Summary of the Article
- Developers lose up to 60% of their time on DevOps tasks rather than writing code, resulting in a significant “DevOps tax” on productivity
- Platform engineering develops dedicated infrastructure that can increase developer speed by up to 50% while reducing operational overhead
- Self-service developer portals and automated deployment pipelines remove the most frequent bottlenecks in the software delivery process
- Implementing “Golden Path” patterns gives developers pre-approved workflows that balance freedom with operational stability
- Observability solutions assist organizations in measuring and optimizing platform engineering effectiveness, ensuring maximum developer productivity
Software developers should be coding, not dealing with infrastructure. However, recent research from Dynatrace shows that the average developer only spends 40% of their time on productive coding tasks. The rest is lost to what industry experts are now calling the “DevOps tax” – a hidden productivity killer that’s costing organizations millions in wasted engineering hours.
The True Cost of DevOps: Why Developers Aren’t Doing Their Jobs
DevOps was supposed to be a game changer: it would eliminate barriers between development and operations teams, leading to faster, more dependable software delivery. But DevOps requires a specific set of skills, most notably on infrastructure, and not so much on development. Developers were always supposed to stay focused on developing, but a lot of organizations expected developers to add infrastructure skills to their wheelhouse in order to become “DevOps engineers” and it’s like asking an interior designer to suddenly be a builder too and be able to build and maintain the building before they decorate it. Rather than empowering developers, this approach has saddled them with operational duties they were never prepared for, nor will they ever be.
Understanding the DevOps Tax
The DevOps tax is a term used to describe all the non-programming activities that take away from a developer’s productive time. These activities include configuring environments, debugging deployment issues, writing YAML files, troubleshooting infrastructure issues, and waiting for approvals. It also includes the mental effort required to learn multiple tools across the software delivery lifecycle. Most importantly, the DevOps tax refers to the frustration and burnout developers feel when they can’t focus on what they do best: solving problems through coding.
The DevOps tax isn’t just a concept – it affects your business in tangible ways. Every hour your developers spend configuring Kubernetes instead of working on features is an hour of delayed response to the market and a disadvantage against your competitors.
How Much Time Do Developers Waste on Infrastructure Tasks?
These figures are overwhelming. Industry research shows that developers now spend up to 60% of their time on tasks that do not involve writing code. The breakdown is surprising: 25% on environment setup and configuration, 20% on deployment and release management, and 15% on troubleshooting infrastructure issues. And we haven’t even counted meetings, which can take up another 15-20% of a typical workweek.
For a development team of 50 engineers earning an average annual salary of $120,000, this equates to more than $3.5 million in lost productivity each year. What’s more, 36% of developers say they’ve thought about quitting their jobs because of a bad developer experience. This makes it not just a productivity problem, but also a retention crisis.
The Scary Efficiency Gap in Today’s Development Teams
The move to cloud-native architectures has expanded this efficiency gap. Microservices, containers, and distributed systems have brought exponential complexity. What used to be a single deployment now involves coordinating dozens of services, each with their own configuration, security needs, and operational features.
The intricacy of these systems has led to a situation where developers spend more of their time linking and setting up systems rather than actually creating them. They have essentially become part-time infrastructure engineers, security specialists, and operations experts. These roles divert them from their main area of expertise and what they are passionate about.
Platform Engineering: The Key to Gaining More Coding Time
Platform engineering is the best solution to fight the DevOps tax and recover developer speed. Essentially, platform engineering is about forming a team that is tasked with the creation and upkeep of internal developer platforms (IDPs). These platforms simplify infrastructure and allow development teams to use self-service features.
The basic idea is easy to understand and very effective: platform engineering teams can deal with the operational complexity that would otherwise be the responsibility of individual developers by creating standard, reusable components and automated workflows. This means that development teams can concentrate on what they’re really good at – writing code that provides business value.
Instead of conventional DevOps methods that attempt to transform every developer into an operations expert, platform engineering accepts the fact that the majority of developers don’t want to be infrastructure specialists. It offers guidelines, automation, and self-service tools that allow developers to be productive without needing extensive operational knowledge.
“An Overview Of Platform Engineering …” from devopscurry.medium.com and used with no modifications.
Automated Deployment Pipelines Reduce Release Hassle
Old-school deployment methods require several handoffs, permissions, and manual tasks that slow down developers who are ready to deliver their code. Platform engineering resolves this by creating automated pipelines that manage the whole process from commit to production with little human involvement. These pipelines include security scanning, quality checks, and compliance validation automatically, lifting the weight off individual developers while maintaining organizational standards.
Automated deployment pipelines have a significant and immediate effect. Teams that implement full deployment automation usually see a 200-300% increase in deployment frequencies, while deployment failures are reduced by up to 70%. This allows developers to confidently push code, knowing that the pipeline will identify issues before they reach production, and without having to wait days or weeks for release windows. For more insights on how this process fits into the broader context of platform engineering, check out this guide on platform engineering best practices.
Faster Troubleshooting with Centralized Observability
Issues are bound to crop up, and when they do, developers can lose valuable time trying to assemble a coherent picture of what went wrong from scattered logs and metrics strewn across multiple systems. Platform engineering solves this problem by putting in place unified observability solutions that offer a comprehensive view of application performance, infrastructure health, and user experience through a single interface. This centralized method significantly cuts down on the time spent diagnosing problems and allows developers to rapidly pinpoint root causes.
The Golden Path Pattern: A Roadmap to Developer Success
Central to successful platform engineering is the idea of the “Golden Path” – a meticulously crafted, pre-approved path that leads developers through frequent workflows such as creating a new service, deploying an application, or implementing observability. These paths are the suggested, supported, and tested methods for completing certain tasks within your organization.
The Golden Path isn’t designed to limit developers’ freedom. Instead, it’s meant to lessen the cognitive load they bear. It removes hundreds of minor decisions that not only drain mental energy but also create needless variation. By offering clear patterns for common tasks, teams can save their creative energy to solve their domain’s unique challenges instead of recreating infrastructure patterns.
Why is the Golden Path Worth Pursuing?
A successful Golden Path is the perfect combination of providing real value while being opinionated and accommodating legitimate edge cases with flexibility. It should automate repetitive tasks, encode best practices, provide sensible defaults, and guide developers towards secure, scalable solutions without excessive ceremony. The best Golden Paths are always evolving based on developer feedback and emerging technologies, creating a continuous improvement cycle that benefits the entire organization.
Striking a Balance Between Developer Independence and Operational Limitations
Platform engineering success hinges on striking a balance between allowing developers the freedom to do their jobs and maintaining operational stability. If the system is too restrictive, developers will either find ways around the restrictions or become frustrated. If it’s too lax, you run the risk of creating unmanageable complexity and security vulnerabilities. The best way to handle this is to make the standard path so easy and well-supported that developers will choose it of their own accord, but still provide exceptions for legitimate edge cases that require customization.
Actual Instances of Successful Golden Paths
Top tech companies show the strength of Golden Paths in action. The Backstage developer portal from Spotify offers templated service creation that automatically includes security, monitoring, and deployment configurations. The engineering platform from Netflix features standardized patterns for resilience that make chaos engineering and fault tolerance the standard rather than a secondary consideration. These companies have discovered that by making the correct thing the simple thing, developers naturally lean towards practices that enhance both their productivity and the operational health of the company.
Assessing the Effectiveness of Platform Engineering
Platform engineering is an investment, and like any strategic initiative, you need to measure its success. The upside is that successful platform engineering can lead to improvements in several areas that can be quantified and tracked over time.
The most immediate effect is on the speed of development – how fast teams can go from concept to execution. This speed boost comes from cutting down on waiting times, lessening mental strain, and automating repetitive tasks. By tracking the correct metrics before and after the process, you can show stakeholders a tangible return on investment.
Platform engineering doesn’t just increase velocity. It also enhances reliability, security, and developer satisfaction. All these factors contribute to business outcomes like faster time to market, fewer outages, and better talent retention.
How to Measure Developer Velocity Improvements
If you want to see how your platform engineering efforts are paying off, look at metrics that show developer productivity and efficiency. Lead time for changes, which is the time from when code is committed to when it is successfully deployed in production, gives you a good idea of how efficient your delivery pipeline is. Deployment frequency tells you how often your team is able to safely release code. Change failure rate and mean time to recovery (MTTR) show you how reliable your systems are.
The DORA research program has popularized four metrics that provide a balanced scorecard for measuring delivery performance. Elite performers deploy multiple times per day, have lead times under an hour, recover from failures in less than an hour, and maintain change failure rates below 15%.
- Lead time for changes: Should decrease by 50-80% after platform engineering implementation
- Deployment frequency: Typically increases 3-10x within six months
- Change failure rate: Should decrease by 40-60% as automated testing and validation improve
- MTTR: Often drops by 70% or more with better observability and self-service rollback capabilities
- Developer satisfaction: Measured through surveys, usually shows 30-50% improvement
Beyond these delivery metrics, track platform adoption rates, developer NPS scores, and time spent waiting for environments or approvals to get a holistic view of your platform’s impact.
How to Determine Your Existing DevOps Tax Load
Before you put any money into platform engineering, you should first determine your current DevOps tax to understand how much productivity you’re losing. Ask your developers to keep track of their time for a period of two weeks, during which they should categorize their activities as either value-adding (such as writing code or designing features) or overhead (like waiting for environments, debugging deployments, or writing configuration). Once you’ve figured out the percentage of time that’s being spent on overhead activities, multiply that by your engineering payroll to determine the financial impact.
Most organizations find that 40-60% of a developer’s time is spent on non-coding activities. This can represent a loss of millions in productivity, even for mid-sized engineering teams. This baseline measurement is useful in justifying the investment in platform engineering and provides a benchmark for measuring improvements.
Justifying the Investment to Management
When arguing the case for platform engineering, concentrate on three primary benefits: faster delivery schedules, better system dependability, and improved developer retention. For delivery speed, work out the revenue benefits of getting features to market more quickly or reacting faster to competitive pressures. For dependability, work out the cost savings from avoiding downtime through better deployment practices and observability.
One of the most convincing arguments is the talent retention aspect. Considering that the full cost of replacing a senior engineer often exceeds $100,000, reducing turnover by even a small percentage through a better developer experience can justify considerable platform investments. Pair these benefits with your DevOps tax calculation to create a compelling business case.
Creating Your Initial Internal Developer Platform
The first step towards successful platform engineering is to create a basic internal developer platform (IDP) that solves the most urgent problems for your developers. To do this, map out the current developer experience from the moment they join the company until they deploy to production. Identify the areas that cause the most problems and the manual processes that could be improved with automation and self-service.
Instead of trying to construct a full platform at once, concentrate on delivering incremental value through a minimum viable platform (MVP) strategy. Start with the features that will have the most impact on developer productivity, such as environment provisioning, deployment automation, and basic observability. As the platform’s usage increases and you receive feedback, expand the platform’s capabilities to meet more complex requirements.
You can also save yourself a lot of effort and outsource the whole creation and management of your IDP to platform experts like SlickFinch who can build, run and maintain your IDP for you, working directly with your development team to create the IDP that you actually need.
Take Baby Steps: The Bare Minimum Platform
Creating a successful internal developer platform doesn’t have to be a Herculean task. Begin with a narrow MVP that solves your developers’ most pressing problems. Pinpoint the 2-3 workflows that are the most time-consuming and create the most friction for your developers – usually environment provisioning, CI/CD pipeline setup, and basic observability implementation. Develop standardized, self-service solutions for these specific areas before broadening the scope of your platform.
Your first platform should show immediate value through measurable time savings. For example, if developers currently spend 8+ hours setting up new environments, a self-service portal that reduces this to 15 minutes creates a clear productivity boost. These early wins build credibility and drive adoption, creating momentum for your platform engineering initiative. Remember, it’s better to solve three problems completely than fifteen problems partially.
Key Tools and Technologies
While the technology landscape for platform engineering is broad, some foundational components have become indispensable. A developer portal is the central interface where developers access platform capabilities. Tools like Backstage, Port, and OpsLevel are popular in this area. Infrastructure automation is handled by Terraform, Pulumi, or CloudFormation, which take care of environment provisioning. CI/CD pipelines use tools like GitHub Actions, GitLab CI, or Tekton. Kubernetes is often the container orchestration layer, with service meshes like Istio handling inter-service communication. For observability, platforms like Dynatrace provide the unified monitoring necessary for troubleshooting complex systems.
Dodging Common Implementation Mistakes
The road to platform engineering success is full of avoidable errors. The most common is creating a platform in isolation without continuous developer feedback, leading to capabilities that don’t address actual pain points. Just as problematic is building overly restrictive platforms that annoy developers with excessive guardrails or approval gates. Another frequent mistake is underinvesting in documentation and examples, making it hard for developers to understand and use platform capabilities effectively. Lastly, many platform teams fail to establish clear adoption metrics, making it impossible to show value and secure ongoing investment. Dodge these mistakes by keeping close collaboration with development teams, balancing security with usability, investing in comprehensive documentation, and establishing clear success metrics from the start.
Get Your Developers Back and Change Your Business
The DevOps tax isn’t just a technical problem – it’s a strategic business issue that affects your ability to provide value to customers. Every hour your developers spend on infrastructure tasks instead of developing features is an hour of competitive advantage lost. By implementing platform engineering practices, you’re not just improving developer velocity; you’re fundamentally changing your organization’s ability to respond to market opportunities and threats.
By introducing complete observability throughout the entire application lifecycle, companies can get the most out of their platform engineering investments. Observability solutions provide the insights necessary to pinpoint your development process’s bottlenecks, gauge the effectiveness of platform engineering initiatives, and constantly improve the developer experience.
Commonly Asked Questions
As platform engineering becomes more popular, businesses are naturally curious about how to implement it, its benefits, and the best way to use it. We’ve put together responses to the most frequently asked questions to assist you in your journey towards increasing developer speed and lowering operational costs.
The questions above are genuine worries from organizations at different stages of platform engineering maturity. Whether you’re just beginning to explore the concept or looking to optimize an existing platform, the answers below will provide practical guidance for your specific situation.
While each company’s path is different, the hurdles and benefits of platform engineering are strikingly similar across sectors and business sizes. The following responses encapsulate the wisdom gained from hundreds of successful rollouts.
Think of these frequently asked questions as a way to kick off discussions about your platform engineering project, so you can foresee potential issues and establish achievable goals with the people involved.
“Top-notch platform engineering groups do more than just provide technology – they offer developer experiences. They operate like product managers, grasping the requirements of their users and constantly enhancing based on input and usage trends. The platform isn’t the ultimate objective; instead, it’s the vehicle for achieving business agility via developer productivity.”
-Kelsey Hightower, Principal Engineer, Google Cloud
How long does it typically take to implement platform engineering practices?
Platform engineering is a journey rather than a destination. Most organizations see meaningful results within 3-6 months of starting with a focused MVP. This initial implementation typically addresses one or two key developer workflows. Full platform maturity, covering the entire development lifecycle with comprehensive self-service capabilities, generally takes 18-24 months. The key is to start small, deliver incremental value, and expand based on developer feedback and measured impact. Organizations that attempt big-bang implementations often struggle with adoption and fail to demonstrate clear ROI.
How does platform engineering and an internal developer platform differ?
An internal developer platform (IDP) is a product, a tangible technology solution that developers use to deploy applications, provision environments, and manage services. On the other hand, platform engineering is a practice, a method, team structure, and process for creating and improving the platform. To put it simply, platform engineering is the action, and the IDP is the outcome. Successful organizations need both: a well-structured platform that satisfies the needs of developers and a sustainable engineering practice that continuously enhances the platform based on feedback and changing requirements.
Is platform engineering necessary for smaller teams?
Absolutely, but it should be proportionate to their size. Even the smallest teams can fall victim to the DevOps tax when their developers have to waste time on repetitive tasks related to infrastructure instead of focusing on creating features. However, smaller teams should aim for practicality and make sure their implementations are in line with their resources. The best place to start is by standardizing and automating the workflows you use most often – such as provisioning environments, deploying processes, and setting up basic monitoring – without having to create complex custom solutions.
SlickFinch’s solutions can provide all of the benefits of platform engineering without the need for dedicated teams. As your organization scales, you can slowly add more sophisticated platform capabilities based on clear developer needs and demonstrated value.
Keep in mind that the aim is to boost the productivity of developers, not to adopt platform engineering for the sake of it. For some smaller teams, implementing streamlined processes and using existing tools could provide a better return on investment than creating custom platform capabilities.
Is platform engineering applicable in regulated industries?
Platform engineering is not only applicable in regulated industries, but it also delivers more value in such environments. Regulated industries like healthcare, finance, and government have strict compliance requirements that usually slow down software delivery. Platform engineering solves this problem by incorporating compliance requirements directly into platform capabilities, automating security checks, and automatically providing audit trails.
Ensure compliance and governance are incorporated into your platform from the get-go instead of being tacked on at the end. This “shift-left” strategy means that security and compliance can facilitate velocity instead of hindering it. Companies like Capital One and JPMorgan Chase have proven that it’s possible to maintain compliance and speed in heavily regulated environments by successfully implementing platform engineering.
What abilities should I be on the lookout for when assembling a platform engineering team?
Efficient platform engineers require a unique mix of technical abilities and user empathy. Search for engineers who have expertise in infrastructure automation, CI/CD pipelines, and cloud-native technologies such as Kubernetes. However, technical abilities alone are not enough – the most effective platform engineers think like product managers, comprehending developer needs and creating experiences that address actual issues.
Platform engineers must be able to work well with development teams, explain complex concepts in simple terms, and push for the adoption of the platform. The best candidate will have a deep understanding of infrastructure technologies and a broad understanding of the software development lifecycle.
