Monitoring and observability are related but distinct. Monitoring tells you whether a system is working by tracking predefined metrics and alerting on known failure conditions. Observability is a property of a system that lets you ask new questions about its internal state from the outside, so you can diagnose problems you never anticipated. Modern teams running distributed, cloud-native software need both: monitoring to catch expected problems fast, and observability to investigate the ones nobody predicted.
What is the difference between monitoring and observability?
The simplest way to frame it: monitoring answers "is something wrong?" while observability answers "why is it wrong?" Monitoring is built around known failure modes. You decide in advance which signals matter (CPU, error rate, request latency, queue depth), set thresholds, and get alerted when a threshold is crossed. It is excellent for detecting problems you have seen before.
Observability, by contrast, does not assume you already know what will break. In modern architectures, a single user request may pass through a dozen microservices, third-party APIs, message queues, and databases. The number of ways such a system can fail is effectively unbounded. Observability gives you rich, high-cardinality telemetry so you can explore behavior after the fact and answer questions you did not think to ask when you built the dashboards.
Put another way: monitoring is a subset of what a well-instrumented, observable system enables. You can monitor a system without it being truly observable, but you cannot achieve observability without collecting the underlying data that also powers monitoring.
The three pillars of observability
Observability is commonly described through three types of telemetry data, often called the three pillars. Each answers a different kind of question:
- Metrics are numeric measurements aggregated over time, such as requests per second, error percentage, or p99 latency. They are cheap to store and ideal for dashboards and alerting, but they lose per-request detail.
- Logs are timestamped, often structured records of discrete events. They provide detailed context about what happened at a specific moment, which is invaluable during a deep investigation.
- Traces follow a single request as it travels across services, recording timing and metadata at each hop. Distributed tracing is what makes it possible to see that a slow checkout was caused by one downstream inventory call, not the payment gateway everyone suspected.
The real value emerges when these signals are correlated. A spike in a latency metric should let you jump to the traces behind it, and from a slow trace span to the exact logs that explain it. Isolated pillars produce three disconnected tools; correlated pillars produce answers. The open standard driving this correlation is OpenTelemetry, a vendor-neutral framework for generating and exporting telemetry that most major platforms now support.
When is monitoring enough, and when do you need observability?
Not every system justifies a full observability investment. Traditional monitoring is often sufficient when:
- You run a monolith or a small number of predictable services.
- Failure modes are well understood and repeat.
- Infrastructure is relatively static and requests do not fan out across many components.
You should invest in observability when:
- Your architecture is distributed, microservice-based, serverless, or event-driven.
- Incidents frequently produce the phrase "we have no idea why this is happening."
- Mean time to resolution is dominated by finding the problem rather than fixing it.
- You deploy frequently and need to understand the behavior of code paths that have never run in production before.
A practical signal: if your engineers routinely resolve incidents by adding new logging and waiting for the problem to recur, your system is not observable enough. Observability aims to let you diagnose novel issues from data you are already collecting.
How do observability and monitoring work together?
These are not competing choices; they are layers of the same reliability practice. A mature setup uses monitoring as the fast, coarse alerting layer and observability as the deep, exploratory layer beneath it.
- Detect with monitoring. Alerts fire on symptoms your users care about, ideally framed around service level objectives (SLOs) rather than raw resource usage.
- Investigate with observability. Once alerted, engineers pivot from the triggering metric into traces and logs to locate the root cause.
- Improve continuously. Findings feed back into better alerts, refined SLOs, and instrumentation of newly discovered blind spots.
Google's Site Reliability Engineering practice popularized this outcome-focused approach. Their guidance on monitoring distributed systems emphasizes alerting on user-visible symptoms and the "four golden signals" (latency, traffic, errors, and saturation) rather than drowning teams in low-level noise.
What does implementing observability actually require?
Adopting observability is as much a practice as a purchase. The essential ingredients are:
- Instrumentation. Your services must emit metrics, structured logs, and traces. Standardizing on OpenTelemetry keeps you portable across backends and avoids vendor lock-in.
- A backend to store and query telemetry. This may be a managed platform or a self-hosted stack such as Prometheus, Grafana, Loki, and Tempo.
- Correlation and context propagation so a trace ID connects metrics, logs, and spans across service boundaries.
- SLOs and error budgets to decide what "healthy" means and what is worth paging a human for.
- Cost management. High-cardinality data is powerful but expensive; sampling and retention policies matter.
Common mistakes teams make
A few recurring pitfalls undermine even well-funded efforts:
- Alert fatigue. Paging on causes instead of symptoms buries the signal that matters under noise engineers learn to ignore.
- Dashboards without correlation. Pretty charts that cannot link to the underlying traces and logs slow investigations down.
- Instrumenting only the happy path. The failures you did not anticipate are exactly the ones observability is meant to catch.
- Treating tooling as the finish line. Observability is a habit of asking better questions, not a product you install once.
Frequently asked questions
Is observability just a new name for monitoring?
No. Monitoring tracks predefined signals for known failure conditions, while observability lets you explore a system's internal state to diagnose problems you never predicted. Monitoring is one capability that a genuinely observable system supports, but observability requires richer, more granular telemetry and the ability to ask new questions of it after an incident.
Do small teams or startups need full observability?
It depends on architecture, not headcount. A small team running a simple monolith can often succeed with solid monitoring and structured logging. But a small team running many microservices or serverless functions will benefit from distributed tracing early, because the cost of debugging blind across service boundaries grows quickly.
What is the difference between the three pillars and observability?
The three pillars (metrics, logs, and traces) are the raw data types. Observability is the outcome you achieve when that data is well-instrumented, correlated, and queryable enough to answer unanticipated questions. Collecting all three pillars in disconnected silos does not, by itself, make a system observable.
Which should we implement first?
Start with symptom-based monitoring and structured logging so you can detect and describe incidents. Then add distributed tracing and correlation as your architecture grows more distributed. Standardizing on OpenTelemetry from the start keeps that progression smooth and avoids rework later.
How Direlli can help
Direlli designs and operates monitoring and observability stacks that fit your architecture, not a one-size template. Our engineers instrument services with OpenTelemetry, wire up correlated metrics, logs, and traces, and define SLO-based alerting that reduces noise instead of adding to it. Explore our monitoring services and broader DevOps services, or get in touch to talk through your setup. Rated 5.0 on Clutch, Direlli serves clients across the US, Europe, and MENA.
