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Observability Has a Complexity Problem

Dotan Horovits
Logz.io

The journey of maturing observability practices for users entails navigating peaks and valleys. Users have clearly witnessed the maturation of their monitoring capabilities, embraced DevOps practices, and adopted cloud and cloud-native technologies.

Notwithstanding that, we witness the gradual increase of the Mean Time To Recovery (MTTR) for production issues year over year. In this year's (2023) DevOps Pulse survey, conducted by Logz.io, 73% of respondents stated that it took multiple hours on average to resolve production issues.


In comparison, in 2022 only 64% of respondents said the same and only 47% cited this in the year prior. Why is that? The answer seems to lie within the growing overall system complexity, due to the adoption of Kubernetes and cloud-native technologies and practices, as practitioners reported on the survey.

The findings from the report indicate that DevOps practices show signs of maturity and growth within organizations. One such example is that 45% of users have fully adopted and embraced DevOps practices — a 7% increase compared to 38% the year before. Similarly, there has been significant uptake of cloud adoption, as evidenced by the fact that 78% of organizations have either partially or fully transitioned to the cloud this year.

However, as maturity advances, a notable trend is surfacing: the mean time to recovery (MTTR) for production issues has been steadily increasing year over year, as witnessed in the above survey results. A leading cause for the substantial increase in MTTR for organizations is the rising adoption of cloud-native technologies and their intricacies.

Technologies such as Kubernetes generate abundant and complex data, making it difficult to monitor and troubleshoot. As such, these technologies were cited by 46% of respondents as the most difficult obstacle for organizations to gain full observability of their environment.

Kubernetes specifically stands out as a challenging tool for observability users, both in terms of monitoring and running it in production. Over 40% of survey responses stated that monitoring and gaining full observability is one of the key challenges of running Kubernetes. This statistic has grown from 2022, in which only 31% of respondents cited this exact figure. Outside of observability, respondents also noted challenges with Kubernetes security and cluster networking functionality.

In the 2023 report, security was highlighted as another area of major focus for DevOps practitioners, with over 30% of respondents stating that a shared model is used for security and observability. As these teams take on the responsibility of security, they are running into issues with centralizing security data and outlining clear roles and responsibilities for their teams.

One of the most challenging factors of security implementation is tool integration for cloud-native technologies. In fact, nearly 50% of respondents highlighted that implementing security for Kubernetes is the most difficult aspect of running it in production. The dynamic environment, distributed architecture, and overall complexity and scale of these technologies can further compound the difficulty of implementing comprehensive security practices.

The escalating complexity within IT systems also led to a surge in telemetry data volumes, consequently driving up the expenses associated with observability. In response, organizations have implemented various strategies to mitigate observability costs, including the adoption of open source tools as part of their overall approach.

The report shows that roughly 53% of this year's respondents stated that half or more of their observability tools are open source. The uptick from last year is notable, as only 37% of 2022 survey respondents indicated that half or more of their tools were open source. Respondents cited the lower cost of ownership (36%), ease of integration (47%), and benefits of the open source community (33%) as some of the key reasons for open source tool adoption.

As organizations face increasing complexity in observability, collecting and gathering insights from observability practitioners remains paramount. By utilizing their expertise, organizations can navigate the evolving landscape and make informed decisions to optimize their observability practices.

Dotan Horovits is Principal Developer Advocate at Logz.io

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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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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 ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

Observability Has a Complexity Problem

Dotan Horovits
Logz.io

The journey of maturing observability practices for users entails navigating peaks and valleys. Users have clearly witnessed the maturation of their monitoring capabilities, embraced DevOps practices, and adopted cloud and cloud-native technologies.

Notwithstanding that, we witness the gradual increase of the Mean Time To Recovery (MTTR) for production issues year over year. In this year's (2023) DevOps Pulse survey, conducted by Logz.io, 73% of respondents stated that it took multiple hours on average to resolve production issues.


In comparison, in 2022 only 64% of respondents said the same and only 47% cited this in the year prior. Why is that? The answer seems to lie within the growing overall system complexity, due to the adoption of Kubernetes and cloud-native technologies and practices, as practitioners reported on the survey.

The findings from the report indicate that DevOps practices show signs of maturity and growth within organizations. One such example is that 45% of users have fully adopted and embraced DevOps practices — a 7% increase compared to 38% the year before. Similarly, there has been significant uptake of cloud adoption, as evidenced by the fact that 78% of organizations have either partially or fully transitioned to the cloud this year.

However, as maturity advances, a notable trend is surfacing: the mean time to recovery (MTTR) for production issues has been steadily increasing year over year, as witnessed in the above survey results. A leading cause for the substantial increase in MTTR for organizations is the rising adoption of cloud-native technologies and their intricacies.

Technologies such as Kubernetes generate abundant and complex data, making it difficult to monitor and troubleshoot. As such, these technologies were cited by 46% of respondents as the most difficult obstacle for organizations to gain full observability of their environment.

Kubernetes specifically stands out as a challenging tool for observability users, both in terms of monitoring and running it in production. Over 40% of survey responses stated that monitoring and gaining full observability is one of the key challenges of running Kubernetes. This statistic has grown from 2022, in which only 31% of respondents cited this exact figure. Outside of observability, respondents also noted challenges with Kubernetes security and cluster networking functionality.

In the 2023 report, security was highlighted as another area of major focus for DevOps practitioners, with over 30% of respondents stating that a shared model is used for security and observability. As these teams take on the responsibility of security, they are running into issues with centralizing security data and outlining clear roles and responsibilities for their teams.

One of the most challenging factors of security implementation is tool integration for cloud-native technologies. In fact, nearly 50% of respondents highlighted that implementing security for Kubernetes is the most difficult aspect of running it in production. The dynamic environment, distributed architecture, and overall complexity and scale of these technologies can further compound the difficulty of implementing comprehensive security practices.

The escalating complexity within IT systems also led to a surge in telemetry data volumes, consequently driving up the expenses associated with observability. In response, organizations have implemented various strategies to mitigate observability costs, including the adoption of open source tools as part of their overall approach.

The report shows that roughly 53% of this year's respondents stated that half or more of their observability tools are open source. The uptick from last year is notable, as only 37% of 2022 survey respondents indicated that half or more of their tools were open source. Respondents cited the lower cost of ownership (36%), ease of integration (47%), and benefits of the open source community (33%) as some of the key reasons for open source tool adoption.

As organizations face increasing complexity in observability, collecting and gathering insights from observability practitioners remains paramount. By utilizing their expertise, organizations can navigate the evolving landscape and make informed decisions to optimize their observability practices.

Dotan Horovits is Principal Developer Advocate at Logz.io

Hot Topics

The Latest

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 ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.