The Top 10 API Metrics to Demonstrate Performance and Drive Improvement Monitoring API performance can often feel like walking in the dark. You may have created a robust API, but without proper insights, you can’t fully understand its performance and potential shortcomings. And you’re not alone. Many teams struggle with tracking the right API metrics because narrowing things down to the most relevant KPIs is challenging. Factors like data overload, alert fatigue, integration challenges with various tools, and security concerns add to the complexity of effective monitoring. We’ll break down the 10 key API metrics you should track, explain why they matter, and what green and red flags you should watch out for. Why do API metrics matter? Without proper monitoring of API metrics, businesses risk running into performance bottlenecks, security vulnerabilities, inefficient operations, and missed opportunities for growth. Tracking this data is the best way to keep your APIs running smoothly and ultimately support broader business goals, like growing revenue or expanding company into new markets. Specifically, monitoring API metrics can improve the following: Performance and scalability API metrics show how well the API performs under different loads. By monitoring them, you can find bottlenecks in processes and optimize the system to handle increased traffic, ensuring scalability as the app grows. Security Tracking metrics related to system and data security can help identify and mitigate security threats. By regularly monitoring these metrics, you can ensure that your APIs comply with security standards and protect sensitive data. Data-driven decision-making API metrics can provide valuable insights into business decisions. They can highlight trends and patterns, helping you to make informed decisions about product development, resource allocation, and strategic planning. Improve user experience (UX) User satisfaction is closely tied to API performance, which directly impacts user experience. By monitoring UX metrics, API teams can ensure a smooth and responsive UX, increasing user satisfaction and retention. Revenue growth Revenue growth-related metrics provide insights into how APIs contribute to your business’s bottom line. These metrics can help you spot revenue-generating opportunities and optimize your API strategies accordingly. Product development Product development metrics are helpful in prioritizing development efforts. By understanding which features are most used and valued by users, you can focus on improving these areas and effectively allocate resources to drive product innovation and user satisfaction. Operational efficiency Operation-related metrics can be used to greatly improve overall API and team efficiency by identifying inefficiencies and areas for optimization, leading to cost savings and improved performance. Compliance and governance Monitoring compliance metrics keeps your API aligned with all necessary regulatory requirements. This reduces the risk of legal issues and enhances trust with users and stakeholders. Support and maintenance Support and maintenance metrics are crucial for improving your support services, reducing app downtime, and making sure users can access reliable and helpful documentation at all times. Keep in mind that there will be different categories of these metrics important to different members of your API team and relevant for different business goals. By aligning your choice of metrics with your business goals, you can ensure a comprehensive and strategic approach to API management. Let’s explore the key categories of API metrics you should track to drive performance and constant improvement. Product development metrics Product development metrics show how users interact with new features, which endpoints are most popular, and how quickly new users get value from your API. Product managers and developers can use these metrics to prioritize improvements, optimize performance, and make sure the API is meeting user needs. Feature adoption rates and time to first call (TTFC) are two product development metrics that can inform your next decisions in your API roadmap. Feature adoption rates API feature adoption rate measures how quickly and widely developers integrate new API endpoints or capabilities into their apps. This metric helps API product teams: Assess the success of newly released API features Improve overall UX by focusing on popular features Pinpoint potential barriers to adoption for specific API functionalities How to improve feature adoption rates Longer integration times often indicate areas for improvement: Documentation or code samples may be lacking The feature could be more complex than necessary There might be other underlying API issues Low usage metrics may also suggest that the feature may not be addressing a critical need. On the other hand, shorter integration times post-documentation or code sample improvement reflect the effectiveness of your efforts. If you’re aiming to improve API feature adoption rates, implement some of the following tactics: Analyze integration timelines after new feature releases to identify bottlenecks Collect feedback from developer communities on feature utility Offer dedicated support channels for developers during the integration process Build comprehensive documentation with code samples Time to first call (TTFC) Time to first call measures the duration it takes for a new user to make their first successful API call after signing up. This allows developers and technical writers to: Identify and reduce technical barriers that may prevent users from making their first API call quickly Validate that the documentation is helpful for new users How to improve TTFC High TTFC signals potential issues with onboarding, API design, or documentation. It can be a warning sign for low feature adoption and may also indicate poor developer experience. At the same time, decreasing TTFC may mean streamlined onboarding processes, API key acquisition, or environment setup. So, if you’re looking to improve TTFC, consider the following tactics: Focus on simplifying the initial developer experience Streamline onboarding and minimize the complexity of setup Provide clear documentation with step–by-step instructions Offer interactive API explorers or sandboxes that allow developers to make test calls without writing any code Operational efficiency metrics Operational efficiency metrics let IT teams monitor server health, find and resolve potential bottlenecks, and ensure that resources are used effectively. Tracking operational efficiency metrics can prevent performance issues, reduce downtime, and improve UX. Key operational efficiency metrics you should monitor are CPU utilization, uptime, average and max latency, errors per minute, and request per minute (RPM). CPU utilization CPU utilization measures the percentage of central processing unit (CPU) capacity that API servers use. It shows the computational load and ensures the servers operate efficiently. IT operations teams use it to: Prevent server overload by identifying when the CPU usage is reaching critical levels Ensure that the API servers are running efficiently, improving response times and overall performance Aid in capacity planning by providing data on current usage trends and predicting future needs How to improve CPU utilization High CPU utilization often indicates inefficient code, resource-intensive operations, or inadequate server capacity. If your CPU utilization is optimized, you’ll enjoy consistent, predictable performance and efficient resource usage. You can improve CPU utilization by: Implementing monitoring tools to track usage patterns and spot any bottlenecks Regularly reviewing your code and server configurations to optimize them for efficient resource use Ensuring database queries are optimized to minimize CPU usage Right-sizing server instances based on CPU utilization patterns to avoid overprovisioning or underprovisioning Applying caching strategies to reduce unnecessary CPU load on frequently requested data or computations Uptime Uptime measures the time the API is available and operational without interruptions. It’s a good indicator for reliability and availability. Tracking uptime allows you to: Make sure the API is available when needed, building trust and satisfaction Adhere to service level agreements by providing a clear measure of service availability Quickly identify downtime incidents and their causes, making it easier to resolve issues promptly and minimize their impact How to improve uptime Increased uptime percentages indicate improved API reliability, but if you’re seeing frequent outages, fluctuating performance, or high error rates, it may point to some underlying problems with your API. Here’s how to improve your uptime: Investigate network issues, server load, or application errors as potential causes Implement redundant systems (servers, databases, networks) to mitigate single points of failure Distribute traffic across multiple servers to prevent overload and improve fault tolerance Set up monitoring systems with real-time alerts to quickly address any issues that may affect availability Develop a robust incident response plan to minimize downtime impact Schedule regular maintenance during off-peak hours to minimize disruption to your users Average and max latency Average and max latency measures the time it takes for the API to respond to requests. Average latency provides an overall picture of API performance, while max latency highlights the longest response times experienced. Tracking latency helps: Ensure a fast and responsive UX for user satisfaction and retention Pinpoint any performance bottlenecks and areas that need optimization Ensure that the API meets performance-related service level agreements (SLAs) How to improve average and max latency For both average and max latency, you want them to decrease and/or stay low, as this reflects fast and consistent API performance. However, if you notice high latency, error rates might have increased or there might be network issues. To make sure average and max latency is optimal for your API, try the following: Investigate specific requests to identify the cause Optimize database queries, indexes, and caching to enhance response times Rely on load balancers and content delivery networks (CDNs) to distribute traffic Serve content from locations closer to your users Refactor inefficient code to reduce processing time and improve latency Errors per minute Errors per minute measure the frequency of errors in the API within a given minute. This metric helps identify issues that affect the API’s reliability and stability. Tracking errors per minute helps IT teams: Maintain the health and reliability of the API infrastructure Quickly identify and diagnose problems, allowing for faster resolution Improve API reliability by catching errors and reducing their frequency How to improve errors per minute Low EPM rate indicates a stable and reliable API. If the EPM pattern is consistent, it suggests predictable error behavior. On the other hand, an increased EPM rate can signal potential issues impacting user experience and problems that require immediate attention, especially if there’s a sudden spike. What can you do to keep EPM under control? A few things: Investigate recent code changes, database issues, or external dependencies Implement comprehensive error logging to capture relevant information for analysis Use automated monitoring tools to conduct regular code reviews Implement robust error handling mechanisms to gracefully handle exceptions and provide informative responses Increase test coverage to identify potential issues before they impact production Requests per minute (RPM) RPM measures the number of API requests received within a minute, which provides insight into the API’s usage patterns and load. Tracking RPM helps you: Understand whether the API infrastructure can scale to handle increasing demand Identify potential bottlenecks and optimize API performance Aid in forecasting future traffic and planning capacity How to improve requests per minute High RPM may indicate potential performance issues or capacity constraints. Consequently, it can lead to increased latency if the system is not adequately scaled. However, if your error rate is low under high RPM, that indicates good system responsiveness. Also, if the system can handle traffic spikes, that’s proof of effective scalability. To improve RPM, you can: Conduct load tests to determine the API’s capacity limits and identify performance bottlenecks Add more servers to handle increased load Increase the resources of existing servers to improve performance Implement caching strategies to reduce database load and improve response times Enforce limits on the number of requests to protect the system from overload User experience & customer satisfaction metrics User engagement metrics provide insights into user behavior, satisfaction, and retention, helping you to adjust your API to meet user needs better. Tracking these metrics can drive improvements in your API, leading to higher retention rates, increased loyalty, and positive word-of-mouth. If you’re aiming to pinpoint areas for improvement and continue growing your user base, you’ll want to track these three key metrics: API calls per user/developer, net promoter score, and customer satisfaction score. API calls per user/developer This metric measures the average number of API requests made by individual users or developers over a specific period, giving you insights into how often and when your API is being used. Tracking API calls per user/developer enables you to: See how actively users interact with the API, indicating engagement and satisfaction Identify usage patterns and understand which features are most popular Ensure the API infrastructure can handle the usage load and plan for future scalability Make informed decisions on feature improvements, optimizations, and new developments based on user behavior and needs How to improve API calls per user/developer Increased API calls per user/developer can indicate successful feature adoption or increased user engagement. Steady growth indicates increasing user engagement and API adoption, so it’s a positive sign to look for. But, if there are sudden spikes in CPU utilization or it’s combined with low user value, it may indicate bugs or potential API performance problems. Here’s what you can do to ensure optimal performance: Investigate if the endpoint is being used efficiently or if there are performance optimization opportunities Provide clear documentation and guidance on efficient API usage to reduce unnecessary calls Implement rate limiting to prevent abuse and protect API resources Optimize API endpoints for performance to handle increased CPU utilization without impacting response times Correlate CPU utilization with feature usage to understand which features drive user engagement Analyze CPU utilization by user type (free, paid, enterprise) to identify differences in usage patterns Net promoter score (NPS) NPS measures customer loyalty and satisfaction by asking users how likely they’re to recommend the API to others on a scale of 0 to 10. The score is calculated by subtracting the percentage of detractors (scores 0-6) from the percentage of promoters (scores 9-10). Formula: NPS = % Promoters – % Detractors Tracking NPS: Indicates user satisfaction and likelihood of recommending the API Highlight areas where the API may fall short based on feedback from detractors and passives Guides product enhancements and feature development based on user feedback and satisfaction Monitors user sentiment and satisfaction changes over time, helping assess the impact of product updates and changes How to improve NPS A higher NPS score indicates improved user satisfaction and loyalty and can show good overall API health. It may correlate with other metrics, like lower error rates. On the other hand, high latency may negatively impact NPS. If NPS spikes or drops correlate with other key metrics, it may point to an underlying issue that needs your attention. For a better NPS, you can: Analyze user feedback to identify issues and make necessary adjustments (even better if you can do it by user segments, like industry or company size) Conduct in-depth surveys to understand the factors driving NPS scores Improve the onboarding process to enhance initial user experience Enhance UX through better onboarding and support Engage with promoters to leverage positive experiences (gather testimonials or engage them in a customer advocacy program) Customer Satisfaction Score (CSAT) CSAT measures users’ satisfaction with the API or a specific interaction. Typically, users are asked to rate their satisfaction on a scale from 1 to 5, where 1 represents “very unsatisfied” and 5 represents “very satisfied.” Formula: CSAT = Summary of all scores / Number of responses Tracking CSAT: Provides immediate feedback on user satisfaction with specific features, interactions, or the overall API experience Highlights areas where users are dissatisfied Offer ongoing insights into user satisfaction, helping to track the impact of changes and updates over time Enhances user retention, as satisfied users are likelier to continue using the API How to improve CSAT While industry-specific benchmarks can vary, a general guideline is: Excellent: CSAT score of 90% or above Good: CSAT score between 80% and 90% Average: CSAT score between 70% and 80% Needs Improvement: CSAT score below 70% If you notice your CSAT has dropped significantly, here’s what you can do to improve it: Analyze CSAT by user segment (industry, company size, API usage level) to identify specific areas for improvement Consider using Customer Effort Score (CES) to measure the ease of using the API Collect detailed feedback alongside scores to identify common dissatisfaction themes Aim to improve customer support (by reducing response time or offering self-service options, for example) Use A/B testing to compare different versions for features, documentation, and other support-focused content Communicate what actions you’ve taken based on feedback to your users API metrics FAQs What are the three pillars of API monitoring? The three pillars of API monitoring are: Performance monitoring, which involves tracking the speed and efficiency of API responses. Key metrics include latency, throughput, and response times. Performance monitoring helps ensure that APIs meet the expected speed and reliability standards, providing a good UX. Functional monitoring ensures that APIs perform their intended functions correctly. It includes monitoring the correctness of API responses and verifying that endpoints return the expected data. Functional monitoring helps identify and fix bugs or logical errors in the API implementation. Security monitoring, which involves tracking the security aspects of API interactions. It includes monitoring for unauthorized access attempts, data breaches, and compliance with security standards. Security monitoring helps protect sensitive data and keeps the API secure from potential threats. How often should I review my API metrics? The frequency of reviewing API metrics depends on the criticality and usage of the API: Real-time monitoring: Continuous real-time monitoring is essential for mission-critical APIs to detect and respond to issues immediately. Daily reviews: High-traffic or business-critical APIs should review their metrics daily to meet performance and reliability standards. Weekly reviews: APIs with moderate usage can have weekly reviews to track overall performance and identify trends. Monthly reviews: Low-usage or internal APIs might be reviewed monthly, focusing on overall trends and long-term performance improvements. What is acceptable API performance? Acceptable API performance varies based on the specific use case and industry standards but generally includes the following: Latency: Ideally, API response times for most applications should be under 2s. Lower latency is crucial for real-time applications such as financial trading platforms or gaming services. Uptime: Aiming for an uptime of 99.9% or higher is considered good. This equates to less than 1m 26s of downtime per day or less than 8h 41m per year. Error rates: While errors below 1% are typically acceptable, important APIs should strive for even lower errors to ensure reliability. Throughput: The API should handle the expected load efficiently and be able to scale up during peak times without significant performance degradation. Set your API up for success Tracking these metrics can keep your API running smoothly and your users happy. If you’re looking for a place to get started, check out the metrics features included in Developer Dashboard. Want to learn more? Reach out here to get a demo and learn more, or head here to sign up for a free trial.