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Exploring the Current State of Observability

Trevor Jones
Grafana Labs

In today's cloud-native world, companies are dealing with a deluge of telemetry data. But despite the concept of observability being more than six decades old, companies are still struggling to mature their observability practices. In turn, they can't tap into their data and extract insights that would otherwise help them improve performance, reliability, and cost.

According to Grafana Labs' 2024 Observability Survey, it doesn't matter what industry a company is in or the number of employees they have, the truth is: the more mature their observability practices are, the more time and money they save.


From AI to OpenTelemetry — here are four key takeaways from this year's report:

Too many tools

As businesses adopt new technologies and amass more data sources, they're also adding more observability tools to their toolkit to keep track of their systems. Collectively, respondents cited using 60+ technologies, and most use at least four observability tools.

But with more tools comes more complexity, which is why it's no surprise that among teams that have centralized observability, or a "single pane of glass" view of all their systems and data, 79% say it has saved them time or money as a result of lower MTTR, vendor fees, and operational costs.


How many observability technologies are you using?

AI isn't that helpful — yet

AI is seemingly everywhere these days, but in the observability space, there's more talk about its potential than there is about actually putting it into practice. Only 7% of respondents say they're using observability on AI systems and LLMs, while 46% say it's not even on their radar.

But for those who are thinking about using AI in observability, anomaly detection is cited as the most exciting use case. Roughly half also see value in predictive insights, dashboard generation, query assistance, and automated incident summaries.


Which AI/ML-powered features would be most valuable to your observability practice?

Open source and open standards reign supreme

Open source plays an important role in the observability landscape, with 80% of the most popular technologies cited in the survey being open source. It's no surprise that OpenTelemetry and Prometheus top that list and continue to gain traction. In fact, an overwhelming majority of respondents are investing in Prometheus (89%) or OpenTelemetry (85%) — and almost 40% of respondents use both in their operations, with more than half increasing their usage of each project over the past year.

Concern over cloud costs is growing, but observability maturity can help

Respondents were asked where they land on Grafana Labs' Observability Journey Maturity Model and while a little over half say their organization has taken a proactive approach, more checked reactive than systematic. Being reactive means that more often than not, customers are raising issues before observability teams can catch them, while taking a systemic approach means developing procedures and implementing tools to find issues before users, and minimize their impact.

The different approaches can result in very different outcomes, with 65% of those with a systematic approach having saved time or money through centralized observability, compared to just 35% of those who take a reactive approach.

And since more than half of respondents say cost is their biggest concern, moving towards a systemic approach to observability has never been more important.


From centralizing systems and embracing open source standards to exploring new ways to use AI and fostering more proactive strategies — organizations can streamline that complexity and pave the way for enhanced operational efficiency and effectiveness.

Methodology: Grafana Labs surveyed over 300 people from all around the world for the report. Respondents hailed from companies of all sizes, from less than 10 employees to more than 5,000, and spanned across all industries, including technology, financial services, retail, healthcare, and more, providing a holistic view of the diverse landscape of observability practices and challenges across various sectors and organizational scales.

Trevor Jones is Senior Content Manager at Grafana Labs

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Exploring the Current State of Observability

Trevor Jones
Grafana Labs

In today's cloud-native world, companies are dealing with a deluge of telemetry data. But despite the concept of observability being more than six decades old, companies are still struggling to mature their observability practices. In turn, they can't tap into their data and extract insights that would otherwise help them improve performance, reliability, and cost.

According to Grafana Labs' 2024 Observability Survey, it doesn't matter what industry a company is in or the number of employees they have, the truth is: the more mature their observability practices are, the more time and money they save.


From AI to OpenTelemetry — here are four key takeaways from this year's report:

Too many tools

As businesses adopt new technologies and amass more data sources, they're also adding more observability tools to their toolkit to keep track of their systems. Collectively, respondents cited using 60+ technologies, and most use at least four observability tools.

But with more tools comes more complexity, which is why it's no surprise that among teams that have centralized observability, or a "single pane of glass" view of all their systems and data, 79% say it has saved them time or money as a result of lower MTTR, vendor fees, and operational costs.


How many observability technologies are you using?

AI isn't that helpful — yet

AI is seemingly everywhere these days, but in the observability space, there's more talk about its potential than there is about actually putting it into practice. Only 7% of respondents say they're using observability on AI systems and LLMs, while 46% say it's not even on their radar.

But for those who are thinking about using AI in observability, anomaly detection is cited as the most exciting use case. Roughly half also see value in predictive insights, dashboard generation, query assistance, and automated incident summaries.


Which AI/ML-powered features would be most valuable to your observability practice?

Open source and open standards reign supreme

Open source plays an important role in the observability landscape, with 80% of the most popular technologies cited in the survey being open source. It's no surprise that OpenTelemetry and Prometheus top that list and continue to gain traction. In fact, an overwhelming majority of respondents are investing in Prometheus (89%) or OpenTelemetry (85%) — and almost 40% of respondents use both in their operations, with more than half increasing their usage of each project over the past year.

Concern over cloud costs is growing, but observability maturity can help

Respondents were asked where they land on Grafana Labs' Observability Journey Maturity Model and while a little over half say their organization has taken a proactive approach, more checked reactive than systematic. Being reactive means that more often than not, customers are raising issues before observability teams can catch them, while taking a systemic approach means developing procedures and implementing tools to find issues before users, and minimize their impact.

The different approaches can result in very different outcomes, with 65% of those with a systematic approach having saved time or money through centralized observability, compared to just 35% of those who take a reactive approach.

And since more than half of respondents say cost is their biggest concern, moving towards a systemic approach to observability has never been more important.


From centralizing systems and embracing open source standards to exploring new ways to use AI and fostering more proactive strategies — organizations can streamline that complexity and pave the way for enhanced operational efficiency and effectiveness.

Methodology: Grafana Labs surveyed over 300 people from all around the world for the report. Respondents hailed from companies of all sizes, from less than 10 employees to more than 5,000, and spanned across all industries, including technology, financial services, retail, healthcare, and more, providing a holistic view of the diverse landscape of observability practices and challenges across various sectors and organizational scales.

Trevor Jones is Senior Content Manager at Grafana Labs

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AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...