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.

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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

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Almost Half of AI-Generated Code Fails in Production

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

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 ...

Reliability Is the New Bottleneck of Innovation

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 ...

5 Takeaways from the Observability Forecast for Retail and eCommerce

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 ...