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Open Source Is Dominating Observability

Open source dominance continues in observability, according to the Observability Survey from Grafana Labs.

A remarkable 75% of respondents are now using open source licensing for observability, with 70% reporting that their organizations use both Prometheus and OpenTelemetry in some capacity. Half of all organizations increased their investments in both technologies for the second year in a row.

The 2025 Observability Survey reveals that OpenTelemetry has continued its trajectory toward mainstream status with half of all organizations increasing their investments in the open source project for the second year in a row.

More than two-thirds of organizations (67%) use Prometheus in production in some capacity and while OpenTelemetry has less production usage (41%), it appears to have more momentum for future growth, with more than a third (38%) of respondents investigating it and only 6% reporting they have no plans to use OpenTelemetry at all. The survey also found that vendor neutrality and flexibility remain the most cited requirements for compatible observability solutions, directly aligning with OpenTelemetry's core value proposition.

"Our survey data confirms what we're seeing in the field — organizations aren't choosing between observability technologies, they're embracing multiple approaches to solve real-world problems," said Ted Young, developer programs director at Grafana Labs, cofounder of OpenTelemetry, and member of the OpenTelemetry Governance Committee. "This includes the growing adoption of both OpenTelemetry and Prometheus, two tools that work great together."

Other report findings include:

C-Suite Sees Importance of Observability

Roughly three-quarters of all companies say observability is business-critical at either the CTO, VP, or director level, with CTO being the most common response (33%). Organizations whose C-suite sees observability as business-critical are more likely to adopt more advanced tools and practices such as traces, profiles, SLOs, OpenTelemetry, and unified application and infrastructure observability.

Desire for AI to Tame Complexity Rises

The number one observability concern for respondents is complexity, while alert fatigue is cited as the biggest obstacle to faster incident response, so it's no wonder training-based alerts and faster root cause analysis topped respondents' AI/ML wishlist for observability.

Cost Management Remains Important, But Not Critical

Three-quarters of companies say cost is an important criteria when selecting observability technologies, though less than a third say they're concerned about observability costing too much — meaning organizations are more focused on getting value from their tools and techniques than just selecting the cheapest option.

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Open Source Is Dominating Observability

Open source dominance continues in observability, according to the Observability Survey from Grafana Labs.

A remarkable 75% of respondents are now using open source licensing for observability, with 70% reporting that their organizations use both Prometheus and OpenTelemetry in some capacity. Half of all organizations increased their investments in both technologies for the second year in a row.

The 2025 Observability Survey reveals that OpenTelemetry has continued its trajectory toward mainstream status with half of all organizations increasing their investments in the open source project for the second year in a row.

More than two-thirds of organizations (67%) use Prometheus in production in some capacity and while OpenTelemetry has less production usage (41%), it appears to have more momentum for future growth, with more than a third (38%) of respondents investigating it and only 6% reporting they have no plans to use OpenTelemetry at all. The survey also found that vendor neutrality and flexibility remain the most cited requirements for compatible observability solutions, directly aligning with OpenTelemetry's core value proposition.

"Our survey data confirms what we're seeing in the field — organizations aren't choosing between observability technologies, they're embracing multiple approaches to solve real-world problems," said Ted Young, developer programs director at Grafana Labs, cofounder of OpenTelemetry, and member of the OpenTelemetry Governance Committee. "This includes the growing adoption of both OpenTelemetry and Prometheus, two tools that work great together."

Other report findings include:

C-Suite Sees Importance of Observability

Roughly three-quarters of all companies say observability is business-critical at either the CTO, VP, or director level, with CTO being the most common response (33%). Organizations whose C-suite sees observability as business-critical are more likely to adopt more advanced tools and practices such as traces, profiles, SLOs, OpenTelemetry, and unified application and infrastructure observability.

Desire for AI to Tame Complexity Rises

The number one observability concern for respondents is complexity, while alert fatigue is cited as the biggest obstacle to faster incident response, so it's no wonder training-based alerts and faster root cause analysis topped respondents' AI/ML wishlist for observability.

Cost Management Remains Important, But Not Critical

Three-quarters of companies say cost is an important criteria when selecting observability technologies, though less than a third say they're concerned about observability costing too much — meaning organizations are more focused on getting value from their tools and techniques than just selecting the cheapest option.

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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 ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...