Monitoring for your SLOs is not optional, keep it, invest in it, and make sure your alert thresholds reflect the failure modes your users actually experience. Then decide, based on your debugging evidence, whether your unknown-failure problem is real enough and expensive enough to justify the instrumentation overhaul that genuine observability requires.
What is the difference between monitoring and observability?
Monitoring and observability get treated as synonyms in a lot of job postings and vendor slide decks. They aren’t. The difference isn’t a matter of tool sophistication as it’s a difference in what questions you’re even capable of asking when something goes wrong.
Why doesn’t monitoring catch unknown failures?
People often describe observability as three pillars: metrics, logs, and traces. That framing is contested. Honeycomb argues that the real unit of observability is the arbitrarily wide structured event, a single record that carries every relevant attribute about a request at the moment it happened: user ID, build ID, request ID, tenant, region, feature flag state, whatever else you thought to attach.
What are the leading tools and providers in this space?
Monitoring is often built on pre-aggregation: you pick your metrics in advance, and aggregation can obscure the outlier. A performance spike that affects only a specific tenant on a specific shard can disappear into a rolled-up average. If you never defined a dimension to record, you can’t filter by it after the fact.
What does observability actually cost compared to monitoring?
The key concept here is cardinality. Cardinality, in this context, means the number of distinct values a field can take. User IDs are high-cardinality; they can be in the millions. A status code field has low cardinality, there are only a handful of values. What makes observability different is the ability to group, filter, and slice by high-cardinality fields without pre-defining the query. You can ask “show me latency broken down by build_id crossed with tenant_id” after the fact, because the raw events exist at that level of detail.
What is the most common mistake teams make when adopting observability?
The failure pattern to avoid is buying a new observability tool and assuming the capability transfers automatically. It doesn’t. The instrumentation has to change. The data model has to change. And that’s work, real, ongoing work, not a one-time purchasing decision.
The Real Split: Known Unknowns vs Unknown Unknowns
On the monitoring side, Prometheus, Grafana, Nagios, and Alertmanager represent the mature, widely-adopted stack. On the observability side, Honeycomb is a frequently cited advocate for the high-cardinality event model. OpenTelemetry provides vendor-neutral instrumentation plumbing that works across both approaches.
Mature engineering teams often run both. Monitoring handles the SLO-backed alerts, the known failure modes that should page a human at 3am. Google’s SRE book builds its alerting philosophy around the four golden signals (latency, traffic, errors, saturation), and that’s monitoring doing exactly what it’s designed to do. The observability layer is what you open after you’re already awake and trying to understand what’s actually happening.
Monitoring watches for failure conditions you’ve already anticipated. You define a metric, set a threshold, and get paged when the threshold is crossed. Observability is about your system’s ability to answer questions you didn’t know you’d need to ask — it requires high-cardinality, explorable telemetry that you can slice after the fact, without shipping new instrumentation.
How Classic Monitoring Works (and Where It Breaks Down)
Start by auditing the last five incidents your team debugged. For each one, ask: did the page come from a threshold alert on a metric you’d predefined? And when you sat down to find the root cause, what data did you actually reach for? If the answer is “we grepped logs and ran ad-hoc queries and still couldn’t isolate the problem to a specific user or request,” that’s the signal that your instrumentation doesn’t support the questions you’re asking.
No. Framing this as a binary choice is mostly a marketing artifact.
The other constraint is dimensional: you can’t slice by a field you never chose to record. If you didn’t instrument tenant ID as a label when you set up your metrics, tenant ID doesn’t exist in your data. You cannot easily add it retroactively to a time-series database without rebuilding your metric collection. That’s the “known unknowns” ceiling made concrete.
What Observability Actually Requires
That’s not a minor semantic distinction. It determines whether you can even begin debugging a novel failure, or whether you’re stuck staring at a dashboard that confirms something is wrong without telling you anything useful about where to look.
Buying an observability platform and pointing existing low-cardinality metrics at it. The tool changes; the debugging capability does not. Observability is a property of your instrumentation discipline and data richness, not a product SKU you can purchase.
*Cost characterizations reflect architectural properties of the respective data models; no specific pricing figures are cited because costs vary substantially by provider, request volume, and deployment model.*
The limitation is structural. Aggregation can hide the outlier. If you have a p99 latency spike that’s only hitting requests from one tenant on one shard, that spike can disappear into the averaged metric. The overall p99 looks fine. You get no page. Or you do get a page — when the problem has spread far enough to affect the aggregate, but by then you’ve already lost significant time.
Do You Have to Choose One or the Other?
The honest catch: high-cardinality storage is expensive. Time-series databases like Prometheus aren’t designed for it, high label cardinality can cause serious storage scaling problems. Columnar event stores and sampled traces exist to manage this cost, but they don’t eliminate it. Whether full observability is affordable at your request volume depends heavily on whether you’re running self-managed infrastructure or paying SaaS ingest pricing. OpenTelemetry, the vendor-neutral instrumentation layer, can help by keeping your telemetry portable across backends, so you’re not locked into a cost model that doesn’t scale for you.
The cleanest way to understand the difference is through a framing Charity Majors and the team at Honeycomb put into wide circulation: monitoring handles your known unknowns, observability handles your unknown unknowns. Known unknowns are the failure modes you’ve seen before, or can reason about in advance: CPU above 90%, error rate above 1%, disk full. You can write those thresholds down.
Consider a practical scenario: a team at a mid-sized SaaS company receives a page that their error rate has crossed 2%. That alert comes from their Prometheus stack, a monitoring threshold doing its job. They open their observability tool and start asking questions the monitoring system can’t easily answer: which users are affected, what version of the mobile client they’re running, which database replica their requests are hitting. Without high-cardinality event data, the debugging session stalls at “something is wrong with errors.”
Monitoring using pre-aggregated metrics tends to keep storage relatively flat even at high request volume, making it comparatively cheap and predictable. Observability with high-cardinality event storage is often significantly more expensive — the cost depends on whether you self-host or pay SaaS ingest pricing, and how much of your traffic you sample. There is no single industry price because the variance is that wide.
Evaluating Your Options: Monitoring and Observability Side by Side
| Attribute | Monitoring | Observability |
|---|---|---|
| Primary question answered | Is the predicted failure happening? | Why is this specific thing broken? |
| Data model | Pre-aggregated metrics, fixed labels | High-cardinality structured events |
| Representative tools | Prometheus, Grafana, Nagios, Alertmanager | Honeycomb, columnar event stores |
| Instrumentation standard | Custom scrapers, exporters | OpenTelemetry (vendor-neutral) |
| Cost profile | Often low, predictable (flat storage at scale) | Often higher, variable (SaaS ingest or self-hosted columnar) |
| Best fit | Known failure modes, SLO alerting | Novel failures, post-incident debugging |
| Core limitation | Can miss the outlier or unanticipated dimension | Expensive; requires high-cardinality data discipline to deliver value |
Classic monitoring is typically pre-aggregated by design. Prometheus scrapes your metrics endpoints, you configure thresholds, and Nagios or Alertmanager pages you when a threshold is crossed. The architecture is simple, the tooling is mature, and the cost model is often favorable: pre-aggregated metrics keep storage relatively flat even as request volume grows, because you’re storing a summarized number rather than every individual event.
What to Do Before You Change Anything
Monitoring answers “is the thing I predicted happening?” Observability answers “why is this specific request slow when I never predicted this?”
This is also why buying an observability platform without rethinking your instrumentation gets you nowhere. If your events are thin and low-cardinality, essentially the same data you were logging before, you’ve purchased a more expensive dashboard. The tool is there; the data richness isn’t. Observability is a property of your telemetry and instrumentation discipline, not a SKU.
Before evaluating any platform, answer these questions: Are your events structured and high-cardinality, or are they flat log lines and aggregated counters? Do you have OpenTelemetry instrumentation in place, or are you tied to a single vendor’s SDK? What’s your current request volume, and have you modeled the ingest cost difference between self-managed columnar storage and SaaS pricing at that volume?
