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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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...