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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.

The Latest

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

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.

The Latest

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...