Monitoring vs Observability: When You Actually Need Which
Observability has become one of those words that gets thrown around so much it barely means anything anymore. Every vendor claims to sell observability. Every conference talk mentions it. And if you read the thought leadership content, you'd think monitoring is outdated and everyone should be investing in distributed tracing and structured logging.
Most teams don't need full observability platforms. They need better monitoring.
What Monitoring Actually Tells You
Monitoring is about known unknowns. You decide what metrics matter, you track them, and you alert when they cross thresholds. CPU usage, error rates, request latency, queue depth, these are things you know to watch because they indicate health.
Good monitoring answers questions like "is the system working right now?" and "did something break in the last hour?" It's reactive. Something goes wrong, you get an alert, you investigate.
The limitation of monitoring is that it only tells you about what you thought to measure. If you didn't set up a metric for a particular failure mode, you won't know it's happening until users complain.
What Observability Adds
Observability is about unknown unknowns. It's designed for situations where you don't know what question you need to ask until you're in the middle of debugging. Instead of predefined dashboards, you have high-cardinality data that lets you slice and group by arbitrary fields.
With observability tooling, you can ask questions like "show me all requests from this specific user that hit this endpoint with this header value and took longer than 200ms." You couldn't have predicted that query ahead of time, but when you're troubleshooting a weird issue, it's exactly what you need.
The trade-off is cost and complexity. Storing high-cardinality data is expensive. Building instrumentation that captures everything is time-consuming. And querying that data requires learning new tools and query languages.
When Monitoring Is Enough
If you have a handful of services, well-understood failure modes, and a relatively simple architecture, monitoring is probably fine. You don't need distributed tracing to debug a three-service application. You can get by with logs, metrics, and some basic dashboards.
Here's a reasonable test: if you can troubleshoot most incidents by checking a few dashboards and tailing logs, you don't need observability yet. Monitoring is working for you.
Adding observability when monitoring is sufficient just adds overhead. You'll spend time integrating tools, instrumenting your code, and learning new interfaces, all for capabilities you don't actually use.
When You Need Observability
Observability starts paying off when your system is complex enough that monitoring can't keep up. This usually happens when:
You have many services. If you have dozens of microservices and a request touches 10 of them, tracing helps you understand where time is being spent and where failures are happening.
You deal with high cardinality. If you need to debug issues specific to certain users, tenants, or feature flags, observability tools let you filter and group in ways that traditional monitoring doesn't handle well.
Failures are subtle. If your incidents involve weird interactions between services, edge cases you didn't anticipate, or performance issues that only affect a subset of traffic, observability gives you the tools to investigate.
If you're a three-person startup with a monolith and a database, you probably don't have these problems yet. If you're running a multi-tenant SaaS platform with 20 services and complex routing logic, you might.
You Still Need Good Monitoring
Even if you go all-in on observability, you still need monitoring. Observability tools are great for investigation, but they're not always the best choice for alerting.
Alerts should be simple and reliable. You want to know when error rates spike or latency crosses a threshold, and you want that alert to fire every time without depending on a complex query or a heavy data pipeline.
Most teams end up with a hybrid setup: monitoring for known issues and alerts, observability for debugging and exploration. That's a reasonable approach.
Observability Is Not a Magic Fix
A lot of observability marketing suggests that if you just buy the right tool, all your debugging problems will disappear. That's not how it works.
Observability tools give you visibility, but they don't tell you what's wrong. You still need engineers who understand the system, know how to form hypotheses, and can interpret the data. Bad instrumentation or poorly structured logs make observability useless, just like bad monitoring is useless.
Start With Monitoring, Add Observability When It Hurts
If you're building a new system, start with monitoring. Track the basics. Set up alerts. Build dashboards for the things you care about. Get good at using those tools.
When you start hitting the limits of monitoring, when you find yourself saying "I wish I could filter by this dimension" or "I need to trace this request across services," that's when you add observability.
Don't adopt observability because it's trendy or because everyone else is doing it. Adopt it when you have a specific problem it solves.
And if monitoring is working fine, keep using it. There's no prize for using the fanciest tools. There's only the goal of understanding your systems well enough to keep them running.