Platform Engineering is becoming increasingly crucial. According to the 2024 State of DevOps Report: The Evolution of Platform Engineering, 43% of organizations have had platform teams for 3-5 years. The field offers numerous benefits, such as faster time-to-market, enhanced developer happiness, and the elimination of team silos. Key benefits of platform engineering include improved productivity, software quality, deployment speed, and system stability. Increased productivity is a direct result of adopting platform engineering practices.
However, there is one critical piece of advice that Platform Engineers often overlook: treat your platform as an internal product and consider your wider teams as your customers. Platform adoption within organizations can be challenging, making it essential to track progress and foster cooperation across teams to ensure successful adoption.
So, how can they do this effectively? It’s important to measure what’s working and what isn’t using consistent indicators of success.
In this blog, we’ve curated the top platform engineering KPIs that software teams must monitor:
Platform Engineering, an emerging technology approach, enables the software engineering team with all the required resources. This is to help them perform end-to-end operations of software development lifecycle automation. The goal is to reduce overall cognitive load, enhance operational efficiency, and remove process bottlenecks by providing a reliable and scalable platform for building, deploying, and managing applications. Platform engineering also simplifies complex infrastructure deployments, making it easier for developers to work efficiently and streamlining the software delivery process. Additionally, it optimizes the development process to enable faster and more reliable software delivery. These efforts are critical within the platform engineering domain, where aligning metrics and practices with platform stability and reliability is essential.
Platform Engineering KPIs offer insights into how well the platform performs under various conditions. Monitoring delivery health provides real-time visibility into the efficiency and effectiveness of project delivery, helping teams stay aligned and proactive. Tracking cycle time as a key metric allows teams to measure the speed and efficiency of the software delivery process, enabling continuous improvement. They also help to identify loopholes and areas that need optimization to ensure the platform runs efficiently.
These metrics guide decisions on how to scale resources. It also ensures the capacity planning i.e. the platform can handle growth and increased load without performance degradation.
Tracking KPIs ensure that the platform remains robust and maintainable, while monitoring code quality helps ensure the software codebase meets organizational standards. Assessing software quality through KPIs such as change failure rate and mean time to restore provides valuable indicators of stability and deployment success. Using numerous metrics offers insights into codebase standards and developer effectiveness. In addition to the main KPIs, considering other metrics—like incident resolution rates, open incident counts, and deployment frequency—gives a more comprehensive view of platform reliability and team productivity. This further helps to reduce technical debt and improve the platform’s overall quality.
They provide in-depth insights into how effectively the engineering team operates and help to identify areas for improvement in team dynamics and processes. Measuring developer satisfaction through regular surveys and feedback mechanisms is essential for boosting team morale and productivity. Platform engineering practices enable product engineers by providing reliable infrastructure and automation, empowering them to focus on delivering value. High performing teams are characterized by frequent deployments and strong collaboration, reflecting their agility and efficiency.
Regularly tracking and analyzing KPIs fosters a culture of continuous improvement. Hence, encouraging proactive problem-solving and innovation among platform engineers.
Deployment Frequency measures how often code is deployed into production per week. It takes into account everything from bug fixes and capability improvements to new features. Continuous integration and continuous delivery practices enable rapid, reliable deployments by automating the build, test, and release process, making frequent releases possible. Deployment frequency is also a key indicator of the team's ability to ship features quickly and efficiently to end-users. It is a key metric for understanding the agility and efficiency of development and operational processes and highlights the team’s ability to deliver updates and new features.
The higher frequency with minimal issues reflects mature CI/CD processes and how platform engineering teams can quickly adapt to changes. Regularly tracking and adapting Deployment Frequency helps in continuous improvement as it reduces the risk of large, disruptive changes and delivers value to end-users effectively.
Lead Time is the duration between a code change being committed and its successful deployment to end-users. It is correlated with both the speed and quality of the platform engineering team. Higher lead time gives a clear sign of roadblocks in processes and the platform needs attention.
Low lead time indicates that the teams quickly adapt to feedback and deliver products timely. It also gives teams the ability to make rapid changes, allowing them to adapt to evolving user needs and market conditions. Tracking it regularly helps in streamlining workflows and reducing bottlenecks.
Change Failure Rate refers to the proportion or percentage of deployments that result in failure or errors. It indicates the rate at which changes negatively impact the stability or functionality of the system. CFR also provides a clear view of the platform’s quality and stability eg: how much effort goes into addressing problems and releasing code.
Delivering changes rapidly is only half the job; ensuring system stability and availability is equally important to maintain platform reliability.
Lower CFR indicates that deployments are reliable, changes are thoroughly tested, and less likely to cause issues in production. Moreover, it also reflects a well-functioning development and deployment processes, boosting team confidence and morale.
Mean Time to Restore Mean Time to Recover (MTTR) represents the average time taken to resolve a production failure/incident and restore normal system functionality each week. Low MTTR indicates that the platform is resilient, quickly recovers from issues, and efficiency of incident response.
Faster recovery time minimizes the impact on users, increasing their satisfaction and trust in service. Moreover, it contributes to higher system uptime and availability and enhances your platform's reputation, giving you a competitive edge.
This KPI tracks the usage of system resources. It is a critical metric that optimizes resource allocation and cost efficiency. Resource Utilization balances several objectives with a fixed amount of resources.
It allows platform engineers to distribute limited resources evenly and efficiently and understand where exactly to spend. Resource Utilization also aids in capacity planning and helps in avoiding potential bottlenecks.
Error Rates measure the number of errors encountered in the platform. It identifies the stability, reliability, and user experience of the platform. High Error Rates indicate underlying problems that need immediate attention which can otherwise, degrade user experience, leading to frustration and potential loss of users.
Monitoring Error Rates helps in the early detection of issues, enabling proactive response, and preventing minor issues from escalating into major outages. It also provides valuable insights into system performance and creates a feedback loop that informs continuous improvement efforts.
Team Velocity is a critical metric that measures the amount of work completed in a given iteration (e.g., sprint). It highlights the developer productivity and efficiency as well as in planning and prioritizing future tasks.
Tracking team velocity also helps measure how reliably the team delivers on its commitments and planned work, providing insight into delivery predictability and consistency.
It helps to forecast the completion dates of larger projects or features, aiding in long-term planning and setting stakeholder expectations. Team Velocity also helps to understand the platform teams’ capacity to evenly distribute tasks and prevent overloading team members.
Cloud costs have emerged as a critical determinant of success for modern engineering teams, particularly as organizations scale their infrastructure and embrace increasingly sophisticated cloud resources across multiple providers and services. For platform engineering teams operating in today's complex technological landscape, maintaining vigilant oversight of cloud costs and resource utilization becomes an essential practice that ensures every dollar invested generates maximum value and measurable outcomes. By systematically tracking comprehensive metrics such as granular cloud resource usage patterns, cost per initiative breakdowns, and detailed return on investment (ROI) calculations for cloud-based projects and deployments, engineering leaders can analyze historical spending data to identify recurring patterns of waste, pinpoint optimization opportunities, and establish predictive models for future resource allocation decisions.
Effective resource allocation strategies and advanced resource efficiency methodologies form the foundational pillars of successful platform engineering operations in cloud-native environments. Leveraging sophisticated cloud cost management tools such as AWS Cost Explorer, Azure Cost Management, Google Cloud's Cost Management suite, and third-party solutions like CloudHealth and Spot.io, while simultaneously implementing industry-proven best practices for resource utilization optimization, enables platform engineering teams to strategically optimize their cloud expenditure, eliminate costly over-provisioning scenarios, and ensure that computational resources are intelligently allocated to initiatives where they generate the most significant business impact and measurable value. This comprehensive data-driven approach not only facilitates seamless alignment between technical teams and broader organizational objectives but also empowers engineering leaders to make informed, evidence-based decisions that drive sustainable business growth while systematically minimizing unnecessary operational expenses and reducing total cost of ownership.
By establishing cloud costs and resource management as fundamental key performance indicators within their operational framework, platform engineering teams can effectively support sustainable scaling initiatives, enhance overall system performance metrics, and make substantial contributions to the broader organizational objectives of cost efficiency optimization and operational excellence achievement. These methodologies enable teams to predict future resource requirements, implement automated cost controls, and establish governance frameworks that ensure long-term financial sustainability while maintaining high-performance standards and supporting business growth trajectories.
Achieving comprehensive alignment between platform engineering teams, application development teams, and organizational stakeholders represents a critical success factor for delivering enterprise-grade software solutions and maximizing business impact across the entire Software Development Life Cycle (SDLC). Engineering leaders function as orchestrators in this complex ecosystem, ensuring that cross-functional teams maintain synchronized focus on shared business priorities while establishing clear visibility into how individual contributions aggregate into organizational success metrics. This alignment methodology transforms disparate engineering efforts into cohesive value streams that directly support strategic business objectives and accelerate time-to-market for critical software deliverables.
Establishing robust communication frameworks and implementing comprehensive Key Performance Indicator (KPI) taxonomies constitute foundational architectural components within this alignment strategy. Through systematic tracking and analysis of these performance metrics, platform engineering teams can ensure complete transparency in their operational effectiveness while maintaining measurable accountability across all stakeholder interfaces. Advanced monitoring capabilities enable real-time identification of optimization opportunities and facilitate data-driven celebration of milestone achievements. Regular synchronization protocols, including cross-functional sprint reviews, collaborative architectural planning sessions, and stakeholder alignment workshops, systematically eliminate organizational silos while fostering a culture of continuous improvement and iterative optimization. These practices leverage Infrastructure as Code (IaC) principles and DevOps methodologies to create sustainable feedback loops that enhance both technical delivery and business value realization.
Stakeholder satisfaction metrics should be systematically captured through comprehensive survey instruments and Net Promoter Score (NPS) analytics, generating actionable intelligence that illuminates the platform engineering team's effectiveness in serving internal customer requirements across the development ecosystem. This closed-loop feedback mechanism empowers engineering organizations to implement rapid adaptation strategies, proactively address emerging concerns, and maintain strategic alignment with evolving business impact objectives. The integration of machine learning algorithms for predictive analytics and automated anomaly detection within these feedback systems enables platform teams to anticipate stakeholder needs and optimize service delivery before issues manifest. Ultimately, this comprehensive alignment framework ensures that platform engineering initiatives consistently deliver quantifiable business value while supporting the organization's broader digital transformation objectives and competitive positioning in the marketplace.
Customer satisfaction stands as a fundamental metric that reveals the authentic value generated by platform engineering teams across modern software organizations. High-performing engineering teams recognize that their platform's effectiveness extends far beyond technical milestones—it encompasses the satisfaction and loyalty of their user base, whether those users represent internal product engineers or external customers who interact with the platform's capabilities.
Implementing customer satisfaction tracking through comprehensive metrics like net promoter score (NPS) delivers direct insights into how effectively the platform engineering team addresses user requirements and pain points. Furthermore, monitoring critical business impact KPIs including time to market acceleration, average time to recovery optimization, deployment frequency enhancement, cost reduction achievements, and revenue growth trajectory enables engineering leaders to evaluate broader organizational objectives and fine-tune their strategic approach for maximum organizational impact.
Through this dual focus on customer satisfaction excellence and measurable business impact, platform engineering teams can establish a significant competitive advantage, boost developer productivity metrics, and guarantee that their technical initiatives align seamlessly with core business priorities. Consistently analyzing these essential metrics facilitates continuous improvement cycles, strengthens resource allocation decision-making processes, and empowers the entire organization to monitor advancement toward strategic goals. This comprehensive methodology ensures that platform engineering investments transform into measurable business outcomes and sustained organizational growth.
Firstly, ensure that the KPIs support the organization's broader objectives. A few of them include improving system reliability, enhancing user experience, or increasing development efficiency. Always focus on metrics that reflect the unique aspects of platform engineering.
Select KPIs that provide a comprehensive view of platform engineering performance. We’ve shared some critical KPIs above. Choose those KPIs that fit your objectives and other considered factors. Consider including project timelines as a KPI to assess planning accuracy and how well your engineering projects adhere to scheduled deadlines.
Assess current performance levels of software engineers to establish baselines. Set targets and ensure they are realistic and achievable for each KPI. They must be based on historical data, industry benchmarks, and business objectives.
Regularly analyze trends in the data to identify patterns, anomalies, and areas for improvement. Set up alerts for critical KPIs that require immediate attention. Don't forget to conduct root cause analysis for any deviations from expected performance to understand underlying issues.
Lastly, review the relevance and effectiveness of the KPIs periodically to ensure they align with business objectives and provide value. Adjust targets based on changes in business goals, market conditions, or team capacity.
Typo is an effective platform engineering tool that offers SDLC visibility, developer insights, and workflow automation to build better programs faster. It can seamlessly integrate into tech tool stacks such as GIT versioning, issue tracker, and CI/CD tools.
Typo also provides platform engineering KPI dashboards, giving platform teams transparency and observability into key performance indicators. It offers comprehensive insights into the deployment process through key metrics such as change failure rate, time to build, and deployment frequency. Moreover, its automated code tool helps identify issues in the code and auto-fixes them before you merge to master.
Typo has an effective sprint analysis feature that tracks and analyzes the team’s progress throughout a sprint. Besides this, It also provides 360 views of the developer experience i.e. captures qualitative insights and provides an in-depth view of the real issues.
Monitoring the right KPIs is essential for successful platform teams. Collaboration with product teams is also crucial to achieve shared objectives and maximize the impact of platform engineering KPIs. By treating your platform as an internal product and your teams as customers, you can focus on delivering value and driving continuous improvement. The KPIs discussed above provide a comprehensive view of your platform’s performance and areas for enhancement.
There are other KPIs available as well that we have not mentioned. Do your research and consider those that best suit your team and objectives.
All the best!
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