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11 Key Observability Trends for 2022 - Part 2

Buddy Brewer
New Relic

These are the trends that will set up your engineers and developers to deliver amazing software that powers amazing digital experiences that fuel your organization's growth in 2022 — and beyond. This is Part 2.

Start with: 11 Key Observability Trends for 2022 - Part 1

6. Usage-based pricing tips the scales in the customer's favor

The pricing structures of many monitoring tools actually discourage IT leaders and engineers and developers from ingesting all of their data because their pricing is confusing, difficult to predict and scale, and generally just too expensive. As a result, organizations compromise on visibility. In fact, according to the Observability Forecast, 60% of global respondents still monitor telemetry data at the application level only, leaving massive amounts of data unmonitored in their software stack.

The move to modern observability and increasing its adoption includes shifting from legacy subscriptions to usage-based consumption and pricing models that align with customer success. With modern consumption-based pricing, organizations get full visibility into all of their telemetry, and only pay for what they use. With digital businesses relying on increasingly complex software systems, IT leaders will start demanding this pricing model from their observability vendors because it's easy to understand, predict, and scale. Plus, usage-based pricing will be given preference as it promises to remove upfront guesswork on usage and the shelfware frustrations and overage penalties that often follow.

How to Seize the Trend

Learn how you might be able to achieve even more value while making your observability platform (and your organization's data) available to more engineers and developers across the software lifecycle. It's a great first step to seize all of the first six trends of this Observability Trends report.

7. Observability shifts from "it's complicated" to an "open" relationship

Having a variety of tools to choose from creates challenges in telemetry data collection. Organizations find themselves managing multiple libraries for logging, metrics, and traces, with each vendor having its own APIs, SDKs, agents, and collectors. An open source, community-driven approach to observability will gain steam in 2022 to remove unnecessary complications by tapping into the latest advancements in observability practice.

With continued innovation and investment, observability will work out-of-the-box by default and use open standards to make it even more accessible to all. In fact, Gartner predicts that by 2025, 70% of new cloud-native application monitoring will use open source instrumentation rather than vendor-specific agents for improved interoperability. Open source standards such as OpenTelemetry and OpenMetrics are converging in the industry, preventing vendor lock-in and bringing us a step closer to unified observability.

How to Seize the Trend

Encourage your engineering teams to tap into open source technologies like OpenTelemetry to advance their observability practice and capabilities.

8. The rising tide of Kubernetes and containers floats observability boats too

With the Observability Forecast highlighting that 88% of IT decision makers are exploring Kubernetes, with 25% of respondents conducting research, 25% evaluating, 29% in development, and 10% in production, the popularity of Kubernetes continues to explode. This growth also brings challenges and gaps from the necessary cultural shift to technology trends and advancements. As the next wave of microservices and more stateful applications are deployed on Kubernetes and container-based platforms, there is a need for more visibility into operations, as well as tools for self-defense and self-healing against malicious applications (both intentional and inadvertent).

Looking forward, as teams use more microservices and serverless architectures, they will reduce the amount of interaction with the underlying infrastructure. This allows more focus on the application and other business needs, and will lead to an improved developer experience in 2022.

How to Seize the Trend

It's no secret that most Kubernetes monitoring solutions, including amazing tools like Prometheus, are designed primarily for operations teams, which made sense in the early days. However, that's not the case anymore. When your team is looking for an actionable observability platform, make sure they request tools that are purpose-built for developers to identify performance bottlenecks faster with code-level insights. This will help your engineering teams to seamlessly drill down into both application-level and infrastructure-level behavior, so they can correlate the impact that application changes are having on the infrastructure and vice versa.

9. Increasing adoption of a DevOps mindset for observability

By adopting a DevOps mindset and embracing agile rather than waterfall development, engineering teams will be able to shift from a culture of blame and finger-pointing to one of empathy and ongoing improvement. This will position engineers and developers to release better software, faster, and meet the growing expectations of their organizations. Just as digital companies have updated the way they plan, build, deploy, and operate software, they will now look to modernize their approach to monitoring that software with observability tools that benefit not only the DevOps team, but the entire organization.

How to Seize the Trend

With increasing pressure mounting on engineering teams across industries, observability is key to delivering a positive user experience in the face of ever-expanding software applications. Adopting a DevOps culture will enable your teams to cut through the noise and focus on the performance issues that have the biggest impact on your business, customers, and employees.

10. Observability cultivates collaboration among engineering teams

Observability is quickly becoming the industry gold standard to help software engineering teams and developers through the inevitable times when something goes wrong in the continuous integration/continuous deployment (CI/CD) pipeline. The reasons are clear: When the CI/CD pipeline is observable, engineering teams have more confidence in their code, and they can move faster to implement fixes when needed. And when observability platforms enable collaboration on code directly within the developer environment (IDE), asking questions for better understanding, highlighting potential errors, and partnering on code becomes second nature — as does delivering even better outcomes as a matter of engineering practice.

Looking forward, modern observability will enable and cultivate a culture of collaboration across software engineering and development disciplines by allowing teams to better collaborate. The result will be stronger teams, procedures, and alert systems that improve the way engineers handle monitoring and incident detection throughout the software lifecycle.

How to Seize the Trend

As you build your observability team in today's distributed workplace, make sure all your SREs and developers have access to your observability tools. This will enable all your engineers around the world to have access to real-time data for decision making, and make cross-functional collaboration more efficient and easier.

11. Observability continues to improve service and reliability

As organizations work in a world that increasingly relies more on digital services — due to COVID-19 or otherwise — the data from these applications can give us greater detail into real-world performance. For example, an increase in web traffic or application demand will usually be linked to higher levels of transactions and business. This increase can be seen and tracked across application components, but it can also be seen in revenue too. That's why observability data has a greater purpose beyond just showing us how well our app components are performing over time. Instead, moving forward this data will be used to improve both the ability to handle risks and show where business results are affected.

How to Seize the Trend

It's far more common today for your engineering teams to tackle service and reliability issues on a regular basis. When planning for next year's IT infrastructure, think about observability from a reliability perspective. This will ensure that your applications are better able to handle issues like a cloud outage or service failure.

Buddy Brewer is GVP and GM at New Relic

Hot Topics

The Latest

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 ...

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 ...

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

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 ...

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 ...

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 ...

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 ...

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 ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

11 Key Observability Trends for 2022 - Part 2

Buddy Brewer
New Relic

These are the trends that will set up your engineers and developers to deliver amazing software that powers amazing digital experiences that fuel your organization's growth in 2022 — and beyond. This is Part 2.

Start with: 11 Key Observability Trends for 2022 - Part 1

6. Usage-based pricing tips the scales in the customer's favor

The pricing structures of many monitoring tools actually discourage IT leaders and engineers and developers from ingesting all of their data because their pricing is confusing, difficult to predict and scale, and generally just too expensive. As a result, organizations compromise on visibility. In fact, according to the Observability Forecast, 60% of global respondents still monitor telemetry data at the application level only, leaving massive amounts of data unmonitored in their software stack.

The move to modern observability and increasing its adoption includes shifting from legacy subscriptions to usage-based consumption and pricing models that align with customer success. With modern consumption-based pricing, organizations get full visibility into all of their telemetry, and only pay for what they use. With digital businesses relying on increasingly complex software systems, IT leaders will start demanding this pricing model from their observability vendors because it's easy to understand, predict, and scale. Plus, usage-based pricing will be given preference as it promises to remove upfront guesswork on usage and the shelfware frustrations and overage penalties that often follow.

How to Seize the Trend

Learn how you might be able to achieve even more value while making your observability platform (and your organization's data) available to more engineers and developers across the software lifecycle. It's a great first step to seize all of the first six trends of this Observability Trends report.

7. Observability shifts from "it's complicated" to an "open" relationship

Having a variety of tools to choose from creates challenges in telemetry data collection. Organizations find themselves managing multiple libraries for logging, metrics, and traces, with each vendor having its own APIs, SDKs, agents, and collectors. An open source, community-driven approach to observability will gain steam in 2022 to remove unnecessary complications by tapping into the latest advancements in observability practice.

With continued innovation and investment, observability will work out-of-the-box by default and use open standards to make it even more accessible to all. In fact, Gartner predicts that by 2025, 70% of new cloud-native application monitoring will use open source instrumentation rather than vendor-specific agents for improved interoperability. Open source standards such as OpenTelemetry and OpenMetrics are converging in the industry, preventing vendor lock-in and bringing us a step closer to unified observability.

How to Seize the Trend

Encourage your engineering teams to tap into open source technologies like OpenTelemetry to advance their observability practice and capabilities.

8. The rising tide of Kubernetes and containers floats observability boats too

With the Observability Forecast highlighting that 88% of IT decision makers are exploring Kubernetes, with 25% of respondents conducting research, 25% evaluating, 29% in development, and 10% in production, the popularity of Kubernetes continues to explode. This growth also brings challenges and gaps from the necessary cultural shift to technology trends and advancements. As the next wave of microservices and more stateful applications are deployed on Kubernetes and container-based platforms, there is a need for more visibility into operations, as well as tools for self-defense and self-healing against malicious applications (both intentional and inadvertent).

Looking forward, as teams use more microservices and serverless architectures, they will reduce the amount of interaction with the underlying infrastructure. This allows more focus on the application and other business needs, and will lead to an improved developer experience in 2022.

How to Seize the Trend

It's no secret that most Kubernetes monitoring solutions, including amazing tools like Prometheus, are designed primarily for operations teams, which made sense in the early days. However, that's not the case anymore. When your team is looking for an actionable observability platform, make sure they request tools that are purpose-built for developers to identify performance bottlenecks faster with code-level insights. This will help your engineering teams to seamlessly drill down into both application-level and infrastructure-level behavior, so they can correlate the impact that application changes are having on the infrastructure and vice versa.

9. Increasing adoption of a DevOps mindset for observability

By adopting a DevOps mindset and embracing agile rather than waterfall development, engineering teams will be able to shift from a culture of blame and finger-pointing to one of empathy and ongoing improvement. This will position engineers and developers to release better software, faster, and meet the growing expectations of their organizations. Just as digital companies have updated the way they plan, build, deploy, and operate software, they will now look to modernize their approach to monitoring that software with observability tools that benefit not only the DevOps team, but the entire organization.

How to Seize the Trend

With increasing pressure mounting on engineering teams across industries, observability is key to delivering a positive user experience in the face of ever-expanding software applications. Adopting a DevOps culture will enable your teams to cut through the noise and focus on the performance issues that have the biggest impact on your business, customers, and employees.

10. Observability cultivates collaboration among engineering teams

Observability is quickly becoming the industry gold standard to help software engineering teams and developers through the inevitable times when something goes wrong in the continuous integration/continuous deployment (CI/CD) pipeline. The reasons are clear: When the CI/CD pipeline is observable, engineering teams have more confidence in their code, and they can move faster to implement fixes when needed. And when observability platforms enable collaboration on code directly within the developer environment (IDE), asking questions for better understanding, highlighting potential errors, and partnering on code becomes second nature — as does delivering even better outcomes as a matter of engineering practice.

Looking forward, modern observability will enable and cultivate a culture of collaboration across software engineering and development disciplines by allowing teams to better collaborate. The result will be stronger teams, procedures, and alert systems that improve the way engineers handle monitoring and incident detection throughout the software lifecycle.

How to Seize the Trend

As you build your observability team in today's distributed workplace, make sure all your SREs and developers have access to your observability tools. This will enable all your engineers around the world to have access to real-time data for decision making, and make cross-functional collaboration more efficient and easier.

11. Observability continues to improve service and reliability

As organizations work in a world that increasingly relies more on digital services — due to COVID-19 or otherwise — the data from these applications can give us greater detail into real-world performance. For example, an increase in web traffic or application demand will usually be linked to higher levels of transactions and business. This increase can be seen and tracked across application components, but it can also be seen in revenue too. That's why observability data has a greater purpose beyond just showing us how well our app components are performing over time. Instead, moving forward this data will be used to improve both the ability to handle risks and show where business results are affected.

How to Seize the Trend

It's far more common today for your engineering teams to tackle service and reliability issues on a regular basis. When planning for next year's IT infrastructure, think about observability from a reliability perspective. This will ensure that your applications are better able to handle issues like a cloud outage or service failure.

Buddy Brewer is GVP and GM at New Relic

Hot Topics

The Latest

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 ...

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 ...

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

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 ...

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 ...

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 ...

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 ...

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 ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.