Observability Won't Save You at 3 A.M

The SRE industry keeps telling itself: invest enough in observability and your operations problems go away. The tooling vendors love this story. Spend a few million euros a year on their platforms and you get "full visibility." Except visibility is not the same as resolution.

Many years working in Observability and Operations has taught me one of many things: observability is one step in a three-step problem, and almost nobody talks about the other two. The three steps are observability, interpretability, and actionability.

Observability

The majority of the industry still rests on the "three pillars" of observability: metrics, logs, traces. You instrument your services, you collect your data, you build dashboards, you tune alerts. And you spend a fortune doing it. With that mindset, Observability has become one of the biggest cost sinks in engineering. Companies have invested millions into instrumentation, into massive data platforms, into teams maintaining all of it.

The promise was always: with enough data, you can solve your operations problems. Here is where it falls apart. Observability, as it's implemented by many today, tells you what your system is doing and if something is wrong. It does not tell you why and what that data means when things go wrong. That part is on you.

Interpretability

It's 3 A.M., an alert fires. Maybe a hundred alerts fire. You get out of bed, open your laptop, and there it is: millions of log lines, 10 million metrics, dozens of dashboards, a wall of noise. Some of those alerts relate to the actual issue. Some don't. Good luck figuring out which is which.

This is interpretability. Going from raw signals to actually understanding what is wrong. And it is almost entirely manual. Your observability platform gives you the data, maybe a nicer query language, maybe some anomaly detection that fires so often nobody trusts it anymore. But connecting the dots, finding the root cause, understanding why the system is broken right now: that is left to the engineer. One person, half awake, under pressure, hunting for a needle in a haystack.

If you apply observability really, really well, not the three-pillars version but proper event-driven, high-cardinality observability with carefully structured data, you can get close to solving this. An experienced engineer in a well-instrumented system can find root causes fast. But "experienced" is doing heavy lifting there, and in practice almost no company gets to that level. Most teams are stuck with someone spending thirty minutes to two hours digging through data while the incident bleeds out.

Actionability

Say the engineer finds the root cause. Great. Now what?

Observability cannot help here. By design, observability is completely separated from the systems it monitors. It has no connection to the APIs, the infrastructure, the deployment pipelines, the configuration endpoints where you actually make changes. Your dashboard can tell you a pod is crash-looping. It will not roll back the deployment that caused it. Your logs can show you a bad environment variable. They will not fix it.

The engineer has to leave the observability tool entirely. Open kubectl, or the cloud console, or the CI/CD pipeline. Figure out the right remediation. Assess the risk. Execute the fix. That is actionability, and observability will never get there. It is architecturally incapable of it.

So Where Does That Leave Us?

Observability will never reach step three. It barely scratches the surface of step two. It generates huge volumes of data at huge cost and then leaves the engineer to do the hard parts: interpreting what the data means and acting on what they find.

When production is broken at 3 A.M., you don't want step one. You want the issue resolved. You want to wrap it up, get back to bed, know the system is healthy. That means you need all three steps, and observability only covers the first.

This is the direction we're building at Hyground. We take the flood of signals your observability stack already produces and give you an interpretation of what is actually wrong. A root cause. A hypothesis for remediation. And very soon, the ability to apply that fix directly, or at the very least guide the engineer to the exact point where one click resolves it.

Because at the end of the day, nobody cares how many metrics you collected. They care whether the problem gets fixed.

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