.

Platform engineering involves building and managing an internal product or toolset for standardized software development and deployment, while abstracting the operational aspect. Its goals include improving developer experience and productivity (as measured for example by DORA metrics), and ensuring security and compliance across development projects.

3. Methodologies and Practices

The DevOps methodology integrates tools, culture, and practices for enhanced team collaboration. It utilizes CI/CD for efficient and continuous software updates and implements practices for rapid and reliable software delivery. Shift left is commonly used to introduce quality and security testing as early as possible in the development lifecycle, but introduces its own set of challenges.

Platform engineering uses product management methodologies to make a platform useful and compelling for developer use cases. It aims to streamline infrastructure management and emphasizes the creation of tools and services that empower developers. Developer platforms encompass deployment, operation, and monitoring elements of the DevOps cycle, providing a backbone for DevOps practices.

4. Roles and Responsibilities

Typically, DevOps teams are responsible for building the infrastructure and pipelines which developers use to code and release features to customers. They address the entire development and operations cycle, including planning, coding, testing, deployment, and monitoring.

Platform engineering teams focus on building, maintaining, and optimizing internal developer platforms, including identifying the needs of development teams and other platform users. They define the tools and workflows used by DevOps teams and focus exclusively on deployment, operation, and monitoring elements.

Comparison Checklist: Platform Engineering vs. DevOps 

Platform EngineeringDevOps
ObjectiveBuild and manage shared platforms for software deployment and developmentIntegrate development and operations for faster delivery
Primary FocusInfrastructure management and tool provisionContinuous integration and delivery (CI/CD)
Methodologies and PracticesUtilizes Infrastructure as Code (IaC), automation, and self-service toolsImplements agile practices, CI/CD, and automation
ApproachStandardizes environments and automates infrastructure managementPromotes collaboration and automates software lifecycle
Tools and TechnologiesSelf-service platforms, IaC, automation toolsAutomation tools, CI/CD pipelines, real-time monitoring
Roles and ResponsibilitiesFocus on infrastructure, compliance, and platform stabilitySpan across planning, coding, testing, and deployment
Key ChallengesStandardizing development environments, security, complianceCollaboration across teams, managing complex pipelines
Cultural EmphasisEmpowering developers with tools and standardized environmentsEnhancing communication and collaboration between teams
OutcomeEnhances developer productivity by abstracting operational complexitiesAccelerates development and operational efficiency
Governance and ComplianceStrong emphasis on governance models and complianceLess focus on governance, more on process efficiency
Innovation FocusStreamlining deployment processes and infrastructure managementRapid delivery, market responsiveness, customer satisfaction

A Day in the Life of a Platform Engineering and DevOps Team

To give you a better idea of what platform engineers and DevOps specialists do in their day-to-day, let’s see a “day in the life” of each of these teams.

Platform Engineer Team

Platform engineering teams interview developers about their needs, ask for feedback on the platform’s capabilities, and measure its performance and engagement. They also prepare roadmaps of future integrated capabilities, carry out vendor research, and work on integrations that can add value to the platform.

DevOps Team

A DevOps team in a software development company works on an application that requires frequent updates and reliable uptime. They deploy new releases, monitor application performance in production, and address production issues. They use automated tools to manage the deployment and operation of applications, ensuring continuous integration and delivery. 

How Do DevOps and Platform Engineering Work Together?

The merging of DevOps and platform engineering is a strategic enhancement that leverages the strengths of both disciplines to accelerate and optimize software development processes. This convergence involves:

Integration of Methodologies

By integrating platform engineering’s infrastructure focus with DevOps’ emphasis on rapid, agile delivery, organizations can create more resilient and adaptable IT environments. Platform engineers develop and manage a standardized platform that DevOps teams can use to quickly deploy and scale applications. This reduces the overhead on DevOps teams, allowing them to focus more on adding value through software development.

Cultural Synergy

The cultural shift involves fostering a deeper collaboration where platform engineers and DevOps teams work closely together. This alignment helps in understanding and addressing the operational needs early in the development process, leading to more secure and robust applications.

Enhanced Tool Integration

Combining these practices often involves integrating tools such as version control systems, automated deployment tools, and monitoring tools into a cohesive platform that supports both infrastructure management and application development. This integration ensures that both platform engineers and DevOps teams have access to the right tools for their respective needs.

Shared Responsibilities

While maintaining their specialized roles, both teams share responsibilities such as ensuring uptime, security, and performance. Platform engineers provide the tools and systems needed to support these objectives, while DevOps teams use these systems to maintain and improve the application lifecycle.

Related content: Read our guide to platform engineering tools (coming soon)

Build Your DevOps Pipeline with Spot by NetApp

Spot by NetApp’s optimization portfolio provides resource optimization solutions that can help make your DevOps pipeline more impactful. Here are some examples of automated actions our users enjoy on their K8s, EKS, ECS, AKS and GKE infrastructure: 

  • Autoscaling: This single word encompasses multiple procedures: knowing when to scale up or down, determining what types of instances to spin up, and keeping those instances available for as long as the workload requires. EC2 ASG’s are an example for rigid, rule-based autoscaling. You might want to get acquainted with additional K8s autoscaling methods like HPA or event-driven autoscaling. 
  • Automated rightsizing: Recommendations based on actual memory and CPU usage can be automatically applied to certain clusters or workloads 
  • Default shutdown scheduling: Requested resources can be eliminated after regular office hours, unless the developer opts out a specific cluster. 
  • Automated bin packing: Instead of having nine servers 10% utilized, gather those small workloads in one server. Bin packing can be user-specific or not, according to your security policies. 
  • Dynamic storage volume: Your IDP should regularly remove idle storage. It’s also recommended to align attached volume and IOPS with node size to avoid overprovisioning in smaller nodes. 
  • AI-based predictive rebalancing replaces spot machines before they’re evicted involuntarily due to unavailability. 
  • Data, network, and application persistence for stateful workloads, either by reattachment or frequent snapshots. 
  • Dynamic resource blending aware of existing commitments (RIs, SPs) which must be used before purchasing any spot or on-demand machines. 
  • “Roly-poly” fallback moves your workload to on-demand or existing commitments if there is no spot availability. When spots are once again available, you want to hop back onto them. 

To discover what key optimization capabilities your platform can enable in container infrastructures, read our blog post or visit the product page.