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Observability Survey 2023: Centralization Saves Time and Money

Organizations are challenged by tool sprawl and data source overload, according to the Grafana Labs Observability Survey 2023, with 52% of respondents reporting that their companies use 6 or more observability tools, including 11% that use 16 or more.

To combat this, 70% of respondents say their companies have centralized observability, and of those, 83% have saved time or money as a result.

Other key takeaways from the report include:

Tool and data overload varies across industry and company size

Larger organizations tend to use more data sources: 41% of companies with more than 1,000 employees pull in 10+ data sources, compared to just 7% for companies with 100 employees or fewer.

Industries leading in observability tool usage were financial services (31% use 10 or more tools) and government (27% use 10 or more tools).

Organizations are at different stages in their observability journey

Nearly one-third of respondents have not centralized observability yet, and some industries are further along than others.

For example, 70% of financial sector companies have adopted centralized observability and saved time and money as a result, compared to 58% across all sectors.

Not all ROI is the same

Different organizations have different objectives with their observability strategies. Yes, saving money is the overarching goal, but there are multiple paths to get there, including MTTx improvements, less toil and infrastructure maintenance, better adoption, increased developer productivity, less complexity, service level objectives (SLOs), better capacity planning, and better alerting and visibility.

Among all respondents, 37% say they prioritize capacity planning when correlating data, 25% prioritize cost control, and 9% prioritize profitability and margin calculation.

Accountability, market maturity comes to observability

The proper execution of SLOs is a good sign of a mature observability strategy. Most respondents say they are using them or moving in that direction, but they're not all at the same stage. Moreover, only slightly more are actively using SLIs/SLOs (28%) than those that don't have them on their radar (21%).

Methodology: Grafana Labs developed the survey — which included questions about the tools, strategies, benefits, and challenges around observability — and solicited responses through newsletters, live events, social media, and its own website. The company also conducted interviews with observability practitioners about how their companies are addressing some of the benefits and challenges presented in our key findings.

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Observability Survey 2023: Centralization Saves Time and Money

Organizations are challenged by tool sprawl and data source overload, according to the Grafana Labs Observability Survey 2023, with 52% of respondents reporting that their companies use 6 or more observability tools, including 11% that use 16 or more.

To combat this, 70% of respondents say their companies have centralized observability, and of those, 83% have saved time or money as a result.

Other key takeaways from the report include:

Tool and data overload varies across industry and company size

Larger organizations tend to use more data sources: 41% of companies with more than 1,000 employees pull in 10+ data sources, compared to just 7% for companies with 100 employees or fewer.

Industries leading in observability tool usage were financial services (31% use 10 or more tools) and government (27% use 10 or more tools).

Organizations are at different stages in their observability journey

Nearly one-third of respondents have not centralized observability yet, and some industries are further along than others.

For example, 70% of financial sector companies have adopted centralized observability and saved time and money as a result, compared to 58% across all sectors.

Not all ROI is the same

Different organizations have different objectives with their observability strategies. Yes, saving money is the overarching goal, but there are multiple paths to get there, including MTTx improvements, less toil and infrastructure maintenance, better adoption, increased developer productivity, less complexity, service level objectives (SLOs), better capacity planning, and better alerting and visibility.

Among all respondents, 37% say they prioritize capacity planning when correlating data, 25% prioritize cost control, and 9% prioritize profitability and margin calculation.

Accountability, market maturity comes to observability

The proper execution of SLOs is a good sign of a mature observability strategy. Most respondents say they are using them or moving in that direction, but they're not all at the same stage. Moreover, only slightly more are actively using SLIs/SLOs (28%) than those that don't have them on their radar (21%).

Methodology: Grafana Labs developed the survey — which included questions about the tools, strategies, benefits, and challenges around observability — and solicited responses through newsletters, live events, social media, and its own website. The company also conducted interviews with observability practitioners about how their companies are addressing some of the benefits and challenges presented in our key findings.

The Latest

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 ...

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

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 ...

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 ...

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 ...