Production Issues Take Longer to Solve Despite Advancements in Observability

Learn more about Logz.io

Despite all of the advancement and maturity of observability systems, production issues take longer to resolve, according to the DevOps Pulse 2022 Report from Logz.io.

DevOps Pulse 2022 respondents consistently reported increasing complexity across their cloud environments — driven by everything from expanding microservices architecture to the proliferation and complexity of observability tools themselves — meaning they are struggling to maintain clear visibility, quickly resolve production issues and manage related monitoring costs.

64% of respondents reported an average MTTR of over an hour compared to 47% reported in the DevOps Pulse 2021.

What's more, 53.4% of respondents surveyed last year claimed to have resolved production issues within an hour on average — this year, that number dropped to 35.94%. This trend is being driven by factors ranging from growing cloud data volumes and systems complexity, to issues of observability tool sprawl, and the need for greater expertise among DevOps teams.

Additional key findings identified in the report include:

Observability costs and data volumes are growing concerns

27% of respondents ranked total cost of ownership and the large volumes of data being ingested into the tools among their main challenges in maintaining effective observability.

Observability is maturing

Some 77% of respondents rated their efforts over 3 on a scale of 1-5 when asked to indicate how extensive and mature their observability strategy has become — compared to last year, when more than 30% of respondents indicated a low score of two or less.

This is combined with a palpable increase in observability tool sprawl. 22% of those surveyed indicated their organization uses 5 or more observability tools, compared to 11% last year.


Open source capabilities are widely used

Open source capabilities are widely used by 90% of respondents. Open Source observability is the most common way to deploy observability.

A shared services model is growing in popularity

Over 85% of respondents indicated that their organizations operate using a shared services observability model in which a central team is responsible for implementing and maintaining tooling for other stakeholders such as app developers, SREs and DevOps teams.

Tools are further diversifying

This year’s survey responses highlighted the full range of observability tools in use by practitioners including log management and analysis (67%), and infrastructure monitoring (59%), followed somewhat behind by distributed tracing (27%) and APM (22%). Meanwhile some 21% said that they have deployed all of these capabilities

"Essentially, the 2022 DevOps Pulse Report reveals that there is too much data and the current model for observability is broken," says Tomer Levy, CEO of Logz.io. "As practices and implementations expand, organizations are becoming more concerned about the impact of data volumes on production quality and cost. In addition to offering an analysis of the evolving landscape, DevOps Pulse 2022 calls on organizations to think carefully about the impact of Kubernetes and Microservices and constantly evaluate telemetry data value and hygiene."

By closely tracking and analyzing data that is central to core observability requirements and finding ways to reduce MTTR despite identified challenges, organizations can better calculate associated spending and ROI. Emphasizing these factors combined with an increased focus on application and data security solve the challenges identified by DevOps teams and observability practitioners.

Related Links

Hot Topics

The Latest

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

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 ...

Why AI Is the Differentiator for Operationally Resilient Organizations

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? ...

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

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 ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Production Issues Take Longer to Solve Despite Advancements in Observability

Learn more about Logz.io

Despite all of the advancement and maturity of observability systems, production issues take longer to resolve, according to the DevOps Pulse 2022 Report from Logz.io.

DevOps Pulse 2022 respondents consistently reported increasing complexity across their cloud environments — driven by everything from expanding microservices architecture to the proliferation and complexity of observability tools themselves — meaning they are struggling to maintain clear visibility, quickly resolve production issues and manage related monitoring costs.

64% of respondents reported an average MTTR of over an hour compared to 47% reported in the DevOps Pulse 2021.

What's more, 53.4% of respondents surveyed last year claimed to have resolved production issues within an hour on average — this year, that number dropped to 35.94%. This trend is being driven by factors ranging from growing cloud data volumes and systems complexity, to issues of observability tool sprawl, and the need for greater expertise among DevOps teams.

Additional key findings identified in the report include:

Observability costs and data volumes are growing concerns

27% of respondents ranked total cost of ownership and the large volumes of data being ingested into the tools among their main challenges in maintaining effective observability.

Observability is maturing

Some 77% of respondents rated their efforts over 3 on a scale of 1-5 when asked to indicate how extensive and mature their observability strategy has become — compared to last year, when more than 30% of respondents indicated a low score of two or less.

This is combined with a palpable increase in observability tool sprawl. 22% of those surveyed indicated their organization uses 5 or more observability tools, compared to 11% last year.


Open source capabilities are widely used

Open source capabilities are widely used by 90% of respondents. Open Source observability is the most common way to deploy observability.

A shared services model is growing in popularity

Over 85% of respondents indicated that their organizations operate using a shared services observability model in which a central team is responsible for implementing and maintaining tooling for other stakeholders such as app developers, SREs and DevOps teams.

Tools are further diversifying

This year’s survey responses highlighted the full range of observability tools in use by practitioners including log management and analysis (67%), and infrastructure monitoring (59%), followed somewhat behind by distributed tracing (27%) and APM (22%). Meanwhile some 21% said that they have deployed all of these capabilities

"Essentially, the 2022 DevOps Pulse Report reveals that there is too much data and the current model for observability is broken," says Tomer Levy, CEO of Logz.io. "As practices and implementations expand, organizations are becoming more concerned about the impact of data volumes on production quality and cost. In addition to offering an analysis of the evolving landscape, DevOps Pulse 2022 calls on organizations to think carefully about the impact of Kubernetes and Microservices and constantly evaluate telemetry data value and hygiene."

By closely tracking and analyzing data that is central to core observability requirements and finding ways to reduce MTTR despite identified challenges, organizations can better calculate associated spending and ROI. Emphasizing these factors combined with an increased focus on application and data security solve the challenges identified by DevOps teams and observability practitioners.

Related Links

Hot Topics

The Latest

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

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 ...

Why AI Is the Differentiator for Operationally Resilient Organizations

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? ...

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

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 ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...