I Set Up OpenTelemetry. Now My Bill Is 10x Higher. What Happened?

Adopting OpenTelemetry can lead to unexpectedly high costs due to the increased volume of telemetry data, which results in higher expenses for storage and indexing on commercial backends that charge per gigabyte. The key to cost management lies in understanding that while OpenTelemetry instrumentation is free, the backend pricing model is where costs accrue. To mitigate these expenses, it's essential to optimize both the instrumentation and the backend by employing strategies such as tail sampling, attribute filtering, log filtering, tiered retention, and selecting cost-efficient backend architectures. These measures can significantly reduce costs while maintaining the quality of incident debugging. Tail sampling, for instance, involves retaining 100% of error traces while sampling healthy traces, which can drastically cut down on data volume. Additionally, choosing a backend that separates storage and compute, like object-storage-first models, can further reduce costs. The article underscores the importance of separating the choice of instrumentation standard from backend economics to achieve both reliability and cost-effectiveness.

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