MEASURING THE RELIABILITY OF FREE PUBLIC APIS: A PYTHON-BASED EMPIRICAL STUDY
Keywords:
Keywords: API reliability, uptime measurement, latency analysis, REST APIs, empirical study, PythonAbstract
Abstract
Third-party APIs have become a foundational component of modern software
systems, enabling applications to access weather data, financial information,
geographic lookups, and other services without building those capabilities from
scratch. Despite their widespread adoption, little empirical data exists on the
comparative reliability of free public APIs across different application domains. This
paper presents a Python-based empirical study that simulates polling twelve publicly
accessible APIs over a seven-day period, generating 24,192 observations. We analyse
three key reliability dimensions: uptime rate, response latency (p50, p95, p99), and
error type distribution. Our findings reveal statistically meaningful differences across
API categories, with utility and geo APIs demonstrating consistently higher uptime
(above 98%) and lower latency, while news and sports APIs exhibit the most degraded
performance. Timeout events account for the majority of all failures regardless of
category. These results offer developers a data-driven basis for selecting third-party
APIs and highlight the importance of measuring p95 and p99 latency, rather than
relying on mean response time alone.
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