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Observability Costs Rising Faster Than Value

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply.

Top findings of the report include:

  • More than half of leaders allocate over 25% of their observability budget to a single platform, yet only 13% say they are very satisfied with the cost-to-value ratio.
  • Nearly 80% of teams are filtering, archiving, or offloading logs to control costs—reducing critical data visibility when teams need it most.
  • 87% of leaders report that slow queries on observability data were caused by inaccessible data delay workflows such as threat detection and incident response.
  • 87% of respondents are exploring or open to platform alternatives that reduce cost and scale pressure without disrupting current workflows, and 98% say they would adopt a fully compatible option.

Observability Spending Is Increasing While Value Declines

Enterprise teams report rising observability costs year over year, but confidence in platform ROI is slipping. Leaders say platform-centric licensing models and the rapid growth of observability data have created an environment whereby retaining essential logs or adding new workloads often requires difficult trade-offs.

Rising Costs Are Forcing Cuts to Visibility

To manage spend, many organizations are reducing retention or shifting data into lower-cost storage tiers. These common cost-saving measures directly reduce visibility and degrade query performance, and come with significant operational consequences:

  • High-value logs get filtered out before ingestion
  • Investigations slow as teams move data out of cold storage
  • Real-time responsiveness suffers during incidents

"Too many organizations are being priced into flying blind," said Eric Tschetter, Chief Architect at Imply. "They're cutting retention because budgets force their hand, and it shouldn’t be that way. Teams tell us they're pushing data into cold storage to keep costs in check and that can slow investigations, can create dangerous blind spots, and can weaken resilience. In a crisis, those trade-offs are unacceptable."

Teams Want Compatibility, Not Replatforming

Despite these challenges, leaders are not looking to rebuild their entire observability stack. Their frustration centers on the cost and scale limits of current approaches, not the workflows themselves.

  • 98% of leaders would adopt a fully compatible option that eases cost and scale pressure
  • Workflow continuity remains a top priority across respondents

"Teams aren't looking for a rip and replace," said Tschetter. "They want to keep their workflows and scale them. If you can separate cost from data volume and work with the tools they already trust, that's a breakthrough."

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Observability Costs Rising Faster Than Value

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply.

Top findings of the report include:

  • More than half of leaders allocate over 25% of their observability budget to a single platform, yet only 13% say they are very satisfied with the cost-to-value ratio.
  • Nearly 80% of teams are filtering, archiving, or offloading logs to control costs—reducing critical data visibility when teams need it most.
  • 87% of leaders report that slow queries on observability data were caused by inaccessible data delay workflows such as threat detection and incident response.
  • 87% of respondents are exploring or open to platform alternatives that reduce cost and scale pressure without disrupting current workflows, and 98% say they would adopt a fully compatible option.

Observability Spending Is Increasing While Value Declines

Enterprise teams report rising observability costs year over year, but confidence in platform ROI is slipping. Leaders say platform-centric licensing models and the rapid growth of observability data have created an environment whereby retaining essential logs or adding new workloads often requires difficult trade-offs.

Rising Costs Are Forcing Cuts to Visibility

To manage spend, many organizations are reducing retention or shifting data into lower-cost storage tiers. These common cost-saving measures directly reduce visibility and degrade query performance, and come with significant operational consequences:

  • High-value logs get filtered out before ingestion
  • Investigations slow as teams move data out of cold storage
  • Real-time responsiveness suffers during incidents

"Too many organizations are being priced into flying blind," said Eric Tschetter, Chief Architect at Imply. "They're cutting retention because budgets force their hand, and it shouldn’t be that way. Teams tell us they're pushing data into cold storage to keep costs in check and that can slow investigations, can create dangerous blind spots, and can weaken resilience. In a crisis, those trade-offs are unacceptable."

Teams Want Compatibility, Not Replatforming

Despite these challenges, leaders are not looking to rebuild their entire observability stack. Their frustration centers on the cost and scale limits of current approaches, not the workflows themselves.

  • 98% of leaders would adopt a fully compatible option that eases cost and scale pressure
  • Workflow continuity remains a top priority across respondents

"Teams aren't looking for a rip and replace," said Tschetter. "They want to keep their workflows and scale them. If you can separate cost from data volume and work with the tools they already trust, that's a breakthrough."

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Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...