No one wants to build a blind system, and I think every distributed system will eventually become one.
Modern observability is today's 'control performance', tracing everywhere, with warnings dialled to the decimal point, and anomaly detectors humming. Every stack shines brightly until it fails in ways the dashboards were never trained on. The irony is not that we lack data. It's that we're drowning in it, also that we can't separate signal from self-noise.
This is the beauty of observability debt, the insidious, incremental cost of mistaking more data for more truth. It's like technical debt; it accumulates quietly, beneath uptime metrics and well-crafted post-mortem slides. And before you know it, it's already influencing the way your teams think, react, and invest.
The Illusion of Measuring Everything
When monitoring became "observability," it promised insight, not just knowing what broke, but why. The universe pushed it back in layers: metrics, logs, traces, distributed context propagation, and dashboards so thick they needed dashboards to look at them.
But scope insidiously became a stand-in for understanding. Every new service introduces new tags; each team has its own telemetry vocabulary. What started as visibility turned into instrumentation bloating.
According to Grafana’s State of Observability 2025 report, 95 per cent of organisations collect metrics, 87 per cent gather logs, and 57 per cent use tracing as part of their observability practice. The irony: mean-time-to-detect has yet to keep pace proportionately. We see more but understand less.
This is where observability debt begins, not with missing data, but with excess context. Each graph adds a small layer of abstraction between engineers and reality. Over time, teams stop debugging systems; they debug dashboards.
A healthy observability practice should ideally shrink uncertainty. Instead, we’ve industrialised it.
How Distributed Truth Fragments
Distributed systems don't fail large; they rust away slowly. A small timeout, a spike of retries, and before long, all microservices report success as users are kept waiting. The facade works because our metrics are designed to average pain out of mind.
Each zone possesses healthy percentiles; aggregation hides the 99.99th percentile tail where real failures live. Eventual consistency obscures errors into statistical oblivion. Machine learning–based anomaly detectors "normalise" recurring issues until they are imperceptible in alarm channels entirely.
The result is a system that looks stable but feels brittle, a system that satisfies all SLOs but insults human patience.
Engineers unconsciously inherit this responsibility by default. Frameworks export boilerplate metrics; auto-instrument functions do cloud SDKs. Observability has become config, not reason. When it fails, you don't diagnose causality; you query indexes built by someone else's schema.
And because dashboards do not typically disagree, they come to be seen as objective. They are not. They are curated hallucinations, coherent, consistent, and occasionally wrong.
When Clarity Is Expensive
For years, companies accepted observability cost as noise in the background, a few percentage points of cloud spend justified as "insurance." That number no longer resides in the margins.
Splunk’s State of Observability 2025 notes that 65 percent of organisations claim measurable revenue benefits from observability. Yet even as its business value grows, the operational cost curve steepens and the mental overhead compounds faster than the savings
What's less obvious is the mental toll. Dashboards double cognitive branches; each panel is a condition your mind needs to resolve before taking action. Engineers spend sprint cycles refining alert rules nobody ever reads or tracing traces that already validated what they had guessed.
You feel observability debt in the moments of flurries. The room fills with graphs; none tell the same. Someone opens his mouth and utters, "What changed?" BUT silence ensues.
The tools can bring data to the surface, but surely not judgment.
Debt is not technical; it's epistemic. It alters how teams conceive of truth. Once confidence shifts from human understanding to dashboard consensus, critical thinking unravels quietly in the background of normalised measures.
Paying the Interest
The first payment on any debt at all is awareness. Observability debt is not something that can be paid with throwing more panels at it or employing yet another stack of metrics. It demands subtraction, considerate ignorance for the sake of higher insight.
Four disciplines separate teams that control debt from those controlled by it:
Instrument for intent, not existence.
If you can't map a metric to a business outcome or a user-perceived action, drop it. Observability should reflect how well you're serving the purpose, not your CPU utilisation.
Get rid of unused telemetry.
Ownerless metrics are clutter. One simple policy, automatic expiry of unvisited dashboards after 90 days, keeps your head clear.
Create replayable context.
Log structures that enable system state reconstitution after an incident are worth more than terabytes of transient metrics. Replayability trumps immediacy.
Budget observability on par with infrastructure.
Each new metric must have an obvious cost centre. Visibility only has value when it is so scarce that it can be interpreted.
Each of these principles is a no-brainer. No organisation follows them because less is seen as neglect. Discipline in engineering is, however, measured not by how little we gather, but by how little we must think well to do so.
The Human Layer We Forgot
Behind every incident dashboard, there is a human reconciling contradictoriness. The more tooling sophistication, the more tempting it is to replace mastery of configuration with understanding.
A healthy observability culture prioritises curiosity over certainty. Mature teams have on-call shifts that include data delete days, meetings for questioning the value of every metric. That ritual isn't symbolic; it's survival.
When observability debt finally hits you, you can locate its symptoms months in advance of outages. Alert fatigue grows. Post-mortems recycle the same graphs. Engineers use dashboards as blame assignment interfaces and not hypothesis generation interfaces.
Pay off that debt, and you're restored to the humility that exposure had once provided. You start asking more humble questions: What does this number do for the user? Who cares about this metric tomorrow? If it vanished, would people even notice, or would our pages load a tad faster?
AI and the New Layer of Synthetic Confidence
By late 2025, observability platforms will increasingly be based on generative abstracts and causal inference models. They consume telemetry and put it in human words: "Database latency normalised after network jitter." Handy, reasonable, and occasionally fabricated.
Synthetic confidence already accumulates here: dashboards that sound right but never got the trust of human validation.
New Relic’s 2025 Observability Forecast found that 54 per cent of organisations now use AI-assisted monitoring and analysis, up from 42 per cent in 2024. Yet adoption does not equal trust. Many teams still treat AI-generated summaries as hypotheses, not the truth. The risk is not that AI misreads telemetry, but that it does so confidently, faster, smoother, and more convincingly than any dashboard a human would question. LLM-facilitated summarisation gets diagnosis cycles faster at the expense of the investigative muscle of engineers. In the end, teams outsourced understanding, the final, un-substitutable ability that turns observability into observability, rather than reporting.
The healthier future does not replace human judgment with AI; it brings AI in as a conversational peer, AND systems need to explain what they know, not inform us of what we ought to believe.
Dashboards to Dialogue
The future of reliability engineering will require fewer dashboards and more discussion. Observability isn't watching machines; it is creating a shared reality between humans who design them.
As systems become more composable and decentralised, teams need epistemic contracts, an agreement about what constitutes the "truth" between systems. Without it, every occurrence is a philosophical debate conducted in JSON.
Consider observability as a language, not a lens. Each metric is a word; each trace is a sentence. Being proficient means having an understanding of the data grammar. That's where meaning is, in structure, not visualisation.
Finally, winning scale-handling businesses won't be the ones that gather dashboards. They'll be the ones that have boring dashboards because systems expose their own activity clearly.
Observability debt will never disappear, but it can be endured through curiosity, humility, and the strength to measure less so we can learn more.
The Quiet Cost of Knowing Too Much
Perfect dashboards do not guarantee healthy systems. They guarantee comfort, a feeling of control that reality rarely cares about.
The best engineers I’ve met distrust smooth graphs. They pause at the flat lines, asking what kind of noise was filtered out to make the picture this neat. They recognise that resilience begins not with more telemetry, but with being more honest about what telemetry cannot capture.
Observability debt is definitely the price of confusing visibility with insight. To pay it off, start with a question no tool can answer: Do we really know what we think we see?