Observability vs Monitoring: understanding their differences and effectiveness in today’s technology landscape

In today’s world, where IT infrastructures are increasingly complex, distinguishing observability from traditional monitoring has never been more critical. For businesses aiming to maintain peak performance and resilience in the face of incidents, embracing this shift is essential. This article takes a look at both approaches and explains why observability represents the next step forward in monitoring practices. 

The Evolution of Monitoring Practices 

The technology landscape has seen major changes in recent years. Monolithic architectures have given way to distributed microservices, hybrid cloud environments are now the norm, and continuous deployment has replaced traditional release cycles. Because of this transformation, our monitoring strategies must evolve too. 

System outages and slowdowns are costly — according to Gartner, a single hour of downtime can cost a business up to €300,000. In this context, the ability to quickly understand and resolve issues is more than a technical challenge; it’s a business imperative. 

What is Traditional Monitoring? 

Traditional monitoring is built on a simple principle: track predefined metrics and trigger alerts when thresholds are breached. It typically focuses on indicators such as: 

• Service availability (uptime) 
• Resource usage (CPU, memory, disk) 
• Response times 
• Network traffic 

This approach operates on a “what you measure is what you see” basis. This means that if your system monitors CPU and memory but not message queues, you may completely miss queue-related issues. 

Tools like Centreon excel in traditional monitoring by offering: 

• Preconfigured dashboards 
• Customisable alert thresholds 
• A high-level view of system health 

However, this model has limitations when applied to modern architectures, where root causes are often unpredictable and result from multiple factors.  

Observability: A More Holistic Approach 

Borrowed from control theory in engineering, observability refers to a system’s ability to have its internal state inferred from its external outputs. 

In IT, observability goes beyond monitoring — it enables you to understand: 

• Why an issue occurred 
• How it impacts the user experience 
• Where the root cause lies 

Observability assumes that in complex systems, you can’t anticipate every possible failure. Instead of monitoring only predefined metrics, it entails collecting enough raw data to answer virtually any question about system behaviour — even before a problem arises. (Read more about why observability is vital to technological evolution.) 

The Three Pillars of Observability 

Observability is typically built on three complementary data types: 

1. Metrics

Metrics are numeric values mesured over a given period. They are ideal for: 

• Monitoring trends 
• Building dashboards 
• Triggering threshold-based alerts 

Example: HTTP 500 error rates have increased by 15% in the last 30 minutes. 

2. Logs

Logs are timestamped textual records of events that provide context. These are used to: 

• Reconstruct event timelines 
• Pinpoint what happened at a specific moment 
• Provide details about specific errors 

Example: A log entry at 14:32:45 shows a “Connection timeout” error during a database call. 

3. Traces

Traces follow the full journey of a request across the various components of a distributed system. They are essential for: 

• Visualising execution flow from end to end 
• Spotting bottlenecks 
• Understanding dependencies between services 

Example: A user request takes 3 seconds, 2.7 of which are spent waiting for the payment service, which in turn is waiting for a response from a third-party provider. 

Platforms like Dynatrace and Splunk have embraced this holistic approach, offering integrated observability solutions that bring together these three pillars in a unified view. 

Monitoring vs Observability: main differences 

	Traditional monitoring	Observability
Aim	Detect when something goes wrong	Understand why something goes wrong
Approach	Reactive (respond to alerts)	Proactive (explore systems)
Focus	Individual components	Journeys and user experience
Granularity	Aggregated metrics	High fidelity data
Configuration	Prior knowledge of what needs  to be monitored is required	Comprehensive measurements  enable retrospective exploration
Complexity	Suited to simple architectures	Vital for complex distributed systems

How Observability is Transforming IT Operations 

Adopting an observability-first approach delivers a range of tangible benefits: 

1. Reduced Mean Time to Resolution (MTTR)

Observability significantly shortens the time it takes to identify and solve problems. According to research from the DevOps Research and Assessment (DORA) group, organisations with mature observability practices report a 50–90% reduction in MTTR. 

2. Enhanced Cross-Team Collaboration

Observability provides a common language and shared data that bring development, operations, and business teams together. Gone are the days of siloed teams using separate tools! 

3. Data-Driven Continuous Improvement

With real usage data at their fingertips, teams can spot opportunities for optimisation. For example, discovering that a little-used feature consumes excessive resources can guide architecture decisions. 

4. Proactive Support for Business Goals

Observability doesn’t stop at the technical layer — it connects directly to business KPIs. For example, linking technical performance to outcomes such as conversion rates on an e-commerce site helps organisations to make better decisions faster. 

Implementing Observability in Your Organisation 

Building observability isn’t just about deploying tools — it’s a cultural shift, and even an ecological one (read about the relationship between observability and green tech practices). Here are the key steps to a successful rollout: 

1. Assess Your Current Maturity

Begin by auditing your current monitoring approach: 

• What types of data are you already collecting? 
• How do you respond to incidents today? 
• Where are your blind spots? 

2. Define an Instrumentation Strategy

Instrumentation is the process of equipping your code and infrastructure to emit meaningful data: 

• Standardise your log formats 
• Implement distributed tracing 
• Capture relevant business metrics  

3. Choose the Right Tools

There’s no one-size-fits-all solution — your choice will depend on your infrastructure, team skills, and budget: 

• All-in-one platforms like Dynatrace, Splunk or Ekara, which cover a full range of observability needs 
• Open-source stacks such as Prometheus, Grafana, and Jaeger 
• Hybrid models combining proprietary and open-source components 
• Specialist solutions like Ekara, which excels at monitoring user experience and performance testing 

4. Upskill Your Teams

Observability demands new skills, including: 

• Data analysis 
• Systemic thinking 
• Advanced debugging 

Investing in training is key to getting the greatest value from your investment in observability. 

5. Track Progress

Define indicators to assess the effectiveness of your observability efforts: 

• Reduction in MTTR 
• Number of undetected incidents 
• Satisfaction of operating teams 

Building a Culture of Observability 

Observability is more than just a technical upgrade of monitoring — it represents a paradigm shift in how we understand and manage complex systems. In a world where digital transformation is accelerating, moving beyond simple monitoring towards true observability is fast becoming a competitive advantage. 

Forward-thinking organisations aren’t simply reacting to incidents — they’re building deep system understanding, anticipating issues before they escalate, and innovating with confidence. 

The real question is no longer if you should embrace observability, but how to implement it effectively to support your business objectives in an ever-evolving technology landscape.