The API response time analyzer calculates latency statistics from raw response time data. Paste a list of times in milliseconds to get p50, p75, p90, p95, p99 percentiles, mean, median, standard deviation, and a visual distribution.
How to Analyze API Response Times
Paste your response times in milliseconds — one per line, or comma-separated. These can be copied from log files, monitoring dashboards, or load test output. The analyzer calculates all key latency percentiles and statistics in one view.
Understanding Percentiles
P50 (median) is your typical response time. P95 means 95% of requests complete within that time. P99 captures "tail latency" — the slowest 1% of requests. For SLA compliance, p99 under 500ms is a common target for user-facing APIs.
FAQ
Is this API response time analyzer free?
Yes, completely free with no signup. All calculations run locally in your browser.
What is a p95 percentile?
The p95 (95th percentile) is the response time below which 95% of requests fall. For example, if your p95 is 200ms, it means 95% of your API requests respond within 200ms. The remaining 5% take longer. P95 and P99 are common SLA metrics.
Why is p99 important?
P99 (99th percentile) captures the slowest 1% of requests — often called 'tail latency'. In high-traffic systems, 1% of requests can represent thousands of users per minute. High tail latency often indicates lock contention, garbage collection, or resource starvation.
How do I input response times?
Paste response times in milliseconds, one per line or comma-separated. The tool accepts plain numbers like: 45, 120, 89, 230, 44. You can paste directly from log files or monitoring tools.
What is a good API response time?
For web APIs, common targets are: under 100ms (excellent), 100-300ms (good), 300-1000ms (acceptable for complex operations). P99 under 500ms is a common SLA target. For user-facing APIs, Google's research shows 53% of mobile users abandon pages taking over 3 seconds.