Principle:Confident ai Deepeval Trace Configuration

Overview

Trace Configuration is the principle of configuring trace collection behavior to meet the operational requirements of production environments. This includes controlling sampling rates, applying data masking for privacy compliance, segregating traces by environment, and managing global tracing toggles. These controls enable production-grade tracing with appropriate data privacy and performance characteristics.

Core Concept

Tracing in development and tracing in production have fundamentally different requirements. While development benefits from comprehensive, unfiltered trace collection, production environments demand careful control over what is collected, how much is collected, and how sensitive data is handled. Key configuration dimensions include:

• Sampling strategies -- In high-throughput production environments, collecting every trace may be prohibitively expensive in terms of storage and network overhead. Sampling rates (e.g., 10% of traces) allow teams to maintain statistical visibility while controlling costs.
• Data masking and PII redaction -- Production traces may contain personally identifiable information (PII) such as email addresses, phone numbers, or user queries with sensitive content. A configurable masking function allows teams to redact sensitive data before it leaves the application boundary.
• Environment segregation -- Traces from development, staging, and production environments should be clearly labeled and separable. This prevents development noise from polluting production dashboards and enables environment-specific analysis.
• Global tracing toggle -- The ability to enable or disable tracing entirely provides an emergency kill switch and supports deployment scenarios where tracing should be conditional.

Theoretical Basis

This principle draws from established practices in production observability:

• Sampling strategies -- Probabilistic sampling techniques used in distributed tracing systems (e.g., head-based sampling in OpenTelemetry) to balance observability coverage with resource consumption.
• Data masking / PII redaction -- Privacy engineering practices that ensure sensitive data is transformed or removed before being transmitted to external observability platforms, supporting compliance with regulations such as GDPR and HIPAA.
• Environment segregation -- The operational practice of maintaining separate telemetry pipelines or labels for different deployment environments, preventing cross-contamination of observability data.

Why It Matters

Without proper trace configuration:

• Performance degradation -- 100% trace collection in high-throughput systems consumes significant CPU, memory, and network resources
• Privacy violations -- sensitive user data flowing unredacted to external platforms creates compliance and legal risk
• Dashboard pollution -- development and staging traces mixed with production data obscure real operational insights
• No emergency control -- inability to quickly disable tracing during incidents or performance emergencies

Trace configuration transforms tracing from a development tool into a production-ready observability system.

Relationship to Implementation

This principle is realized through the trace_manager.configure() method on the global TraceManager singleton, which controls all trace collection behavior.

Implementation:Confident_ai_Deepeval_TraceManager_Configure

Metadata

DeepEval Tracing Observability LLM_Evaluation 2026-02-14 09:00 GMT