The observability vs. monitoring distinction has been argued about for years, and a lot of the debate has generated more heat than light. Some of it is marketing: tool vendors claiming their product is "observability" while competitors are "just monitoring." Some of it is genuinely useful conceptual work that changes how you think about system visibility.
The part that matters for alerting strategy is specific: whether you're monitoring known failure modes or developing the ability to investigate unknown ones. This is not a binary choice; most healthy platform practices include both. But the mix determines what you instrument, what you alert on, and what kinds of noise your system generates.
The monitoring model: known failures, known signals
Traditional monitoring is hypothesis-driven. You have a theory about how your system can fail, and you instrument to detect that failure. CPU gets too high, the service degrades. Error rate rises above X%, users are affected. Disk fills up, writes start failing. You encode these hypotheses as alert rules, and you get paged when the hypothesis appears to be confirmed.
This model works well when failures are predictable and your failure hypotheses are correct. It has two structural weaknesses. First, it only catches failure modes you anticipated when you wrote the rules. Novel failures (a new code path under load you didn't test, an interaction between two components you didn't model) may produce no signal in your monitoring until they cascade into something large enough to cross one of your crude threshold rules. Second, every hypothesis encoded as an alert rule is a potential false positive. If the hypothesis is wrong, imprecise, or outdated, the rule fires noise.
Most production alert rule sets accumulate both of these problems over time: gaps in coverage for failure modes the team hasn't experienced yet, and stale rules generating noise for failure modes the system has moved past.
The observability model: unknown unknowns, rich context
Observability as a technical concept comes from control systems theory: a system is observable if its internal state can be inferred from its external outputs. Applied to software, this means instrumenting your services with enough telemetry (logs, metrics, traces) that you can answer arbitrary questions about their behavior, not just the questions you anticipated when you set up your monitoring.
The three pillars framing (metrics, logs, traces) is one practical implementation of this idea. Rather than pre-encoding all the questions you'll want to ask, you collect enough raw signal that you can ask questions retroactively. When something goes wrong, you can investigate by querying your telemetry rather than by relying on whatever pre-configured alerts happened to fire.
This is the part that matters for alert design: if your system has good observability properties, you can alert on fewer known failure modes (because you'll catch most failures during investigation even without precise alerts) and rely more on broader signals that indicate something is wrong without specifying what. Symptom-based alerting rather than cause-based alerting.
Why the distinction changes your alert strategy
A monitoring-only strategy tends to produce many specific alert rules, each watching a particular failure hypothesis. The rule set grows over time as the team adds rules after each incident ("we didn't have an alert for this, add one"). This growth is natural and well-intentioned, but it tends to create noise because every new rule is another potential false positive, and the overall rule count grows without systematic pruning.
An observability-informed strategy uses fewer, higher-signal alerts. The primary page-worthy alerts are symptom-based: elevated error rate for customer-visible requests, SLO burn rate above a threshold, latency at the 99th percentile above a service-specific threshold. These alerts are coarser than cause-based alerts but more reliable: if a customer-visible error rate is elevated, something is wrong, full stop. The investigation then uses your rich telemetry to diagnose what.
The cause-based rules (CPU high, memory pressure, queue depth above N) still exist, but they're informational rather than page-worthy. They show up in dashboards. They generate low-urgency notifications during business hours. They don't wake people at 2am, because high CPU by itself isn't an incident; it's a precursor to a possible incident, and the investigation starts when the symptom alert fires.
Where noise comes from in each model
Monitoring-heavy stacks generate noise primarily through stale cause-based rules. A rule that was written to catch a specific failure mode that the service has since moved past, or a rule with a threshold that made sense when the service had different load characteristics. These rules fire regularly and are never real incidents. The team acknowledges them and moves on. After a few months, nobody trusts the alerts anymore.
Observability-heavy stacks generate different noise patterns. Symptom-based alerts fire less often and are usually real, but they fire without telling you where to look. If you don't have good trace coverage or structured logging, you wake up to "error rate elevated" and spend 40 minutes drilling through logs to find the cause. That's not a false positive, but it's a page that could have been more useful. The investment in instrumentation quality pays off directly in how quickly you resolve when the symptom alert fires.
Neither model is noise-free. What ObsrvHQ adds is a baseline layer that applies to both: regardless of whether a rule is cause-based or symptom-based, we check whether the firing is consistent with known historical patterns for that service. A symptom alert that fires every Tuesday morning because your batch job runs is still noise, even though it's a real symptom-based alert on a real metric.
The practical transition
Teams usually can't flip from monitoring-heavy to observability-informed overnight. The instrumentation investment is significant, and you need the monitoring rules as a backstop while you build better telemetry coverage.
A pragmatic middle path: audit your existing alert rules by category. Identify which rules are symptom-based (error rate, latency, availability) and which are cause-based (resource utilization, process counts, dependency health). Elevate the symptom-based rules to your primary page-worthy tier. Move cause-based rules to lower urgency. Add dynamic baseline suppression for all of them to catch the stale-threshold noise problem.
Over time, as you instrument more services with distributed tracing and build structured logging patterns, you can start retiring cause-based rules when the investigation telemetry makes them redundant. "We no longer need the database connection count alert because trace data shows us exactly which service is exhausting the pool" is a legitimate reason to remove a rule, and it's the kind of cleanup that only becomes possible once you've built the observability foundation that makes the cause-based rule unnecessary.
The goal isn't to reach a state with zero monitoring rules and pure observability. It's to reach a state where the alerts that page you are a reliable indicator that something worth investigating is happening, and where the investigation tooling gives you enough context to resolve it efficiently.