Protecting Margins at Scale: Taming Cloud-Native & AI Telemetry Costs

Cloud-native architectures already produce enormous amounts of telemetry. GenAI is adding another dimension to the challenge, increasing both the cardinality and volume of observability data while introducing new workloads whose behavior and costs can be difficult to predict. For engineering leaders, the result is a growing economic problem: observability budgets can rise faster than the business value the additional data delivers.

Simply collecting less isn’t necessarily the answer. Aggressive filtering can create dangerous blind spots, while indiscriminate collection can leave organizations paying to ingest, store, and analyze enormous volumes of data they may never use. The challenge is establishing greater control over the telemetry lifecycle: understanding what data is valuable, filtering low-value signals at ingestion, managing trace depth intelligently, and allocating observability spend where it produces the greatest operational value.

Join Blend and Cortex XCOR by Palo Alto Networks for an intimate virtual roundtable exploring the changing economics of observability in the AI era. Through candid peer discussion, we’ll examine how organizations are controlling telemetry growth, protecting reliability while reducing cost, governing observability spend across teams, and using AI itself to identify waste and optimize an increasingly complex telemetry footprint.