Developer Builds Cost-Aware OTel Processor to Cut LLM Observability Spending

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A developer has released TraceShrink, an OpenTelemetry Collector processor designed to reduce observability costs for teams running LLM-powered services in production. Unlike standard 1% random tail sampling, TraceShrink uses cost-aware rules to retain traces based on token cost, error status, and response duration. In simulations using one million spans with a realistic AI workload, the tool retained just 2% of spans while preserving 70% of total dollar cost and keeping all error and slow traces. At scale, the developer estimates savings ranging from a few hundred to over twelve thousand dollars per month depending on span volume and the observability platform used. TraceShrink integrates into existing collector configurations via a single YAML block and uses LiteLLM's pricing database covering over 2,700 models to calculate per-trace costs.

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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