Software environments are growing more complex, and customer expectations are higher than ever. With development moving from monoliths to microservices and users demanding instant digital experiences, businesses face new challenges in keeping systems fast, reliable, and secure.
DevOps observability has become key in addressing these problems, giving teams deep insights into system performance and helping detect and resolve problems much faster. It provides the feedback required to keep projects on track without sacrificing quality.
When applications run slowly or crash unexpectedly, users get frustrated, and businesses risk losing both revenue and trust. In fact, more than 75% of organizations report that their mean time to recovery (MTTR) exceeds multiple hours. This makes system failures even more expensive and highlights the need for better solutions. With real-time insights, teams can reduce MTTR by 53% and cut downtime by almost 50%.
DevOps observability helps not only keep systems running smoothly but it also reduces costs. For example, businesses with mature observability strategies lose around $2.5 million per year due to downtime, compared to $23.8 million for those in the early stages.
This clear guide discusses observability in DevOps to help you understand its essentials, from key components to implementation strategies, with insights from Neontri’s experience.
Key takeaways:
- DevOps observability means monitoring and analyzing a system’s health and activities based on its output.
- Businesses with DevOps observability in place can improve app performance and availability, fix issues quicker, and make better data-driven decisions. But they may also face talent shortages, high costs, or integration issues.
- Before implementing observability into your DevOps practices, make sure to set clear goals, choose the right tools, and build an observability culture in your organization.
Understanding DevOps observability
Observability in DevOps refers to the process of getting valuable and real-time insights about the system’s internal state based on its external outputs. It involves collecting, analyzing, interpreting, and visualizing data to monitor how applications perform in complex and distributed environments.
With this approach, DevOps teams can quickly identify problems and understand “why” a system behaves the way it does. Since DevOps is all about continuous delivery of a solution, it becomes important to get the right feedback at the right time.
Most DevOps models use CI/CD (Continuous Integration and Continuous Delivery), therefore knowing whether new changes have the potential to make or break the system is critical. This helps:
- Resolve issues
- Improve performance
- Prevent incidents
DevOps observability brings many advantages that enhance system efficiency, smooth out operations, and allow teams to address issues without delay.
When organizations have poor observability habits, they’re simply flying blind during critical system outages and can’t determine why they’re experiencing continuous declines in performance. This could naturally lead to longer development cycles, poorer competitive edge, dissatisfied customers, and potential revenue loss.
Key components of DevOps observability
Observability uses three main types of telemetry data: metrics, logs, and traces. These data results show different insights into how systems work. When combined, they offer a complete view of the application to spot issues that affect business goals.
Metrics are quantitative measurements that track system performance across time. They’re often used for CPU and memory usage, response times, user sessions or network traffic. Retail apps may slow down during busy periods like Black Friday, which can have a negative impact on user experience. These indicators help teams identify performance bottlenecks and add more tools to manage the load.
The example:
Logs are chronological, detailed, and time-stamped records of events that take place within applications and systems. They provide information about who, what, when, and where, helping analyze the problem and determine its underlying cause (like a query that’s too long, a failed API request or an unauthorized access attempt). Search service logs can be used to find slow queries in an e-commerce web app.
The example:
Traces track the requests or transactions across different system components and services so that it’s easy to detect and fix issues. These cover API calls, logins, network requests, file uploads, and database queries that follow a user request through microservices to find where delays occur.
The example:
Best practices for implementing observability in DevOps
To implement observability in DevOps workflow successfully, adopt the following best practices, some of them are based on Neontri’s experience.
Recommendation #1: Set clear goals—businesses don’t need to monitor everything. While IT platforms generate a lot of data, not all of it might be useful. Decide what to keep an eye on. So, whether it’s system reliability, error rates, application speed or uptime, pay attention to the key measures that have the most direct effect on the business goals.
Recommendation #2: Incorporate the three pillars of observability—you can get a full picture of how a system works from logs, data, and traces. These three things will work together to give you information about system events, speed, and request flows.
Recommendation #3: Centralize data—keep all the data in one place. As a result, teams will find it simpler to correlate it and identify problems throughout the entire infrastructure.
Recommendation #4: Choose the right observability tools—make sure the tools fit into your CI/CD pipeline to collect necessary data.
| Small projects (startups, MVPs, small teams) | Medium projects (growing businesses, mid-sized applications) | Large projects (enterprises, high-scale systems) | |
| Logging | Logtail, Papertrail, Loki | Logz.io, Datadog Logs, Graylog | Splunk, Elastic Stack (ELK), AWS CloudWatch Logs Neontri’s recommendation: Elastic Stack |
| Metrics | Prometheus, StatsD, AWS CloudWatch (basic) | Datadog, New Relic, OpenTelemetry | Grafana Mimir, Amazon Managed Prometheus, Google Cloud Monitoring Neontri’s recommendation: Grafana, Prometheus |
| Tracing | Jaeger, Zipkin (self-hosted) | OpenTelemetry, Honeycomb | AWS X-Ray, New Relic, Dynatrace Neontri’s recommendation:AWS X-Ray |
| APM (Application Performance Monitoring) | SigNoz, New Relic Lite | Datadog APM, Instana, AppDynamics | Dynatrace, New Relic, Splunk APM Neontri’s recommendation: Dynatrace |
| Infrastructure monitoring | Netdata, Prometheus | Datadog, Site24x7, Zabbix | Nagios, LogicMonitor, ThousandEyes Neontri’s recommendation: LogicMonitor |
| Error tracking | Sentry, Rollbar | Raygun, Bugsnag | AppDynamics, Splunk Observability Neontri’s recommendation: AppDynamics |
| Cloud-native and Kubernetes | k6, Prometheus | OpenTelemetry, Grafana Cloud | Dynatrace, Istio, Azure Monitor Neontri’s recommendation: Dynatrace |
| SIEM and security monitoring | Wazuh, Security Onion | Splunk Security Cloud, Elastic SIEM | IBM QRadar, Microsoft Sentinel Neontri’s recommendation: IBM QRadar |
Neontri expert’s advice:
Selecting the right observability tools is a strategic decision that goes beyond feature checklists. The key is to develop a holistic strategy that aligns technology with your specific organizational needs, infrastructure maturity, and growth trajectory.
When evaluating tools, look for scalability, ease of integration, and the ability to provide actionable insights rather than simply collecting data. Moreover, you should also take into consideration total cost of ownership, learning curve, vendor support, and how well the tool will adapt to your evolving technology stack.
Recommendation #5: Set up alerts only for critical events—don’t overload teams with notifications so as not to distract them. They should be notified only when something important happens.
Recommendation #6: Define SLAs and SLOs—establish clear system reliability and performance goals with Service-Level Agreements (SLAs) and Service-Level Objectives (SLOs). Observability tools help monitor these goals and make sure they are always met.
Recommendation #7: Integrate it into your workflow—connect tools to automation frameworks to enable continuous monitoring and trigger alerts for potential problems.
Recommendation #8: Automate the process—to avoid having to manually verify everything, set up dashboards and alarms that are updated automatically.
Recommendation #9: Create a culture of observability—promote collaboration and continuous improvement across teams.
Challenges of observability in DevOps
Even though observability is becoming an important part of modern DevOps, companies that decide to adopt it might face some hurdles. Here are a few common challenges organizations can come across along with possible solutions.
| Challenge | Description | Solution |
| Skill gaps | To adopt DevOps observability, teams need to know how to instrument their systems, analyze data, and plan architecture. | Give your teams necessary knowledge, plan training, and invest in professional development opportunities. If necessary, hire experienced tech specialists. Neontri’s tip: Set up mentorship programs and encourage regular meetings where team members can share their knowledge. |
| Data overload | Too much complex data slows progress and introduces inefficiencies. | Use filtering and sampling to collect only the most relevant information. Neontri’s tip: Instead of storing and archiving everything, set data retention policies for specific time periods that reduce storage needs. |
| High costs | It can be expensive to support the infrastructure, storage, and processing needed for comprehensive DevOps observability, especially for large enterprises. | Adopt scalable, cloud-based observability platforms and leverage open-source tools when possible. Neontri’s tip: Focus on the most important data at first, and then slowly add more to the observability setup. |
| Integration with legacy systems | Modern tools might not work well with older systems, which complicates the integration process. | Use middleware or APIs to connect legacy systems with contemporary tools. Neontri’s tip: Assess current systems to pinpoint integration gaps and develop a phased plan for implementation. |
| Alert fatigue | Too many notifications can overwhelm teams, which can then start to miss important issues or respond more slowly. | Simplify alerting mechanisms by setting clear limits and cutting down on false alarms. Neontri’s tip: Review alert settings often to make sure that the team only gets important messages. |
| Cultural barriers | Teams might be resistant to DevOps observability practices because of old habits and skill gaps. | Encourage teams to be more open to change and to have an “innovation mindset.” Neontri’s tip: Build a culture of continuous learning and experimentation. |
How to choose the right DevOps observability tools
With so many different alternatives available, selecting the best DevOps observability platform can be hard. Ideally, it should be able to collect and process critical system information and fit well with the current infrastructure.
Think about the following aspects more carefully before choosing any tools:
Tip #1: Metrics and functionality—check if the software you’re considering can track the key metrics you’re interested in. In case some of them aren’t included, ensure the tool can create custom ones and adapt to the needs of your team. It should also collect logs from the necessary sources and send alerts if any important numbers are outside their normal range.
Tip #2: DevOps pipeline integration—choose a technology that integrates with the DevOps pipeline so the observability process doesn’t weigh down the continuous testing approach. Verify compatibility with your CI/CD workflow, such as GitLab CI or Jenkins.
Tip #3: Ongoing scalability needs—observability software should scale with your needs. So, before making a decision, it might be a good idea to test it under heavy load to see how well it performs during busy times.
Tip #4: Learning curve—when considering possible observability tools, pay attention to the user interface and overall complexity. The more complicated the platform, the more time the team will need to learn to use it. To make the integration process less cumbersome, find any available documentation or training.
Tip #5: Budgeted cost and support—review the price plans and support levels. To get the complete picture, look at short and long-term costs, upgrades, and extra features.
Myths and misconceptions around DevOps observability
The more popular something gets, the more myths arise around it. This is the case with DevOps observability too—most people still don’t understand what it is and how it functions. Let’s take a look at the most common myths and debunk them.
| Myth | Reality |
| Observability is just monitoring | Monitoring collects system data on performance and behaviour. Observability, on the other hand, uses that information to gain insights into how systems work, which allows teams to quickly detect and resolve issues before they impact users. |
| More data means better observability | Having more data doesn’t improve observability. Without context and correlation, it can create noise, making it more difficult to spot real issues. |
| DevOps observability systems alone solve all problems | Tools are important, but building a strong observability culture with experienced specialists and clearly defined processes is equally important. |
| AI-driven observability always predicts issues accurately | While artificial intelligence provides valuable insights, human workers still need to be there to make sure it’s done right so they can understand the data and make smart decisions. |
| Only SREs (Site Reliability Engineers) need to care about observability | Many stakeholders involved in software development and operations benefit from an observability solution. This includes developers, operations teams, product managers, and even business partners. |
| Observability in DevOps is always expensive | It doesn’t have to break the bank because there are various pricing models companies can choose from. |
| Observability is only relevant for large and complex systems | DevOps observability helps all systems. Even simple programs can use DevOps observability to detect problems and improve performance. |
| Observability is a one-time setup | Systems change and that’s why they need to be continuously improved. |
Trends in DevOps observability
As DevOps observability becomes more and more central to maintaining systems fast and stable, there are several key trends that will shape its future.
Trend #1: OpenTelemetry (OTel)
OpenTelemetry has become a significant trend in DevOps observability. It allows for standardizing logging, metrics, and tracing—and now profiling, which captures real-time performance data. That makes it simpler for companies to get a clear picture of their system and detect issues earlier.
Trend #2: AI-driven predictive operations
Traditional monitoring identifies problems only after they’ve occurred. Artificial intelligence systems work better. By learning data patterns, they can predict failures, like performance bottlenecks or memory leaks, before they even take place. As a result, organizations can reduce downtime, maximize resources, and build more effective systems.
Trend #3: Unified observability platforms
Some organizations are considering solutions that collect all of their logs, traces, data, events, and profiles in one location. They can eliminate data silos between monitoring tools and enhance data correlation. This makes it easier and quicker to find problems.
Trend #4: Shift to flexible pricing models
Businesses can now choose from a variety of pricing alternatives as they try to keep expenses down. Many choose pay-as-you-go plans because they allow them to expand tools without having to make large upfront payments. They only pay for what they use, which lowers observability costs without compromising functionality. AWS CloudWatch, for instance, charges based on the number of logs it processes, such as paying per million log events.
Optimize your development process with Neontri’s DevOps services
Going through this article might give you an example of how difficult developing and managing applications can be. Here, at Neontri, we try to make this easier by providing expertise and support. With our DevOps services, you will set up effective monitoring, spot performance issues, and keep your apps fast, reliable, and secure.
With over 10 years of experience and 400+ successful projects, we know how to cut down on the time it takes to release new features and updates, while improving the quality of your software. Reach out to us to discuss the details.
Final thoughts
As modern software environments become more complex and customer expectations continue to rise, DevOps observability provides companies with real-time insights into system health, helping them catch and fix issues early. While challenges such as skill gaps, data overload, and integration with legacy systems exist, these can be overcome with the right tools and strategies, making observability an important part of any DevOps workflow.
FAQ
How does observability differ from traditional monitoring in DevOps?
Monitoring focuses on predefined metrics to track system health, while observability provides deeper insights by analyzing logs, metrics, and traces to understand why issues occur. Observability is more dynamic and suited for modern, complex systems.
What challenges do banks face when integrating observability with DevOps?
Banks need to handle large volumes of sensitive data, ensure compliance with regulations, and integrate observability tools into legacy systems. These are rather challenging tasks that require careful planning and strong security measures.
How can DevOps observability enhance customer satisfaction in e-commerce?
It helps e-commerce sites quickly find and fix performance problems so that users have a good experience. This means less downtime, better load times, and more trust from customers.
What are the main challenges in implementing DevOps observability in e-commerce?
E-commerce companies often have trouble with too much data, integrating tools across microservices, and keeping real-time insight when traffic is high. To deal with these, they need tools that can be scaled up and data screening that works well.
How does AI enhance observability in DevOps?
Artificial intelligence automates anomaly detection, identifies root causes faster, and predicts potential issues. As a result, DevOps teams can manage complex systems more efficiently and proactively.
Sources
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