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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 MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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 ...

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 MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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 ...