The Hidden Price of Poor Observability: Scale-Up Case Studies

Key takeaways

• Scale-ups often only discover observability gaps after outages or customer complaints.
• Blind spots in metrics, logs, and traces inflate engineering costs and slow innovation.
• Investing in observability early protects customer trust, reduces downtime, and enables faster growth.

Scale-ups live in a delicate stage of growth. They are no longer early startups, hacking together features in search of product–market fit, but they are not yet enterprises with the mature processes and infrastructure to match their ambitions. This middle ground is where growing pains appear, and one of the most painful is poor observability.

What starts as a few services with simple dashboards quickly becomes dozens or hundreds of distributed components spread across clouds and regions. Without a clear view of how these systems interact, blind spots creep in. Issues that should take minutes to resolve stretch into hours or days.

Forbes has repeatedly noted that downtime can cost digital businesses millions per hour, depending on sector and customer base. For scale-ups, the absolute numbers may be smaller, but the proportional damage is just as severe.

What observability really means

Observability is often confused with monitoring, but the two serve different purposes. Monitoring answers the question, “Is my system working?” Observability asks, “Why is it not working?”

Metrics, logs, and traces form the three classic pillars, but observability at scale is less about collecting data and more about connecting it. Cindy Sridharan, author of Distributed Systems Observability, captures the difference well: “Monitoring tells you when something is wrong, observability lets you explore why.”

For a scale-up, this distinction becomes existential. At the seed stage, a handful of engineers might SSH into servers and tail logs manually. That approach may be clunky, but it works when traffic is low and the system is small. By the time the company is handling thousands of daily transactions, expanding into new regions, or serving enterprise clients, those shortcuts collapse. A missing trace can mean hours of guesswork. An alert that only measures uptime but not latency can mean customers churn without anyone noticing until it’s too late.

The hidden costs of poor observability

The first cost is operational. Poor observability increases mean time to detect (MTTD) and mean time to resolve (MTTR). An incident that should have been noticed within minutes may go undetected for hours. In financial services or healthcare, that delay can carry regulatory penalties. In SaaS, it may mean enterprise customers walking away.

The second cost is engineering productivity. Debugging distributed systems without unified logs and traces is like looking for a needle in a haystack without a magnet. Engineers burn days combing through fragmented data. As headcount grows, knowledge is siloed across teams, making root-cause analysis even harder.

The third cost is strategic. When engineers spend most of their time firefighting, that is time not spent on innovation. At Netguru, we have seen this firsthand in fintech and SaaS projects. The opportunity cost of poor observability is invisible but immense. Every day spent tracking down an incident is a day competitors spend building something new.

Forrester has emphasized that downtime and degraded experiences are not just technical failures but customer experience issues. The same principle applies to scale-ups: observability gaps erode customer trust in ways that directly impact growth.

Real life lessons learned

HiredScore

HiredScore, an AI-driven HR tech company, provides a useful case study of what happens when observability lags behind growth. The business faced a massive scale-up challenge: workloads increased more than twentyfold in a year, across multiple cloud environments and regions.

On paper, their monitoring setup was functional. In practice, it was fragmented. Alerts were inconsistent, telemetry was duplicated across environments, and engineers lacked a single, unified view of the system. As they described in their engineering blog, more time was spent correlating logs across clusters than solving the incidents themselves.

The cost wasn’t just technical debt—it was slower product delivery. Engineers who could have been building features for enterprise clients were instead spending days piecing together distributed traces. HiredScore’s lesson was clear: scaling without a unified observability layer multiplies complexity, and even the best teams struggle to stay ahead of the curve.

Fintech scale-up

A fintech scale-up operating in a heavily regulated sector learned a harsher lesson. Their transaction pipeline experienced an outage that went unnoticed for hours. The reason was simple: they had monitoring in place, but it tracked uptime, not the deeper transaction flows that actually mattered to customers. With no centralized logging and limited traceability, the issue only surfaced when frustrated customers contacted support.

By then, the damage was done. Transactions had failed silently, customers had lost trust, and regulators began asking questions. The company faced not only reputational harm but also fines and delayed expansion plans.

Realizing that piecemeal monitoring was no longer sustainable, the company implemented a dedicated observability platform, Chronosphere. The impact was immediate. Detection times improved fourfold, and the delay between data generation and dashboard visibility—known as “time to glass”—improved nearly ninefold.

In Netguru’s fintech projects, we’ve observed the same principle: in regulated industries, observability is not optional. It is as much about compliance and customer trust as it is about engineering. Blind spots in monitoring can quickly escalate into legal and financial risks.

SaaS collaboration platform

A mid-sized SaaS provider scaling across North America and Europe faced a different but equally costly challenge. As the company grew, each team adopted its own monitoring tools. Some relied on commercial vendors, others on open-source solutions. The result was a fragmented stack with overlapping costs and no unified visibility.

Engineers reported consistently high mean time to repair because they had to jump between tools, correlating partial data sets manually. Meanwhile, monitoring bills skyrocketed. Most damaging of all, engineers spent more time firefighting than building. Product delivery slowed, delaying roadmap commitments and frustrating enterprise clients.

The company eventually consolidated its observability stack around open-source tools—Prometheus for metrics, Grafana for visualization, Elastic APM for traces. The result was a halving of monitoring costs and a 40% improvement in MTTR.

Best practices for scale-ups

The lessons from these scale-ups converge on a few best practices.

The first is to invest early. Observability cannot be bolted on after the fact without painful rewrites. Integrating logs, metrics, and traces into CI/CD pipelines from the beginning creates a culture of visibility.

The second is to centralize. Tool sprawl is seductive—different teams have different preferences—but the long-term cost is duplication, inconsistent data, and slower incident response. A single platform avoids fragmentation and gives teams a shared source of truth.

The third is to tie observability to business outcomes. Google’s Site Reliability Engineering framework emphasizes the importance of SLIs (Service Level Indicators) and SLOs (Service Level Objectives) that reflect customer experience, not just system health (Google SRE Book). Scale-ups that measure latency on key user flows, error rates on critical APIs, or throughput on financial transactions gain a clearer view of how technical issues impact real customers.

Finally, observability should be treated as a product, not plumbing. This means assigning ownership, documenting standards, and training engineers across teams. ThoughtWorks emphasizes that platform engineering is becoming central to scaling observability, precisely because it requires dedicated stewardship rather than ad hoc fixes.

From our work with scale-ups in fintech, healthcare, and SaaS, we’ve seen that poor observability rarely shows up on a balance sheet, but its costs are everywhere. A team that spends the majority of its time in incident resolution is effectively at half velocity. A company that fails to detect outages until customers complain is undermining its own growth.

In our projects, we encourage clients to think of observability not as an operational necessity but as a growth enabler. A reliable, observable system gives engineering teams confidence to ship faster, sales teams confidence to promise more, and customers confidence to stay.

Conclusion

Poor observability is not just a technical gap, it is a growth tax that compounds as systems scale. The experiences of HiredScore, a fintech scale-up, and a SaaS provider show how blind spots lead to longer outages, regulatory fines, inflated costs, and slower delivery. They also show the upside of getting it right: faster detection, reduced costs, and the freedom to innovate.

The biggest cost of poor observability isn’t downtime—it’s the opportunity cost of innovation.

For scale-ups, the lesson is straightforward. Observability is not optional overhead, to be bolted on after product-market fit. It is a strategic enabler of resilience, trust, and growth. The sooner it is treated that way, the smoother the path to scaling becomes.