Observability vs Monitoring: What's the Difference?

Monitoring watches for problems you already know about, like a server running out of disk. Observability is the ability to work out why something is going wrong, even when it is a problem nobody predicted. In the observability vs monitoring debate, you don't pick one: monitoring raises the alarm, and observability helps you find the cause.

This guide explains both in plain terms, then covers data observability, which applies the same idea to your data pipelines and reports.

What monitoring is

Google's SRE book defines monitoring as collecting, processing, aggregating and displaying real-time numbers about a system. Think error counts, response times and CPU use.

You decide in advance what to watch, set a threshold, and get an alert when it is crossed. "Alert me if the checkout page takes more than 3 seconds" is monitoring.

The same book suggests starting with four "golden signals":

• Latency: how long a request takes.
• Traffic: how much demand there is, such as requests per second.
• Errors: how many requests fail.
• Saturation: how full your most limited resource is (memory, disk, database connections).

If you only measure four things about a customer-facing system, these are a good four.

What observability is

The OpenTelemetry project puts it simply: observability lets you understand a system from the outside, by asking questions about it without knowing its insides in advance.

The key difference is the unknown problem. Monitoring answers questions you thought of beforehand. Observability lets you ask new questions after something strange happens.

Picture a Shopee seller's own ordering site. Monitoring says "errors went up at 9pm". Observability lets you find that the errors only hit customers paying with one e-wallet, on the mobile app, after a new release.

The three signals

Observability tools mostly work with three kinds of telemetry (data a system sends out about itself), as defined by OpenTelemetry:

• Logs: timestamped messages, such as "payment failed for order 1234".
• Metrics: numbers summarised over time, such as error rate per minute.
• Traces: the path one request takes as it passes through several services. Each step is called a span.

Traces matter most in modern systems. A single click can touch a website, a payment service, a stock system and a database. Without a trace, each team only sees its own piece.

Observability vs monitoring at a glance

	Monitoring	Observability
Main question	Is something wrong?	Why is it wrong?
Works best for	Known failure types	New or unexpected problems
Typical output	Dashboards and alerts	Searchable logs, metrics and traces
Set up	Pick metrics and thresholds	Instrument code to send rich telemetry

Engineer Charity Majors, co-author of Observability Engineering, makes a useful point: monitoring is still needed, but simpler, predictable systems got by on it alone. Distributed systems, with many moving parts, need more.

Do you actually need observability?

Not every business does on day one. A simple company website on one server can be well served by uptime checks and basic alerts.

You will feel the need for observability when:

• Your app is split into several services or uses many cloud services.
• Outages take hours to diagnose because everyone is guessing.
• Problems hit only some customers, some regions or some devices.
• You release changes often and need to see what each release changed.

If you are moving to serverless or microservices, plan for it early. See our guide to serverless computing for why short-lived functions make this harder.

Data observability: the same idea for your data

Data observability asks the same "why" question about data, not software. IBM describes it as monitoring, managing and maintaining data so it stays high quality, available and reliable across your systems and pipelines.

It matters because a broken data pipeline rarely crashes loudly. A sales dashboard just quietly shows yesterday's numbers, or half the rows, and someone makes a decision on it.

IBM lists five pillars to watch:

1. Freshness: is the data up to date?
2. Distribution: are values within a normal range? A sudden jump in "average order value" may be a bug.
3. Volume: did the expected amount of data arrive?
4. Schema: did the structure change, such as a renamed or missing column?
5. Lineage: where did this data come from, and what depends on it?

For a Penang logistics firm, that might mean an alert when the overnight delivery file from a partner arrives late or with half the usual rows, before the morning report goes to management.

Data observability also matters for AI. A model fed stale or broken data gives poor answers with full confidence. Our AI infrastructure guide covers data pipelines in more detail.

How to get started

You don't need a big platform to begin. A sensible order:

1. Monitor the golden signals for your most important customer-facing service.
2. Centralise logs so you can search them in one place, not by logging into each server.
3. Add tracing with an open standard such as OpenTelemetry, so you are not locked into one vendor.
4. Add basic data checks on key tables: freshness, row counts and schema.
5. Write down what each alert means and who responds. An alert nobody owns is noise.

Keep security in mind too. Logs often contain personal data such as names, phone numbers and IC numbers. Under Malaysia's PDPA, that data still needs protecting, so mask it before it is logged and limit who can see the logs.

The bottom line

In observability vs monitoring, monitoring is your smoke alarm and observability is the ability to find where the fire started. Start with good monitoring, add observability as your systems grow, and apply the same thinking to your data so the reports people rely on can be trusted.

Quick answers

Is observability replacing monitoring?

No. Monitoring is part of observability. You still need alerts on known problems; observability adds the ability to investigate unknown ones.

What are the three pillars of observability?

Logs, metrics and traces. Logs record events, metrics summarise numbers over time, and traces follow a single request across services.

What is data observability in simple terms?

It is checking the health of your data the way you check the health of your servers: is it fresh, complete, in the right shape, and do you know where it came from?

Do small businesses need observability tools?

Not always. If you run a simple website or a single app, good monitoring and central logs may be enough. Observability pays off as systems get more complex.