Improving API performance and reliability

A customer-facing API experienced slow responses and intermittent failures during periods of peak demand. The team had limited end-to-end visibility, which made incidents difficult to diagnose and encouraged cautious, time-consuming releases.

As usage grew, the business needed greater confidence that the API could support customers reliably. The goal was to improve current performance while giving engineers the tools and operating practices to prevent regressions.

Yoprel established a performance baseline using representative traffic, tracing, database analysis, and infrastructure metrics. This made it possible to identify the requests and dependencies contributing most to latency and error rates rather than relying on assumptions.

Improvements were delivered in measured increments: expensive queries were addressed, caching was introduced where safe, timeouts and failure handling were clarified, and dashboards and alerts were aligned to user-visible service levels. Load tests were added to the delivery process and incident findings were captured as concrete follow-up work.

The team gained a faster and more predictable API, alongside clearer evidence about capacity and remaining constraints. User-impacting issues became easier to detect and diagnose, and releases could be assessed against a repeatable performance baseline.

Documentation, dashboards, and collaborative handover left the internal team with practical tools to maintain reliability as demand and the product continued to evolve.

• REST APIs
• OpenTelemetry
• PostgreSQL
• Redis
• Grafana

Technology choices were shaped by the client’s existing platform, team capabilities, and the outcomes required.