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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

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...