Why the distinction actually matters
Charity Majors, co-founder of Honeycomb and one of the people who pushed the term into mainstream engineering vocabulary, put it plainly:
Monitoring is about known-unknowns and actionable alerts. Observability is about unknown-unknowns.
That sentence explains most of the confusion in the market. Monitoring assumes you already know what "broken" looks like: CPU above 90%, error rate above 5%, response time above 800ms. Someone wrote that rule in advance, because they'd seen the failure before, or predicted it.
Observability exists for the failure nobody predicted. A checkout service that's fine in Frankfurt but timing out in São Paulo, only for one payment provider, only after a deploy three services upstream touched a shared library. No dashboard was built for that scenario. Nobody could have built one. The team needs to ask new questions of the data they already collected, and that's the entire point of observability.
What monitoring is actually built to catch
Monitoring is the discipline of watching known signals against known thresholds. CPU, memory, request volume, error rate, queue depth, uptime. It's the layer that pages someone at 2am because a number crossed a line.
Good monitoring setups usually cover:
Infrastructure health: compute, storage, network throughput, capacity headroom.
Application behavior: error rate, latency, throughput, failed transactions.
Database load: query duration, connection pool saturation, replication lag.
Business-critical journeys: login success, checkout completion, payment confirmation.
The failure mode of monitoring isn't that it's wrong, it's that it only sees what it was told to look for. A team that only monitors CPU and memory will miss a slow memory leak masked by auto-scaling, or a dependency quietly returning 200s with empty payloads. The alert never fires because nobody defined that specific condition as "broken."
What observability adds that dashboards can't
Observability is the ability to ask an arbitrary question about system behavior after the fact, without having shipped new code to answer it. That capability comes from three types of data working together, not from any single tool.
Logs record what happened: which request failed, which dependency timed out, which user action triggered the error. Structured logs, tagged with service name, environment, request ID and version, turn a wall of text into something searchable across a whole incident.
Metrics turn activity into numbers over time. They're cheap to store and good at showing trend, which is exactly why monitoring leans on them. A slow, steady climb in p99 latency over two weeks is a metrics story before it's ever a page.
Traces follow one request across every service it touches. In a system where a single checkout call hits an API gateway, an auth service, a payment provider and an inventory database, a trace is often the only thing that shows which hop actually ate the 4 seconds.
None of the three works alone. Logs without traces tell you something failed but not where in the chain. Metrics without logs tell you latency spiked but not for whom. The three pillars of observability earn that name because removing one leaves a genuine blind spot, not a minor inconvenience.
Where the two collide in a real incident
A checkout error rate alert fires at 3pm. That's monitoring doing its job: a known threshold, a defined condition, a page to the on-call engineer.
What happens next is observability. The engineer pulls logs filtered to failed checkout requests and notices they all share one payment method. Metrics confirm the spike is isolated to one region. A trace shows the payment service waiting on a call to an external provider that used to return in 200ms and now takes 6 seconds. Nothing crossed a pre-built threshold for "external provider slow," because nobody had written that rule. The team finds it anyway, because the data was there to query.
This is also where teams that have already defined service level objectives move faster. If checkout has a documented SLO and an error budget, the on-call engineer already knows how much room is left before this incident becomes a breach, and that shapes whether the response is a quick mitigation or an all-hands rollback. Readers who haven't formalized that layer yet can compare the two concepts directly in our article on SLO vs SLA, which covers how objectives and contractual commitments interact once monitoring turns up a problem.
Mantu's SRE consulting and observability services work through exactly this kind of incident chain with clients: not by adding another dashboard, but by making sure logs, metrics and traces are wired to answer the questions that come up mid-incident, not just the ones anticipated at build time.
Picking monitoring tools that don't create blind spots
Most teams don't lack monitoring tools. They have four or five, purchased at different times for different reasons, none of them talking to each other. That fragmentation is worse than having too few tools, because it creates false confidence: a dashboard is green, but it's only watching a third of the request path.
Before adding another tool, it's worth checking whether the current stack actually maps to the journeys that matter to the business, not just the infrastructure that's easiest to instrument. A database that's 100% healthy on paper doesn't help if the API gateway in front of it is dropping requests under load.
A few questions worth asking about any monitoring tool under consideration:
Does it correlate with the other signals already in place, or does it live on its own island?
Can an engineer go from an alert to a trace in under a minute, or does that require switching between three logins?
Does it cover the actual customer journey (checkout, login, upload) or only the infrastructure underneath it?
Organizations rebuilding this stack from scratch, or trying to consolidate five overlapping tools into one coherent setup, often bring in Mantu's SRE consulting team to define which signals matter before buying anything new.
Alerting vs observability: a signal is not an explanation
Alerting and observability get bundled together, but they solve different problems. An alert is a notification tied to a threshold. Observability is the capacity to investigate once that notification arrives.
Alerting | Observability | |
|---|---|---|
Job | Notify when a known condition is met | Explain behavior, including conditions nobody defined in advance |
Trigger | Predefined threshold | Open-ended query against logs, metrics, traces |
Output | A page, an email, a Slack message | An answer to "why is this happening" |
Fails when | The condition wasn't anticipated | The underlying data wasn't collected or connected |
A team that only invests in alerting ends up with engineers who get paged constantly but can't explain half the incidents they resolve, because resolution came from guesswork or a lucky redeploy rather than evidence. A team that only invests in observability tooling but never wires up alerts finds problems only when a customer complains, because nothing notified anyone in real time.
The muscle matters more than the stack
Buying an observability platform doesn't make a team observable. The teams that get real value out of this work first agree on what "healthy" means for their critical journeys, then decide which signals actually answer their hardest incident questions, and only then shop for tooling.
That sequencing, business journey first, signal second, tool third, is usually the difference between an observability investment that pays for itself during the next 3am incident and one that turns into another unused dashboard. Mantu's SRE consulting services help teams work through that sequencing before a single tool gets purchased.
