Logs in the Age of AI: Rethinking Scale, Cost and Trust
As AI drives unprecedented growth in log and telemetry data, how can organizations maintain visibility, control costs, and build the trust[LJ1.1][LJ1.2] needed to scale AI with confidence?
AI workloads have driven a 93% average increase in log and telemetry volume, overwhelming tools and budgets that were not built for this scale. Leaders are now managing an average of seven telemetry tools, creating fragmented visibility and operational complexity, while many struggle to explain which teams or applications are driving logging costs. With 80% saying telemetry complexity is actively delaying AI initiatives, the challenge extends beyond cost. Traditional log management approaches often lack the unified context needed to investigate AI-related issues,[LJ2.1] establish effective governance, and make data-driven decisions with confidence. As telemetry continues to grow alongside AI adoption, how can organizations maintain the visibility, control, and trust needed to scale?
Organizations are reassessing how they collect, retain, analyze, and govern telemetry data as they look for greater visibility across increasingly complex environments. This means examining the economics of logging, rationalizing overlapping tools, establishing clearer accountability for consumption and cost, and determining where greater consolidation can improve operational efficiency. Leaders are also considering how stronger observability practices can provide the context and governance needed to investigate AI-related issues[LJ3.1] and make decisions with greater confidence.
Join industry peers to compare how organizations are responding to rising telemetry volumes, where tool sprawl and cost pressures are creating the greatest challenges, and what approaches are helping teams balance visibility, efficiency, and data retention.
Discussion Topics
Understanding how AI-driven telemetry growth is changing the economics of log management and creating new pressures around cost, scale, and data retention.
Addressing tool sprawl and fragmented observability to improve visibility, accountability, and operational efficiency.
Determining how log management and observability strategies need to evolve to support AI innovation while balancing consolidation, governance, and trust.
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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