As IT and cloud environments expand in scale and complexity, administrators and developers must gain deeper visibility into their performance, stability, and underlying behaviors.
Two key approaches have emerged in the software development lifecycle to address this need: monitoring and observability. Though often used interchangeably, these concepts have distinct purposes and provide different insights into system health. Monitoring tracks known metrics and predefined conditions, whereas observability enables deeper investigation into unexpected behaviors by exposing internal state through logs, traces, and metrics.
In this post, we will explore the differences between monitoring and observability, their relationship, challenges in adoption, and future trends.
We will cover:
Monitoring is the process of collecting, analyzing, and using information to track a system’s performance over time. It involves collecting real-time data, such as resource utilization, error rates, and response times, to detect anomalies or potential failures.
Traditional approach to system health
Monitoring has long been the primary way of maintaining system health in IT environments. The traditional monitoring approach starts with a continuous or regular collection of predefined metrics and logs followed by analysis of the data collected to identify patterns and anomalies, often with the help of specialized monitoring tools.
In this way, IT administrators and developers can compare the current system behavior against baselines. Finally, if abnormalities are detected, users reactively respond by troubleshooting and addressing any detected issues. This method has been effective for relatively simple, stable systems where cause-and-effect relationships are clear and predictable.
Key components of the monitoring process
Let’s look closer at the key components of a complete monitoring solution and system.
1. Metrics
Metrics are quantitative measurements that provide insights into system performance and behavior. They are the backbone of any monitoring strategy, allowing teams to track trends, detect anomalies, and optimize performance.
Common metrics include resource utilization (CPU, memory, disk I/O), application performance (latency, error rates, throughput), and business KPIs (user engagement, conversion rates).
An essential aspect of a monitoring system is the metric collection process, which must be performed efficiently and continuously to gather data without gaps. Metrics form the foundation of monitoring, providing the raw data needed to assess system health.
2. Alerts
Alerts are automated triggers that notify teams of potential system issues based on predefined thresholds or anomaly detection. They serve as an early warning mechanism, allowing engineers to address problems before they escalate. A well-configured alert system differentiates between critical failures and minor deviations to avoid unnecessary noise.
For instance, an alert might be triggered when API response times exceed a defined threshold, signaling potential server overload. The challenge lies in fine-tuning alerts to balance responsiveness without overwhelming teams with false positives, a phenomenon known as alert fatigue.
3. Dashboards
Dashboards transform raw metrics into intuitive visual representations, offering a centralized view of system performance. They provide historical context, helping teams spot patterns, diagnose root causes, and predict future trends. A well-designed dashboard is customizable, allowing users to focus on specific areas such as infrastructure health, user activity, or financial metrics.
Read more: 20+ Most Popular DevOps Monitoring Tools
Limitations of monitoring in complex systems
Although monitoring is crucial for maintaining system health, it faces several limitations when dealing with complex systems:
- Unexpected behaviors: Complex systems exhibit unpredictable behaviors that predefined metrics may fail to capture, leading to missed emerging issues.
- Static threshold challenges: Fixed alert thresholds become ineffective as systems grow more dynamic, causing false positives or negatives.
- Alert fatigue: Excessive non-actionable alerts overwhelm users, making the alerting system difficult to operate.
- Limited system-wide visibility: Traditional monitoring focuses on individual components, making it hard to detect broader system-wide issues.
- Lack of context: Alerts often provide insufficient context, notifying users only after problems arise, which may be too late for critical systems.
- Data overload: Growing system complexity generates vast amounts of data, making it challenging for traditional monitoring tools to process effectively.
Observability offers a more holistic approach to understanding a system’s internal state than monitoring by examining its external outputs. It provides a comprehensive way to understand and gain insights into the behavior and performance of complex distributed systems.
It starts with collecting and analyzing telemetry data — metrics, traces, and logs — from all components. Unlike basic monitoring, which alerts on predefined issues, observability allows teams to explore unexpected failures and performance bottlenecks based on real-time or near-real-time data analysis enabled by powerful machine learning algorithms and tools.
The core concept of observability is to go beyond simple monitoring by allowing teams to ask arbitrary questions about their system’s behavior, especially for issues they didn’t anticipate.
The three pillars of observability: Logs, metrics, and traces
Observability relies on three fundamental types of telemetry data, often called the “three pillars of observability.” These pillars work together to provide a holistic view of system behavior and performance.
Pillar 1: Logs – Logs are detailed, time-stamped records of specific system events. Developers and administrators configure applications and other systems to output logs, which provide context and runtime information about specific occurrences, errors, or state changes.
Pillar 2: Metrics – Metrics are quantitative measurements of system performance and behavior over time. They are typically numeric values that can be aggregated and analyzed to understand trends and set alert thresholds.
Pillar 3: Traces – A newer telemetry data type that has emerged over the last years due to the complexity of distributed systems and large microservices architectures is traces. Traces are end-to-end tracking of requests as they flow through multiple services in a distributed system. They provide visibility into a request’s path, the time spent in each service, and the relationships between different components.
Benefits of observability
Observability offers several significant advantages compared to more traditional and reactive monitoring approaches:
- A deeper understanding of system behavior: By correlating data from logs, metrics, and traces, observability provides a comprehensive view of how different components interact within a system and how its different parts are affected.
- Faster root cause analysis and incident response: Observability tools enable teams to quickly identify the source of issues, reducing mean time to resolution (MTTR).
- Proactive issue detection: With real-time data analysis and advanced anomaly detection, observability allows teams to identify potential problems before they impact users. Users get notified of potential issues that might come in the future or emerging trends before they become problems, giving them time to address them before it’s too late.
- Improved system reliability and performance: Organizations that have successfully implemented an effective observability strategy tend to optimize their systems’ overall reliability and performance.
- Enhanced user experience: Observability enables teams to understand user behavior and optimize the user experience, leading to increased conversion rates and user retention.
- Maintain complex and distributed architectures: Observability is particularly valuable in microservices and cloud-native environments, where traditional monitoring approaches may fail. The complexities, distributed nature, and vast volume of data of such systems make observability approaches better suited for operating and maintaining them.
The table below summarizes the key differences between monitoring and observability:
| Aspect | Monitoring | Observability |
| Focus | Predefined metrics and common issues | System-wide behavior and detection of unknown issues |
| Scope | Individual components | Entire environments, their components, and their interactions |
| Alerting | Reactive, threshold-based alerts | Proactive issue detection and alerting |
| Complexity | More suited to simpler environments | More suited to complex, dynamic, distributed, and large environments |
| Insights and understanding | What is wrong | What is wrong, why, and suggestions for fixes |
Let’s discuss them in more detail:
Focus: Common issues vs. unknown issues
Monitoring primarily focuses on tracking predefined metrics and common issues within a system. It relies on established baselines and thresholds to detect anomalies. Observability, on the other hand, goes deeper—capturing logs, traces, and metrics to uncover patterns beyond predefined alerts. This comprehensive view allows teams to discover and address unexpected problems that may arise in complex, distributed systems.
Scope: Individual components vs. entire systems
Monitoring focuses on metrics and data for specific components, while observability approaches allow users to combine various telemetry data from entire systems and their interactions, offering a more holistic view of an environment’s health.
Alerting: Reactive vs. proactive
Monitoring is typically reactive, notifying teams only after a system anomaly or failure has occurred. This often means responding to issues once they’ve already disrupted performance or availability, leaving little room for preemptive action.
Observability, in contrast, shifts the focus to early detection and prevention.
By analyzing data trends and patterns, engineers can anticipate potential issues before they become significant problems, enabling early intervention during regular maintenance rather than emergency downtimes.
Complexity: Simpler vs. dynamic systems
Observability excels in managing complex, dynamic, and large-scale distributed systems by offering deep, real-time insights into system behavior.
It goes beyond traditional monitoring by analyzing high-cardinality data and correlating diverse signals, uncovering hidden dependencies and emergent issues in cloud-native and microservices architectures. This makes it indispensable for environments where failures are unpredictable, and troubleshooting requires a granular, contextual understanding.
Monitoring is more suited for stable, well-defined systems with predictable performance patterns, where predefined metrics and thresholds are sufficient for detecting issues.
Insights: Issue detection vs. root cause and remediation
Monitoring provides insights into what is wrong, while observability offers a deeper understanding of the underlying issues. Observability shines at leveraging comprehensive data collection and analysis to not only identify what is wrong but also why it occurred.
Observability platforms can pinpoint the root cause of issues, even in complex, distributed systems by correlating logs, metrics, and distributed traces. Furthermore, observability tools often provide actionable insights and suggestions for fixes and remediation, enabling teams to address problems before they escalate.
Observability and monitoring are complementary approaches with a few common aspects and differences. Monitoring serves as a foundation for observability by providing the necessary data collection. Observability builds upon this data to offer deeper insights and a more comprehensive system understanding.
While monitoring highlights known problems using structured data, observability provides the flexibility to analyze logs, traces, and metrics dynamically, making it possible to diagnose unpredictable failures. Monitoring tells you when something is wrong, but observability helps you understand why—giving deeper insights into dependencies, interactions, and system behavior in real time.
When deciding between observability and monitoring, it’s essential to consider the complexity and nature of your system. Observability is particularly well-suited for complex, dynamic, and distributed environments where the interactions between components are often unpredictable.
Consider using observability when:
- Your system is complex, with many interconnected parts. Typically distributed systems or cloud environments.
- You need to understand the root cause of issues and their impact on the entire system.
- You want a proactive approach to problem-solving and performance optimization.
On the other hand, monitoring is more appropriate for simpler environments with predictable behaviors and known failure modes.
Opt for monitoring when:
- You have a clear understanding of potential problems and can predict how your system might fail.
- You need immediate alerts and real-time insights into system health.
- You’re working with more straightforward and predictable systems and can’t invest in an elaborate observability strategy.
Implementing an effective observability strategy is crucial for gaining deep insights into complex systems and ensuring optimal performance. Here’s a high-level guide to developing and executing a robust observability strategy:
Step 1. Define clear objectives and goals
Establishing clear objectives and goals is essential before implementing an observability strategy. Work with key stakeholders to Identify KPIs that align with business objectives and determine the level of visibility required across different systems and services.
Set specific targets for incident response times and system reliability and outline expectations for proactive issue detection and resolution. Clearly defined goals will guide your implementation and help measure the success of your observability efforts.
Step 2. Choose the right tools and platforms
Selecting appropriate tools and platforms is crucial for an effective observability strategy.
First, evaluate open-source and commercial observability solutions and consider their functionalities and integration capabilities with your existing tech stack. Look for solutions supporting all three observability pillars: logs, metrics, and traces.
When evaluating different tools and platforms, prioritize user-friendly interfaces, powerful querying capabilities, and auto-remediation solutions. Popular observability platforms include:
- Datadog – Comprehensive monitoring and security platform.
- New Relic – Performance monitoring with AI-driven insights.
- Honeycomb – Focused on distributed tracing and high-cardinality data.
- Prometheus (open-source) – Metrics-based monitoring and alerting.
- Grafana (open-source) – Visualization and analytics for observability data.
Step 3. Implement centralized data collection and instrumentation
Effective data collection and instrumentation form the foundation of observability. Before selecting a tool, start by implementing structured logging across all services and applications. If you operate in distributed and complex architectures, use distributed tracing to track requests across microservices.
Collect relevant metrics from all system components, such as infrastructure, applications, and business processes. To combine telemetry data, leverage OpenTelemetry for standardized instrumentation across your stack and ensure consistent metadata and tagging across all telemetry data.
A centralized approach enables the correlation of data from various sources, providing a holistic view of system behavior.
Step 4. Continuously iterate
Observability is an ongoing process that requires continuous refinement. Review and update your observability strategy regularly based on changing needs. To keep your strategy relevant, collect feedback from teams using the observability tools and incorporate improvements.
These technologies move fast, so spend time keeping informed about new observability techniques and trends. When issues occur, conduct post-incident reviews to identify areas for improvement in your observability practices. Invest in training and skill development for team members to get the most out of your selected tools.
Although observability offers significant improvements and benefits, organizations face certain challenges and blockers adopting it.
First, modern architectures might include multicloud and hybrid environments, often with legacy systems that must be observed holistically. This distribution complicates monitoring and data processing across platforms. In such cases, ensuring compatibility among tools with varying data formats and protocols can be challenging. Furthermore, as systems grow more complex, observability software can struggle to manage and interpret the intricate relationships between components.
Second, we have observed that cost management is a primary concern for organizations implementing observability. SaaS-based solutions offer excellent tools and functionalities, but they often come at high price points. As data volumes grow, SaaS-based observability solutions billed per usage can become increasingly expensive.
Lastly, all these tools come with their own set of functionalities and require specialized skills to use and configure them effectively and get the most out of them. Organizations must invest in training and skill development to overcome this challenge and maximize the value of their observability tools.
Artificial intelligence and machine learning are already pivotal for shaping observability and will be even more important with the rise of generative AI. We expect AI-powered anomaly detection to become more sophisticated, enabling faster identification of potential issues before they impact users.
Another key development is the extensive usage of AI agents that will leverage observability data and benchmarks to not only detect anomalies but also predict potential issues and take corrective actions automatically.
Observability is increasingly associated with successful DevOps and Site Reliability Engineering (SRE) practices. We expect observability to become the golden standard for better collaboration between development, operations, and security teams, fostering a more unified approach to system reliability and performance.
Observability will be integrated earlier in the development lifecycle, following the trend for a “shift left” approach to reliability and performance.
The scope of observability is expanding beyond traditional IT metrics to encompass business-level insights. Organizations will increasingly correlate technical performance data with business outcomes, providing a clearer picture of how IT systems impact the business. Observability platforms will offer more business-centric dashboards and KPIs, enabling non-technical stakeholders to understand system performance in relation to business goals.
Spacelift is not exactly a monitoring tool, but it allows you to connect to and orchestrate all of your infrastructure tooling, including infrastructure as code, version control systems, observability tools, control and governance solutions, and cloud providers.
Spacelift enables powerful CI/CD workflows for OpenTofu, Terraform, Pulumi, Kubernetes, and more. It also supports observability integrations with Prometheus and Datadog, letting you monitor the activity in your Spacelift stacks precisely.
Key features
- Multi-IaC workflow
- Stack dependencies: You can create dependencies between stacks and pass outputs from one to another to build an environment promotion pipeline more easily.
- Unlimited policies and integrations: Spacelift allows you to implement any type of guardrails and integrate with any tool you want. You can control how many approvals you need for a run, which resources can be created, which parameters those resources can have, what happens when a pull request is open, and where to send your notifications data.
- High flexibility: You can customize what happens before and after runner phases, bring your own image, and even modify the default workflow commands.
- Self-service infrastructure via Blueprints: You can define infrastructure templates that are easily deployed. These templates can have policies/integrations/contexts/drift detection embedded inside them for reliable deployment.
- Drift detection & remediation: Ensure the reliability of your infrastructure by detecting and remediating drift.
If you want to learn more about Spacelift, create a free account or book a demo with one of our engineers.
In this blog, we analyzed the two main approaches to understanding IT systems’ overall health and state: observability and monitoring.
Monitoring tells you what is happening, and observability helps you understand why. Together, they provide the visibility needed to detect, diagnose, and resolve issues in complex, dynamic environments.
We explored each approach’s characteristics and discussed their differences and relationships. Finally, we reviewed the implementation details and future trends for observability.
Take DevOps monitoring to the next level
Spacelift is a infrastructure orchestration platform that allows you to connect to and orchestrate all of your infrastructure tooling, including monitoring, infrastructure as code, version control systems, observability tools, control and governance solutions, and cloud providers.
