Table of contents

• What is observability?
• What is monitoring?
• Pillars of observability and monitoring
• Observability vs monitoring explained
• Comparison table: Observability vs monitoring
• Tools and use cases
• Graphite for code reviews
• Conclusion

In today's software landscape, teams rely on both observability and monitoring to maintain system performance and reliability. Although the terms are often used interchangeably, they refer to distinct but complementary practices. This article covers what is observability, what is monitoring, and what's the difference between observability and monitoring. You'll learn the pillars of observability and monitoring, see practical examples and tooling, and explore when to use each approach. By understanding how they work together, you can build systems that are easier to debug, monitor, and improve over time.

What is observability?

Observability is the ability to understand a system's internal state by analyzing the data it produces (such as logs, metrics, and traces). In other words, observability measures how well you can infer what's happening inside a complex system based on its outputs. The term originates from control theory – for example, car diagnostic systems give mechanics observability into why a car won't start without taking it apart. In software, a highly observable system emits plentiful telemetry (data about its operations) that engineers can use to assess its health, find anomalies, and pinpoint root causes of issues.

Unlike traditional monitoring, observability is proactive and exploratory. It enables developers to ask new questions of their system's behavior even if those questions weren't anticipated in advance. Because all relevant data is available for analysis, teams can investigate "unknown unknowns" – issues or patterns they didn't previously know to watch for. For example, if an outage occurs in a complex microservice application, observability means engineers have detailed logs, metrics, and trace data to dig through and discover the cause of the failure (even if it's a novel failure mode). In summary, observability is about having complete visibility and context, so you can figure out why something is happening and how to fix it, not just that a problem exists.

What is monitoring?

Monitoring is the process of continuously collecting and analyzing system data to track performance and detect issues. By textbook definition, monitoring involves gathering information (often key metrics) about a program or system and using it to guide decisions. Monitoring focuses on watching specific, predefined metrics or conditions – for instance, CPU utilization, memory usage, request rates, or error counts – and raising alerts when those values exceed thresholds or deviate from normal. Typically, monitoring systems present data on dashboards and send notifications so operators know when something is wrong. For example, a monitoring dashboard might show that a web server's CPU usage is at 95% or that error rates spiked above an acceptable level, indicating a problem that needs attention.

Monitoring is generally reactive in nature. It answers "Is there an issue right now?" based on known indicators, but it may not explain why the issue is happening. Monitoring tools and alerts are usually tied to conditions engineers anticipated and configured in advance. As a result, monitoring is excellent for catching expected failure modes or known unknowns (e.g. "alert if traffic drops to zero" or "if memory is too high") – it tells teams when something is wrong – but it provides limited context about the why. Moreover, monitoring is often constrained to the data you decided to collect. If a problem arises outside those predefined parameters, a basic monitoring setup might miss it. This approach was very effective in earlier, stable environments where systems were simpler and failure scenarios well-understood. But as applications become more distributed and dynamic, solely relying on traditional monitoring can fall short (since you can't predict every failure in advance).

Pillars of observability and monitoring

In practice, both observability and monitoring rely on various types of telemetry data from systems. There are three primary categories of telemetry – often referred to as the "three pillars" of observability – which are also crucial for monitoring:

• Logs: Logs are timestamped, discrete records of events that happened within a system. They can include error messages, transaction details, or debug information emitted by applications. Logs provide rich contextual information about what the system was doing at a given time (for example, a log might show an exception stack trace or a user action event). They help engineers follow the sequence of events and understand the state of the application around an incident. Logs complement metrics by detailing the why behind a metric change (e.g. a surge of error log entries can explain a spike in an error-rate metric).

• Metrics: Metrics are numeric measurements collected over time, representing the state or activity of a system. Common metrics include CPU load, memory usage, requests per second, error rates, etc. Metrics are typically aggregated and stored as time-series data, which is perfect for monitoring trends and thresholds (e.g. checking if CPU usage stays below 80%). They provide a quick quantitative view of system health at a glance. For instance, a dashboard might show that latency increased to 500ms in the last 5 minutes – a clear sign of a performance issue. Metrics are efficient for alerting (since you can set numeric triggers), but they usually lack the detailed context of what exactly happened inside the code.

• Traces: Traces (specifically distributed traces) track the path of a single transaction or request as it propagates through a distributed system. In a microservices or cloud-native architecture, a single user request might invoke dozens of services – a distributed trace will record each service call, timing, and any errors along that path. Tracing is invaluable for observability because it shows how different services and components connect to fulfill a request. If a user action is slow, a trace can reveal which microservice or database call caused the bottleneck. Traces help pinpoint where failures or performance issues occur in complex, interdependent systems.

These three pillars – logs, metrics, and traces – form the core data that both monitoring and observability systems use to make sense of what's happening. (Some modern experts also include additional pillars like user experience data and security events as part of observability, expanding the scope of what teams observe to include frontend performance or security-related telemetry.) The key idea is that by capturing logs, metrics, and traces from your systems, you equip yourself with the necessary inputs to both monitor the system's health and investigate deeper when something seems off.

Observability vs monitoring explained

Now that we have defined both terms, let's address what's the difference between observability and monitoring:

• Monitoring focuses on:

  • Capturing and displaying data against expected targets
  • Providing situational awareness (e.g., "our error rate is 5% right now")

• Observability focuses on:

  • Analyzing all the outputs of a system to assess its health and behavior
  • Providing diagnostic insight (e.g., "requests to the payment service are failing due to an unhandled exception in the checkout function")

Key differences in data handling:

• Monitoring:

  • Works with predetermined data and questions
  • Uses a fixed set of metrics and logs chosen in advance
  • Visualizes data in dashboards based on predefined assumptions
  • Effective for known issues but may miss unexpected situations
  • Best suited for known failure modes and steady-state tracking

• Observability:

  • Aggregates all types of telemetry data (logs, traces, metrics)
  • Proactively analyzes data to find anomalies
  • Not limited to pre-selected metrics
  • Allows asking new questions on the fly
  • Best suited for unpredictable, complex environments where dynamic problem exploration is needed

Another way to look at it: monitoring is reactive, observability is proactive. Monitoring systems often use threshold alerts – they react when a metric goes out of bounds or an error count spikes. You find out about issues after they occur (e.g. an alert notifies you that disk space is 100% full). Observability, on the other hand, enables you to dive into the data proactively. You might notice a slight increase in latency and, using observability tools, drill down into traces and logs to investigate before it becomes a major incident. Observability helps in investigating unknown unknowns by letting you slice and dice data in arbitrary ways to spot patterns, while monitoring tends to be limited to known unknowns that you've set an alarm for.

It's important to note that observability and monitoring are complementary rather than in conflict. You don't "choose" one or the other – in fact, you need both. Monitoring gives you the early warning when something's off, and observability gives you the means to figure out the details and address the root cause. Modern reliability engineering uses monitoring to detect issues quickly (ideally before users notice) and observability to perform fast root cause analysis and debugging across distributed systems. In the next section, we summarize the key differences between observability and monitoring in a comparison table for clarity.

Comparison table: Observability vs monitoring

Tools and use cases

A variety of tools exist to help implement both monitoring and observability in your systems. In fact, modern "observability platforms" often encompass monitoring capabilities, and vice-versa – but certain tools are traditionally associated with one or the other:

• Metrics and monitoring tools: To track metrics and status, teams often use tools like Prometheus (an open-source metrics database and alerting system) or cloud services like Amazon CloudWatch and Azure Monitor. These tools collect numeric indicators (CPU, memory, request rates, etc.) and allow you to set up dashboards and alerts. Classic server monitoring solutions such as Nagios or Zabbix focus on host-level checks (CPU, disk, network health) and send notifications on failures. For visualizing metrics data, Grafana is a popular open-source dashboard that can chart data from various sources (Prometheus, InfluxDB, etc.) to give at-a-glance views of system health.

• Observability and tracing tools: For deeper insight, organizations deploy log management and tracing systems. Log aggregation tools (like the ELK stack – Elasticsearch, Logstash, Kibana – or Splunk) collect logs from across your apps and let you search and analyze them in one place. Distributed tracing tools such as Jaeger or Zipkin help trace requests through microservices, which is key for understanding complex transactions. There are also APM (Application Performance Monitoring) suites – despite the name, APM products (e.g. Datadog, New Relic, Dynatrace) actually provide full observability by integrating metrics, logs, and traces in one platform, often with user experience monitoring as well. Additionally, open standards like OpenTelemetry have emerged to standardize how applications emit telemetry data, making it easier to collect granular traces, metrics, and logs for analysis.

Monitoring and observability often complement each other. For example, monitoring might alert you if CPU usage stays above 90% for five minutes—signaling a potential issue. Observability helps you dig deeper: if response times spike, tracing a request could reveal it’s stuck on a slow database query, with logs confirming the root cause. While monitoring flags symptoms like downtime or latency, observability helps explain why they occurred—critical during incident response or postmortems.

In summary, use monitoring to catch the obvious issues quickly and use observability to delve deeper into system behavior. Together, these practices ensure that not only can you detect problems early, but you can also unravel the trickiest failure scenarios in a complex system.

Graphite for code reviews

Maintaining high software quality from the development phase is crucial for building robust systems. Graphite, an AI-powered code review platform, offers tools that assist with code reviews, helping teams improve code quality before issues reach production. Graphite integrates with GitHub pull requests and uses AI to provide developers with immediate, actionable feedback on code changes. Its AI reviewer, Diamond, can detect bugs, security vulnerabilities, performance issues, and adherence to best practices during the review process.

By identifying problems early—before code is merged and deployed—Graphite helps maintain a clean, efficient, and maintainable codebase. Moreover, thorough code reviews, aided by intelligent suggestions, encourage developers to include proper error handling, logging, and instrumentation in their code, enhancing observability once the software is running. Graphite's automatic comments can catch subtle errors before they become bugs, and its AI can suggest fixes for identified issues, streamlining the review cycle and ensuring consistency and standards.

In essence, Graphite acts as a smart assistant in the code review process, helping engineers ship higher-quality code faster. This proactive approach means fewer problems escape into production, and those that do are easier to monitor and troubleshoot due to code written with observability in mind.

Conclusion

Monitoring and observability work hand in hand in DevOps. Monitoring tracks key metrics and alerts you when something’s wrong; observability helps you understand why it happened by analyzing logs, traces, and other data. Both are essential—monitoring shows symptoms, observability reveals causes. Combining alerts, dashboards, and rich telemetry enables teams to catch and fix issues quickly, even in complex systems. Strong observability, supported by good instrumentation and code reviews (with help from tools like Graphite), leads to faster debugging and more resilient software. Together, they create a feedback loop that keeps systems reliable and teams informed.