Roundtable

The Log Tipping Point: Managing Telemetry at AI Scale

October 26, 2026 12:00 pm - 1:30 pm GMT
Data-driven.
People first.
Executive Engagement.

Rethinking log management for AI-driven telemetry growth

Telemetry volumes are accelerating fast as AI models and agents generate far more logs, traces, and metrics than legacy systems were designed to handle. That growth creates three linked pressures: exploding storage and ingestion costs, fragmented visibility across multiple telemetry tools, and weakened investigator effectiveness when context is scattered. Faced with these pressures, many organizations struggle to explain logging spend to finance, to locate which application or team is driving costs, and to preserve the investigatory context needed when incidents touch AI services. How should leaders reshape telemetry strategy so cost, visibility, and speed of investigation are all addressed?

Organizations are responding in several pragmatic ways rather than pursuing a single silver-bullet solution. Some teams rationalize tooling to reduce sprawl and create a clearer ownership model for telemetry costs. Others apply retention and sampling policies tied to business value so high-fidelity data is kept where it matters and cheaper summaries are used elsewhere. There is also a governance layer emerging that maps telemetry sourcing, retention, and access to accountability for AI outputs, helping stakeholders from engineering to finance make clearer trade-offs about visibility versus cost.

Join industry peers to explore where telemetry strategy is proving effective, what governance and cost-allocation practices are working, and which operational changes accelerate AI projects rather than slow them. This discussion is a chance to compare practical approaches to consolidating observability, assigning telemetry ownership, and tightening retention policies so your teams can support AI innovation while keeping costs and investigative time under control.

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Discussion Topics

1

Creating a clear ownership model that ties telemetry generation to cost allocation and application teams so logging spend is explainable and actionable.

2

Balancing retention, sampling, and tiered storage so investigators retain needed context while long-term costs are controlled.

3

Consolidating telemetry tools where it reduces fragmentation, while preserving specialized capabilities that teams rely on for domain-specific investigations.

About Dynatrace

Dynatrace is advancing observability for today’s digital businesses, transforming the complexity of modern digital ecosystems into powerful business assets. Leveraging AI-powered insights, we enable organizations to analyze, automate, and innovate faster to drive their business forward.

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In-depth face time with the world’s leading tech companies pushing the boundaries to solve your challenges

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