Last updated: June 23, 2025
Observability vs Monitoring: Understanding the Differences
Your P99 latency dashboard is green, CPU utilization is normal, and error rates appear steady.
Yet, a flood of support tickets arrives with complaints about intermittent 5xx errors.
Your monitoring tools indicate that everything is fine, which leaves your team without a clear path forward.
This common scenario highlights a gap in traditional monitoring that’s pushing teams to find a more comprehensive way to understand system behavior.
For years, a common industry definition has been: “Monitoring tells you what is wrong, and observability tells you why”.
While a useful starting point, this phrase doesn’t capture the full technical and cultural shift that observability represents.
It’s not just about finding the “why”; it’s about having the capability to ask any question imaginable about your systems, especially the ones you never thought of in advance, and actually get clear answers.
This article will dissect the two practices, moving beyond the platitudes to give you a more precise, technical understanding. We’ll explore the real differentiator that powers modern observability and provide a framework for thinking about how to build and maintain truly resilient software.
Monitoring is answering the questions you already knew to ask
Monitoring is the essential, foundational practice of tracking the health and performance of a system by collecting and analyzing pre-defined sets of data.
At its core, monitoring is about answering questions you already knew you needed to ask.
Consider how monitoring gets set up:
- “What if CPU usage goes above 90% for more than 5 minutes?” -> You define a metric and set an alert.
- “Is our API’s average response time exceeding 500ms?” -> You track this on a dashboard.
- “Do our logs contain the string ‘FATAL’?” -> You set up a log query alert.
This is the realm of predictable failure modes. While you don’t know when an issue will happen, you know which signals are important to watch.
Monitoring instruments your system to keep an eye on these known risk areas and notify you when they breach a defined threshold. It’s your first line of defense, and its absolutely essential in any production environment.
But as systems scale, the number of things that can break in unexpected ways multiplies rapidly.
The limits of monitoring & the rise of complexity
The approach of tracking predictable failures becomes insufficient in the face of modern architectural complexity.
With the rise of microservices, serverless functions, container orchestration, and a web of third-party SaaS dependencies, the surface area for failure has grown exponentially.
A single user request might now traverse dozens of loosely coupled services, each introducing its own risks. You’re no longer troubleshooting a CPU spike on a single monolith.
Instead, you’re chasing down a subtle latency spike in a downstream gRPC call, affecting only users behind a specific feature flag, triggered when a third-party API slows down.
You can’t predict that. You can’t preemptively build a dashboard for it.
This is the world of emergent failure modes, where traditional monitoring tools go quiet and a new approach is required.
Observability is the power to ask new questions
Observability is the ability to understand and debug your system’s internal state by examining outputs from the outside.
It's a property you build into your systems so that when things go wrong you can ask new, unanticipated questions without needing to deploy new code just to instrument for them.
Consider this analogy:
- Monitoring is like a thermometer. It answers one specific, predefined question: “Do you have a fever?”.
- Observability is like seeing a skilled doctor for a full diagnostic workup. The doctor sees the fever (the alert) then asks a range of follow-up questions like: “What else are you feeling? Where have you been? What did you eat?” They might run blood tests, review your medical history, and order scans—all to uncover a root cause they didn’t know to look for.
That power comes not from guesswork, but from the specific characteristics of the data an observable system provides.
The technical foundation is rich and contextual telemetry
The key technical difference between monitoring and observability lies in the nature of the data each practice relies on. This difference can be understood through two concepts:
- Dimensionality: The number of attributes or tags attached to an piece of telemetry.
- Cardinality: The number of unique values each of those attributes can have.
Monitoring typically relies on simple data with few dimensions and a low number of unique values. For example, a metric like http_requests_total might only have dimensions for status_code and http_method. The total number of combinations is small and easy to manage on a dashboard.
12345# HELP http_requests_total Total number of HTTP requests made.# TYPE http_requests_total counterhttp_requests_total{method="GET", code="200"} 124http_requests_total{method="GET", code="404"} 29http_requests_total{method="POST", code="201"} 35
Observability, on the other hand, is fueled by telemetry that is rich in detail and context. Imagine that same request event, but now it’s enriched with a wealth of contextual attributes, from high-level details from the runtime environment, down to highly unique identifiers like a user_id or trace_id.
By collecting events with this level of detail, you can cross-correlate information to pinpoint the exact ‘blast radius’ of an issue and isolate it to a specific property or combination of properties
This capability is what allows an engineer to move from a vague symptom like “the site is slow” to a precise diagnosis like “it’s slow for customers on plan B using version v2.3.1 with feature X enabled”.
Telemetry without shared context is just data
For many teams, the journey into observability begins by focusing on collecting the “three pillars”: metrics, logs, and traces. The assumption is that if you gather enough data, answers will naturally emerge.
Yet, this often leads to a frustrating reality: vast, expensive collections of telemetry that still fail to explain why a critical issue occurred. This reveals a fundamental truth: telemetry without shared context is just noise.
When these signals are treated as separate “pillars” and stored in disconnected systems, the crucial relationships between them are lost. This forces engineers to manually search for connections across different tools, a slow and inefficient process that hinders rapid root cause analysis, especially during an outage.
A modern observability practice, however, is built on creating a single, unified stream of truth where context is built-in from the start. This isn’t something that can be pieced together after the fact; it requires a deliberate and consistent telemetry strategy across all services. The core of this strategy includes:
- Standardized semantic conventions: Using consistent naming for services, environments, and operations as defined by standards like the OpenTelemetry conventions.
- Context propagation: Ensuring trace and span IDs are passed between every service, linking all activity related to a single request.
- Rich resource attributes: Attaching uniform metadata like
service.name,deployment.environment, andcloud.providerto all telemetry.
With this foundation in place, every piece of data becomes inherently explorable and interconnected. The goal is no longer to simply store three pillars of data, but to provide you with instant access to a rich, unified dataset where you can debug at the speed of inquiry.
Overcoming the hurdles to modern observability
Achieving observability isn’t trivial. It comes with two major challenges:
1. High cost
High-cardinality data is essential for real observability, but at scale, collecting and storing every event can become prohibitively expensive. This creates a difficult trade-off between gaining insight and staying within budget.
In the past, this issue was addressed this by aggregating raw data into coarse summary metrics. However, this approach removes the detail that observability relies on. The modern solution is intelligent sampling.
Since most events in a healthy system are routine and successful, sampling allows you to retain a representative set of “normal” traffic while capturing all unique, failed, or anomalous events in full detail.
Unlike aggregation, sampling preserves the full cardinality of the events it retains. This dramatically reduces data volume while still enabling deep comparisons between successful and failing cases, which is crucial for effective debugging.
Ultimately, however, technical solutions like sampling are most effective when paired with a commercial model that supports them. A true observability platform should not penalize you for sending the rich, high-cardinality data needed for troubleshooting.
Dash0's pricing model, for example, addresses this directly by charging per data point, irrespective of its size or dimensionality. This ensures your costs are tied to usage, not the richness of your telemetry.
2. Instrumentation & vendor lock-in
Instrumenting every service to produce rich telemetry used to mean adopting a vendor’s proprietary agent, which often resulted in long-term vendor lock-in.
Today, OpenTelemetry (OTel) provides an open, vendor-neutral standard for generating and managing telemetry data.
But while OTel standardizes data collection, you still need a backend platform built to make sense of this high-cardinality data and turn them into insights.
This requires a platform engineered for this new class of data—a role Dash0 is built from the ground up to fill.
It ingests OTel data directly, without proprietary translation layers, and is optimized for the rapid, exploratory querying of complex telemetry. This allows you to ask any question of your systems and get immediate answers.
And with tools like point-and-click telemetry filters and simple, transparent pricing, Dash0 helps you keep costs at a predictable level without compromising on insight.
Final thoughts
Monitoring is an essential foundation for catching problems you already anticipated. It is vital, but insufficient for the complexity of modern distributed systems.
Observability is the necessary evolution. It is the practice of designing systems that can explain themselves by emitting rich, high-cardinality data, empowering you to investigate any condition, whether anticipated or not.
So the next time you’re faced with a “green dashboard” problem, ask yourself: Does my tooling allow me to ask the questions I haven’t thought of yet?
The answer to that question will determine whether you have monitoring, or if you are truly practicing observability.
Thanks for reading!
