What is DevOps monitoring?

DevOps monitoring uses telemetry (metrics, logs and traces) and event data to gather feedback that helps drive full-stack observability, proactive issue detection and faster software delivery in DevOps pipelines

DevOps monitoring tools provide deep visibility into the entire software development process, from coding and building to deployment and optimization. They track the health and performance of infrastructure, network components, applications, continuous integration/continuous delivery (CI/CD) pipelines, application programming interfaces (APIs) and dependencies to improve software products and the way they’re delivered.

While monitoring and observability—the ability to understand a complex system’s internal state based on external outputs—are often considered distinct disciplines, monitoring efforts often support observability, especially in DevOps environments. Observability practices, which can include DevOps monitoring, help explain why systems behave a certain way, especially in microservices and cloud-native architectures. Often, enterprises rely on advanced monitoring platforms to achieve observability, and observability platforms help guide their monitoring strategies.

Ultimately, advanced monitoring platforms enable DevOps teams to take advantage of comprehensive issue detection and remediation capabilities, so they can build streamlined, highly scalable software applications and deliver seamless customer experiences.

DevOps monitoring empowers teams and enterprises to move away from a reactive, firefighting approach to software delivery and management to a proactive, preventive approach. It provides continuous, end-to-end visibility across distributed cloud-based architectures, an invaluable asset in environments where failures often emerge from subtle interactions between services instead of obvious individual faults. 

Let’s say, for instance, that patients on a healthcare appointment platform are complaining about video appointments that don’t start, but the platform’s video session service shows no major downtime. A high-quality monitoring solution can use telemetry to discover that the failed visits correlate with time periods where the platform’s third-party video session service saw small but noticeable increases in latency and rate limiting issues, which delayed room creation for patients.

From a DevOps perspective, continuous monitoring is a collaboration enabler. All stakeholders (development, operations, site reliability engineering (SRE), security and product teams) work from a shared, single source of truth, often within a single pane of glass. Shared telemetry enables continuous feedback loops, which help DevOps teams make backlog decisions, architectural changes and process improvements throughout the DevOps lifecycle.

DevOps monitoring also supports shift-left practices. “Shifting left” moves vital development tasks—such as testing, issue resolution and security—to earlier stages of the software development lifecycle. Code defects and security vulnerabilities are discovered (ideally) during coding instead of deployment.

As monitoring tools gather data, they feed it back into the DevOps pipeline. Using live environment telemetry and incident patterns, monitoring can reveal issues that escape early testing rounds. Monitoring hooks send the post-production data and insights back to DevOps teams, who can use the information to design new preproduction tests that catch similar defects in development in future software iterations.

DevOps monitoring covers several distinct—but related—types of IT monitoring that together provide complete visibility into systems, applications and user behavior. Each type focuses on instrumenting specific layers or components of the software delivery lifecycle, from infrastructure health to end-user interactions.

Types of monitoring include:

Infrastructure monitoring focuses on the servers, networks, databases, virtual machines (VMs), cloud services, operating systems and disk storage platforms that support applications. The goal is to ingest telemetry from these resources to help ensure that the computing network’s backend performs as expected and that anomalies are found before they affect end users.

Infrastructure monitoring tools collect metrics such as CPU usage, memory consumption, network throughput, latency and uptime. Then, they ship the information to a centralized monitoring platform that stores, analyzes and visualizes the data for DevOps teams.

Network monitoring tools track data flow between routers, switches, firewalls, load balancers, VMs, containers and cloud workloads, watching for connectivity issues and misconfigurations that can slow down data transmission. They also feed information into CI/CD pipelines and incident management workflows, so DevOps teams can set up automated alerts and rollbacks based on real-time data.

DevOps practices involve rapid software releases and high reliability, so teams must have the tools they need to find and fix issues before they cause deployment failures. Network monitoring tools help address this need, automating tasks that a network administrator would otherwise perform manually.

In mature DevOps environments, network monitoring is integrated with chat tools, log management tools, ticketing systems and on-call staff rotations so that alerts become actionable incidents. The same data can help DevOps teams streamline post-incident reviews, enabling them to fine-tune infrastructure, update configurations and improve future releases based on what network telemetry reveals.

Application performance monitoring tools monitor the entire application stack, including the app’s framework (Java or .NET, for instance), operating system, databases, APIs and middleware and web application servers. They measure how quickly and reliably applications respond to user actions, and whether applications are available when users need them.

Application performance monitoring gives DevOps teams visibility into everything from high-level user experiences to low-level technical components, like databases and external services.

For example, many monitoring tools provide detailed transaction traces and code-level insights, so teams can pinpoint slow database queries, inefficient code paths and failing dependencies.

Traditional app monitoring solutions rely on small software components called “agents.” Agents are deployed throughout the application environment and supporting infrastructure to sample performance and telemetry at regular intervals (as frequently as once every minute). More modern solutions use agentless monitoring for a nonintrusive approach to data collection, relying on network traffic analysis to gather app performance data.

In both cases, app performance monitoring tools can make it easier for DevOps teams to meet service-level agreements (SLAs), automate issue management and improve code quality over time.

User experience monitoring—often called digital experience monitoring (DEM)—is about understanding how real people experience a site or application in terms of speed, reliability and usability. It brings together data from web browsers, mobile apps, APIs and backend systems to illustrate where users struggle and why.

User experience monitoring tools track metrics such as page response times, interaction delays and error rates, and they assess whether key user journeys (signup, checkout and search, for instance) succeed. They commonly combine front-end metrics from browsers and apps with backend observability data, so teams can trace bad user interactions back to a specific service or dependency.

User experience monitoring comprises various monitoring approaches, including real user monitoring (RUM) and synthetic monitoring. RUM captures performance and error data from actual user sessions in live production environments. Synthetic monitoring (also called synthetic transaction monitoring) uses scripted, automated tests that simulate user actions from various locations and environments

Regardless of approach, user monitoring tools enable DevOps teams to see systemic performance trends and across the application ecosystem.

Dependent system monitoring measures the availability, performance and behavior of the external systems and services applications depend on to function (payment gateways, authentication providers and partner APIs, for example).

Even though dependent systems—or dependencies—exist outside an application’s codebase, they can have a profound impact on the app’s functionality. If, for instance, the API connecting an airline’s booking app to its payment platform fails, customers can’t book their flights, no matter how well the app itself functions.

Modern IT environments are highly distributed, so a significant number of incidents stem from failures and bottlenecks in upstream or downstream services. Dependent system monitoring helps DevOps teams quickly distinguish an “our code is broken” situation from a “dependency is degraded” situation.  

As such, dependent system monitoring helps DevOps teams speed up incident response, improve app reliability and inform future fallback and retry strategies.

Security monitoring in DevOps is the continuous tracking of systems, applications and pipelines for security vulnerabilities and misconfigurations. It enables DevOps teams to transition away from one-time, point-in-time security checks to ongoing, automated surveillance of the entire stack.

Security monitoring tools combine telemetry monitoring with security controls such as vulnerability detection, access monitoring and compliance checks. That way, security controls can be integrated into CI/CD and runtime environments to help teams detect and remediate security threats throughout the software lifecycle.

While monitoring solutions and observability solutions often support and complement one another, it’s important to understand the distinction between conventional monitoring and observability practices.

At their most basic, monitoring is reactive, and observability is proactive. Monitoring and observability also differ significantly in terms of data use, scope, depth, flexibility and visualization capabilities.

Both monitoring and observability use the same types of telemetry data—metrics, logs and traces.

With monitoring, teams use telemetry data to reveal what is happening in a system. For example, they might set benchmarks or define key performance indicators (KPIs) (which indicate progress toward software performance goals); establish thresholds (to trigger notifications and actions in response to specific system events); and set up dashboards (to visualize telemetry data).

Teams can also use telemetry to identify and document dependencies, indicating how each app component works with other components, applications and IT resources.

But monitoring can’t explain why problematic events are happening.

Observability platforms take monitoring a step further by using telemetry data in a proactive way.

Observability tools use surface-level data, data from CI/CD pipelines and historical data to provide context and correlate seemingly unrelated system events, creating a complete, contextualized view of system health. Correlation features help developers accurately identify the root cause of issues, both in real time and retrospectively.

With observability, DevOps teams get deep visibility into the “what,” “where” and “why” of system events, and how the event might affect the performance of the entire environment.

Furthermore, many observability solutions can automatically discover new sources of telemetry that emerge in the system (a new API call to a software application, for example). Many of today’s observability platforms include artificial intelligence (AI) and machine learning (ML) tools that help teams glean more granular insights from the mountains of raw data modern IT environments create.

Monitoring tools use specific metrics and logs to detect system errors, resource usage patterns and specific failure modes. They help teams identify “known knowns,” meaning IT teams can find only the issues they already know to be possible. Application performance monitoring software, for example, can indicate whether an application is online, offline or experiencing latency issues. 

Observability tools help reveal “unknown unknowns” by enriching telemetry data with additional information about the network environment (topology, configurations, device roles and application dependencies, for instance), drawn from diverse data sources across the network.

This enhanced visibility and deeper insights enable IT teams to take a more exploratory approach to network and application management. Instead of watching dashboards for known failures, teams can ask observability solutions open-ended questions of live systems and iteratively “poke and probe” behavior to identify new failure modes or edge cases.

Engineers might, for example, ask an observability tool to “show traces where latency exceeds five seconds for app users in Sydney, Australia, whose traffic went through service X after deployment Y” to discover cohort-specific performance issues that aggregate averages can obscure.    

Monitoring is limited by the predefined datasets IT teams provide. It can identify only the issues that IT teams program monitoring software to recognize, so monitoring tools are often insufficient for managing dynamic environments.

Relying solely on monitoring tools means relying on siloed monitoring data, which requires teams to expend extra resources on data correlation and manual root cause analysis. Manual processes slow down issue resolution and increase the likelihood of human error, increasing the frequency of service disruptions and outages in kind.

Observability tools can map data interactions from dynamic, diverse data sources across cloud environments (such as hybrid and multicloud environments), on-premises infrastructure and third-party applications. They’re inherently adaptable, making them well suited for the problem-solving demands of modern IT infrastructures.

And with their automation and AIOps capabilities, observability platforms can scale alongside ecosystems, so teams can effectively manage their infrastructures as they expand.

Monitoring tools often visualize system data in dashboards that enable IT personnel to view key metrics in a centralized location. However, they can’t illustrate the origins of system errors. Monitoring tools instead leave predictive tasks and root cause analysis to human operators.

Observability tools create traversable maps that enable DevOps teams to see the entire codebase and app ecosystem with full context and correlated insights. This feature enables teams to easily track system errors back to their root causes and streamline troubleshooting processes.

Implementing comprehensive DevOps monitoring and observability practices offers enterprises a range of benefits, including: