Observability in applications comes with instrumentation overhead, which can impact performance and resource consumption. A benchmark of OpenTelemetry in a Go application revealed a CPU usage increase of about 35% and some additional memory usage, while still maintaining stable throughput. For teams prioritizing incident resolution, the tradeoff for detailed observability is often justified, though eBPF-based instrumentation offers a lighter alternative for monitoring without significant resource costs.

eBPF (extended Berkeley Packet Filter) is emerging as a transformative technology for cloud-native applications, enabling developers to execute code in the kernel without modifying the kernel itself. This capability enhances performance, security, and observability in cloud environments, positioning eBPF as a critical component in the next phase of cloud-native development.

Observability in applications introduces instrumentation overhead that can impact performance, particularly when using OpenTelemetry with Go. A benchmark comparing a Go HTTP server's performance with and without OpenTelemetry revealed a notable increase in CPU and memory usage, but maintained stable throughput. The choice of observability method should balance the need for detailed tracing against resource costs, with eBPF-based instrumentation offering a more lightweight alternative for high-load environments.