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Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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.

Among the key findings:

  • 92% see value in AI helping surface anomalies and issues before they cause downtime
  • 38% say complexity and overhead are their biggest observability concern
  • 77% say open source or open standards are important to their observability strategy
  • 77% say centralized observability has saved their organization time or money
  • Half of organizations now use observability tools to track business-related metrics

Together, the results point to a clear industry direction: organizations want observability solutions that are open, cost-efficient, and capable of delivering meaningful operational insights without adding complexity.

Practitioners Want AI That Earns Its Place, Not AI for AI’s Sake

The survey makes clear that observability practitioners are open to AI, but on their terms. Across a range of use cases, support is overwhelming: 92% see value in AI surfacing anomalies before they cause downtime as well as generating dashboards, alerts, and queries, while 91% endorse AI for forecasting and assisting with root cause analysis. Autonomous actions garner 77% support, but stand out as the highest area of skepticism: 15% don’t yet trust AI to act on their behalf and another 8% see no value in using AI for this.

The No. 1 barrier to AI adoption? Too much manual input of required context (26%). In other words, practitioners don't want AI that creates new toil in place of old toil. And 95% say it’s important for AI to show its reasoning, the clearest possible signal that transparency is not optional. Notably, those who are most enthusiastic about AI are also the most insistent on explainability.

SaaS Adoption Surges as Organizations Invest for ROI, Not Just Growth

The economics of observability are shifting. Half of all respondents now use SaaS for observability in some capacity (up from 43% in 2025). The share using SaaS exclusively has grown steadily from 10% in 2024 to 17% in 2026, a clear signal of market maturation and growing confidence in managed services.

Spending is rising, but thoughtfully. Half of respondents expect to spend more on observability next year, not because vendor prices are going up (only a quarter of respondents cite this), but because of broader adoption (63%) and expectations of higher ROI (31%). Those who expect to spend less point to more efficient operations (37%) as the reason. Meanwhile, cost remains the single most important tool selection criterion for the third year running (65%), followed by ease of use (49%).

The message to vendors is clear: organizations are willing to invest, but they expect demonstrable value in return.

Complexity Remains the Industry's Defining Challenge - and Centralization Is Helping

Complexity and overhead topped the list of observability concerns for 2026, cited by 38% of respondents, more than signal-to-noise challenges (34%) or cost (31%). Alert fatigue remains the biggest single obstacle to faster incident response, cited by 30% of respondents, nearly double the next most common response.

Yet there is genuine progress. More than three-quarters (77%) say they have saved time or money through centralized observability. Teams with mature, centralized practices are more satisfied with their internal operations (61%) compared to those with siloed setups (53%). The industry is also expanding its scope: nearly half (46%) of organizations have unified infrastructure and application observability in full production, and SLO adoption and business observability are both on the rise.

Self-managed teams are most likely to cite complexity as their top concern, while SaaS users are more likely to point to cost. The shift to SaaS, in part, is a direct response to the complexity burden, a trend expected to accelerate.

Open Source Remains the Bedrock while OpenTelemetry Is Coming Into Its Own

For the fourth consecutive year, open source and open standards are foundational to how practitioners think about observability. 77% say open source/open standards are important to their observability strategy, with 61% calling them “essential” or “very important.”

Almost two-thirds (65%) of organizations are investing in both Prometheus and OpenTelemetry. While Prometheus maintains a slight edge in overall investment (77% vs. 76%), OpenTelemetry is showing stronger growth signals: more respondents are building POCs or actively investigating (35% vs. 18%), and a higher share report increased investment over the past year (47% vs. 42%).

OpenTelemetry is no longer niche. It is now in broad use across metrics (57%), traces (50%), and logs (48%). Practitioners cite ease of adoption (41%) and the freedom to switch vendors (37%) as the top reasons they are turning to OTel, a direct expression of the industry’s desire for openness and portability, not lock-in.

Methodology: The survey is based on 1,363 responses from engineers, SREs, and technology leaders across 76 countries, collected through online outreach and industry events between October 1, 2025 and January 6, 2026. 

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Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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.

Among the key findings:

  • 92% see value in AI helping surface anomalies and issues before they cause downtime
  • 38% say complexity and overhead are their biggest observability concern
  • 77% say open source or open standards are important to their observability strategy
  • 77% say centralized observability has saved their organization time or money
  • Half of organizations now use observability tools to track business-related metrics

Together, the results point to a clear industry direction: organizations want observability solutions that are open, cost-efficient, and capable of delivering meaningful operational insights without adding complexity.

Practitioners Want AI That Earns Its Place, Not AI for AI’s Sake

The survey makes clear that observability practitioners are open to AI, but on their terms. Across a range of use cases, support is overwhelming: 92% see value in AI surfacing anomalies before they cause downtime as well as generating dashboards, alerts, and queries, while 91% endorse AI for forecasting and assisting with root cause analysis. Autonomous actions garner 77% support, but stand out as the highest area of skepticism: 15% don’t yet trust AI to act on their behalf and another 8% see no value in using AI for this.

The No. 1 barrier to AI adoption? Too much manual input of required context (26%). In other words, practitioners don't want AI that creates new toil in place of old toil. And 95% say it’s important for AI to show its reasoning, the clearest possible signal that transparency is not optional. Notably, those who are most enthusiastic about AI are also the most insistent on explainability.

SaaS Adoption Surges as Organizations Invest for ROI, Not Just Growth

The economics of observability are shifting. Half of all respondents now use SaaS for observability in some capacity (up from 43% in 2025). The share using SaaS exclusively has grown steadily from 10% in 2024 to 17% in 2026, a clear signal of market maturation and growing confidence in managed services.

Spending is rising, but thoughtfully. Half of respondents expect to spend more on observability next year, not because vendor prices are going up (only a quarter of respondents cite this), but because of broader adoption (63%) and expectations of higher ROI (31%). Those who expect to spend less point to more efficient operations (37%) as the reason. Meanwhile, cost remains the single most important tool selection criterion for the third year running (65%), followed by ease of use (49%).

The message to vendors is clear: organizations are willing to invest, but they expect demonstrable value in return.

Complexity Remains the Industry's Defining Challenge - and Centralization Is Helping

Complexity and overhead topped the list of observability concerns for 2026, cited by 38% of respondents, more than signal-to-noise challenges (34%) or cost (31%). Alert fatigue remains the biggest single obstacle to faster incident response, cited by 30% of respondents, nearly double the next most common response.

Yet there is genuine progress. More than three-quarters (77%) say they have saved time or money through centralized observability. Teams with mature, centralized practices are more satisfied with their internal operations (61%) compared to those with siloed setups (53%). The industry is also expanding its scope: nearly half (46%) of organizations have unified infrastructure and application observability in full production, and SLO adoption and business observability are both on the rise.

Self-managed teams are most likely to cite complexity as their top concern, while SaaS users are more likely to point to cost. The shift to SaaS, in part, is a direct response to the complexity burden, a trend expected to accelerate.

Open Source Remains the Bedrock while OpenTelemetry Is Coming Into Its Own

For the fourth consecutive year, open source and open standards are foundational to how practitioners think about observability. 77% say open source/open standards are important to their observability strategy, with 61% calling them “essential” or “very important.”

Almost two-thirds (65%) of organizations are investing in both Prometheus and OpenTelemetry. While Prometheus maintains a slight edge in overall investment (77% vs. 76%), OpenTelemetry is showing stronger growth signals: more respondents are building POCs or actively investigating (35% vs. 18%), and a higher share report increased investment over the past year (47% vs. 42%).

OpenTelemetry is no longer niche. It is now in broad use across metrics (57%), traces (50%), and logs (48%). Practitioners cite ease of adoption (41%) and the freedom to switch vendors (37%) as the top reasons they are turning to OTel, a direct expression of the industry’s desire for openness and portability, not lock-in.

Methodology: The survey is based on 1,363 responses from engineers, SREs, and technology leaders across 76 countries, collected through online outreach and industry events between October 1, 2025 and January 6, 2026. 

The Latest

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 ...

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