Watching over an information system today means observing a living organism: hybrid, distributed, in constant flux. Every container deployed, every API exposed, every cloud added widens the perimeter, all while availability requirements leave no room for slack.
In this environment, traditional monitoring tools continue to do their job on the lower layers of the IT stack, but they are no longer enough to address the challenges posed by modern application layers. A new paradigm has taken hold: observability.
Before going any further, let’s clearly lay out the difference between the two. Vendors often use the terms interchangeably, which creates a great deal of confusion among IT teams.
Monitoring: watching what you already know
Monitoring means collecting predefined indicators to verify that a system’s components are behaving as expected. It answers a simple question: is it working?
Its natural home is the infrastructure layer: network, servers, storage, hypervisors, and databases, where specialized tools have excelled for years, whether operating remotely via SNMP or WMI, or relying on dedicated agents.
A notable shift is underway: the integration of logs. Long relegated to separate and often disconnected solutions, system logs and network events are now joining metrics in a unified interface. This is a significant step forward: for the first time, it lets us speak of a form of lightweight infrastructure observability.
Observability: understanding what you don’t yet know
Observability goes further. It lets you understand the internal state of a system from its external outputs. It answers the question: “why isn’t it working?” and even “what’s about to become a problem?”
Observability rests on three fundamental pillars:
| Pillar | Definition | Primary use |
|---|---|---|
| Metrics | Quantitative measurements collected at regular intervals (CPU, latency, error rate, etc.) | Detect anomalies, trigger alerts, analyze trends |
| Logs | Timestamped textual records of events occurring within a system | Add context to an incident, reconstruct the timeline, audit |
| Traces | Representations of a request’s path through a distributed system | Identify bottlenecks, visualize dependencies |
The real power of observability lies in correlating these three types of data. A latency spike (a metric) can be tied to an error in a log, which in turn is pinpointed by a trace showing which microservice is responsible.
Comparative overview
| Criterion | Monitoring | Observability |
|---|---|---|
| Key question | “Is it working?” | “Why isn’t it working?” |
| Approach | Reactive: threshold-based alerts | Proactive: exploration and correlation |
| Data | Metrics + infrastructure logs | Metrics + Logs + Traces, correlated |
| Scope | Network, system, storage, hypervisors | Applications, microservices, APIs, Cloud |
| Users | System and network administrators | DevOps, SRE, application teams |
| Tools | PRTG, Zabbix, Centreon, Nagios | VictoriaMetrics, Grafana Stack, Datadog |
Why is this distinction strategic?
Because it avoids two classic mistakes: believing that a modern monitoring tool is enough to cover application observability, or conversely, throwing out your monitoring tools in favor of an observability platform that doesn’t properly cover the bottom of the stack.
The two approaches are complementary, not competing. The question isn’t “monitoring or observability” but “how to combine the two.” That is precisely the focus of our two-headed approach, which we’ll detail in upcoming articles.
This article is drawn from our white paper “From Monitoring to Observability” (PDF, 2026) — free to download.
