Telemetry

Telemetry is the collection of operational data emitted by production systems, including metrics, events, logs, traces, and other signals that describe system behavior over time. Telemetry is the raw material from which observability is built.

The volume of telemetry that modern production systems generate has grown faster than the workflows designed to use it. The 2025 Grafana Observability Survey found that observability now consumes 17% of total compute infrastructure spend on average. Some enterprises are now spending more on telemetry collection, storage, and querying than on the compute that generates it.

Telemetry types are typically captured under the MELT framework: Metrics (numeric measurements over time), Events (discrete state changes like deployments or configuration updates), Logs (textual records of application behavior), and Traces (request paths across distributed services). Each pillar answers different questions, and most modern incidents require correlating across three or four of them to diagnose. The fragmentation across telemetry stores—different tools for metrics, logs, and traces—is one of the largest hidden costs in incident investigation.

AI SRE consumes telemetry as input and produces causal explanations as output. The challenge isn't access to telemetry; that's largely solved by modern observability platforms. The challenge is making it reasonable to reason over at production scale. Naive approaches that put raw telemetry into LLM context windows fail on cost before they fail on accuracy. Traversal's AI-Native Compressor™ delivers approximately 1000:1 compression with zero critical signal loss, making petabyte-scale telemetry tractable inside the customer's own environment.