AI’s Hidden Cost: Up to Half of Observability Spend, Report Finds

SAN FRANCISCO, CA – May 26, 2026 – The rapid adoption of artificial intelligence is creating a significant and often unexpected financial strain on enterprise technology budgets, with AI workloads now consuming up to half of what companies spend on system monitoring, according to a new industry report. The findings highlight a growing crisis where the very tools meant to ensure system reliability are becoming unsustainable cost centers, forcing a difficult trade-off between visibility and budget control.

The report, titled “The Observability Imperative: From Monitoring Layer to AI Decision Infrastructure in 2026,” was released by observability platform groundcover. Based on a survey of 500 U.S. technology leaders, it reveals that while observability is evolving from a reactive monitoring tool into a critical data foundation for AI, most organizations are unprepared for the costs and complexities this transition entails.

Key among the findings is that nearly half (49%) of technology leaders report that AI workloads are responsible for 26% to 50% of their total observability costs. This seismic shift is catching many by surprise, challenging the financial models that underpin modern IT operations.

The AI Cost Shock Hits IT Budgets

The financial impact of AI on observability is stark. The survey found that 39% of organizations are already spending between $1 million and $5 million annually on observability alone. Compounding the issue, a majority (53%) experienced budget overages of 10% or more in the last fiscal year, with a significant portion facing moderate overruns of 10–30%. This spending surge directly correlates with the explosive growth of AI, which is projected to become a $2.5 trillion market by 2026.

According to the report, the primary drivers of these escalating costs are twofold. First, the technical demands of AI systems, which require higher-fidelity telemetry, are responsible for bloating budgets for 37% of respondents. Second, the sheer volume of data generated by AI, including high-cardinality metrics and excessive data ingestion, was cited by 39% of leaders as a key cost driver. Fragmented and overlapping monitoring tools, a common issue in large enterprises, also contribute to the problem for 31% of organizations.

In response, nearly 80% of teams are implementing cost-control measures such as aggressive data sampling, changing data retention policies, or exploring open-source alternatives. However, the report cautions that these measures can create a dangerous false economy. By cutting the data they collect to save money, organizations risk creating the very visibility gaps that can lead to the next major outage or cost spike.

Beyond Monitoring: A New Brain for AI

The soaring costs reflect a fundamental transformation in the role of observability itself. The report makes it clear that for many organizations, observability is no longer just about detecting outages. A remarkable 89% of respondents now use observability data for forward-looking, strategic decisions, with 47% stating it is continuously embedded into their AI, product, or operational workflows. This marks a definitive shift from passive monitoring to observability as an active, intelligent infrastructure—the central nervous system for complex AI applications.

“We know that observability has moved well beyond detecting outages, and this research makes clear that engineering organizations are already treating it as core infrastructure for understanding, operating and ultimately automating complex AI systems,” said Shazar Azulay, CEO and co-founder of groundcover, in the press release. “The organizations that work to close the visibility gap now, especially around AI workloads, will move from reacting to incidents to actively shaping their outcomes.”

However, this evolution brings new, formidable challenges. AI systems introduce a class of problems that traditional monitoring tools were never designed to solve, creating what the report calls an “AI Visibility Gap.” Key challenges include:

• Model vs. Infrastructure Ambiguity: 38% of teams find it difficult to determine whether a failure originates in the AI model’s logic or the underlying infrastructure.
• External Service Blind Spots: 34% report they cannot adequately observe the behavior of the external, third-party AI services and LLMs their applications depend on.

These issues are compounded by the non-deterministic nature of AI. Unlike traditional code, AI models can fail in subtle ways, quietly degrading in quality or producing confidently incorrect outputs without triggering conventional alarms. This makes it essential to monitor not just system health but the behavior and accuracy of the AI itself.

The Data Fidelity Problem and the Trust Gap

Perhaps the most consequential finding of the survey is a massive 53-point gap between the adoption of AI in observability and the trust placed in it. While 87% of organizations report integrating AI or automation into their observability workflows, only 34% describe these systems as “fully operational and trusted.”

The report attributes this trust deficit not to the quality of the AI models but to a core “data-fidelity problem.” To control costs, many observability platforms with volume-based pricing models encourage or require data sampling—ingesting only a fraction of the total telemetry data. When platforms “sample the most relevant spans away to control costs, AI features are forced to reason using an incomplete picture,” the report states. This practice of discarding data fundamentally undermines the reliability of AI-driven insights and automated actions.

This creates a vicious cycle: the need for complete data to safely operate AI systems drives up data volume and cost, which in turn pressures teams to sample data, thereby eroding trust and reintroducing risk. This tension between cost and completeness is a central conflict in the current observability market, particularly for established vendors whose pricing is tied directly to data ingestion.

Navigating the New Observability Landscape

Faced with these challenges, technology leaders are rethinking their strategies. The report indicates a strong trend toward vendor consolidation, with 35% of organizations using cost pressures as a trigger to streamline their fragmented toolchains. As they re-evaluate, many are looking toward new architectures and pricing models that can resolve the conflict between cost and data fidelity.

Technologies like eBPF are gaining traction because they allow for deep, kernel-level data collection with minimal performance overhead, eliminating the need for cumbersome code instrumentation. Furthermore, deployment models like Bring-Your-Own-Cloud (BYOC), where observability data remains within a customer’s own cloud environment, offer enhanced security, data sovereignty, and cost control. This approach, paired with predictable host-based pricing rather than volatile data-volume pricing, allows organizations to capture 100% of their telemetry data without fear of budget overruns.

As organizations deploy more sophisticated agentic AI systems, their observability priorities are also shifting. The survey found that teams are now most focused on monitoring AI reasoning accuracy and explainability (48%), followed by the reliability of the tools and APIs the AI agents use (44%). This focus on AI behavior, rather than just infrastructure health, confirms that the industry is actively grappling with the next frontier of operational excellence in the AI era.