Telemetry that matters: Designing sustainable, high-impact observability pipelines

2026-06-22 · Source: Cloud Native Computing Foundation · Field: Technology & Digital — Software Development & Engineering, Cloud Computing & IT Infrastructure, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

The article discusses challenges with telemetry data in cloud-native environments, specifically over-collection and "green observability." It summarizes strategies from an Observability Summit North America panel on June 22, 2026, featuring Diana Todea, Laura Luttmer, and Antonio Jimenez Martinez. The core problem is that around 50% of collected metrics are never used, leading to bloat, engineering overhead, alert noise, and increased carbon footprint. The panel advocated for treating observability as a day-zero design requirement and highlighted a shift towards an "observability mesh" for incident navigation, integrating traces, metrics, logs, and profiles, using RED metrics for initial isolation. The discussion covered instrumentation trade-offs, recommending starting with zero-code auto-instrumentation before layering in manual methods. Optimization strategies within data pipelines include smart sampling, managing high cardinality, cardinality limiters, log deduplication, and infrastructure enrichment. Finally, the panel addressed observing Agentic and LLM-driven flows, emphasizing evaluating "decision quality" over just system uptime.

Key takeaway

For MLOps Engineers or AI Architects designing observability for complex cloud-native or AI systems, you must prioritize intentional telemetry collection from day one. Avoid over-instrumentation by starting with zero-code solutions, then strategically adding manual instrumentation for critical business logic. Optimize data pipelines with smart sampling and cardinality management to reduce waste and improve incident response, especially when evaluating probabilistic AI system outcomes.

Key insights

Over-collection of telemetry data creates significant waste and hinders effective incident response in complex cloud-native systems.

Principles

• Observability must be a day-zero system design requirement.
• Integrate traces, metrics, logs, and profiles into an "observability mesh."

Method

Start with zero-code auto-instrumentation for a baseline, then progressively layer in manual instrumentation where deep context is needed, followed by pipeline-level optimization.

In practice

• Implement tail-based or pattern-based smart sampling.
• Use transform processors to mask high-cardinality attributes.

Topics

• Cloud-Native Observability
• Telemetry Data Management
• OpenTelemetry
• Observability Mesh
• AI System Observability
• Data Pipeline Optimization

Best for: CTO, VP of Engineering/Data, Director of AI/ML, DevOps Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Cloud Native Computing Foundation.