At the startup level, reliability remains vital, yet it does not determine your organization’s identity. Users forgive hiccups. Engineers wear multiple hats. The developer responsible for writing code is usually the one who will handle its maintenance when problems occur.
The entire business operation transforms when your company receives funding and its user base expands and enters new markets. The monitoring systems that used to be satisfactory now create critical risks for your operations.
During the initial development phase of startups the main objective remains basic: achieving operational functionality. Every aspect of a company transforms when its user base expands from thousands to millions. The difference between 99.99% and 99.999% uptime appears trivial on paper, but proves enormous in actual practice.
The extra decimal point represents a completely new set of challenges that require different approaches to solve. A system that operates at four-nines availability (99.99%) can experience annual downtime for about 52 minutes. The difference between 99.999% (five-nines) and 99.99% (four-nines) uptime becomes apparent as five-nines allow only 5 minutes and 15 seconds of downtime.
Your systems, together with your teams, along with your organizational mindset all need transformation during this phase. My experience with building fault-tolerant systems during multiple hypergrowth phases has shown me that better dashboards are not the key to success. Your entire architecture requires a change in perspective.
I’ve seen this shift up close — moving from a team where every engineer could walk through the entire request lifecycle in their head, to one where no single person understood more than a few services. At one point, we had over 300 services deployed across multiple environments, some owned by different business units and others by external vendors. Debugging a production incident meant jumping across 5+ dashboards, trying to correlate request IDs through logs, metrics, and whatever tracing we had time to implement. We had one critical outage where an upstream schema change in a third-party library silently broke JSON serialization in one service.
The actual root cause? A Go struct tag mismatch. No alerts fired because metrics didn’t spike, and logs only hinted at the issue after searching millions of lines. It was tracing that exposed the failing path — not because we were disciplined about it, but because one service happened to propagate a trace ID. That trace saved us hours of guesswork. Afterward, tracing wasn’t optional — it became policy.
You can’t debug your way out of a scaled system. You have to design for system-wide understanding — across teams, languages, and runtime environments. Observability tooling like dependency graphs, live service maps, and high-cardinality context propagation becomes essential. Not for the sake of beauty, but because production is now a black box — and you’re blind without them.
Early development teams possess complete knowledge about their codebase. A developer can usually identify the source of the problem through self-examination. Systems at unicorn scale have such complexity that one person cannot understand their entire operation. The infrastructure includes multiple hundred services, which separate teams or different companies developed.
Incidents happen to systems that you did not personally build. The challenge involves tracing down issues in complex networks of dependent systems and external libraries and API connections. Traditional logs and metrics aren’t enough. To reason about relationships instead of errors, you must implement tracing along with dependency maps and alert systems.
Take Netflix as an example. The massive user base exceeding 230 million subscribers worldwide means brief system downtime will lead to monetary losses and user dissatisfaction. The observability platform Edgar emerged from Netflix development because the company needed visualization tools to show service interactions and real-time system data flows. Engineers need tools to view system operations beyond familiar parts of the system.
The level of incident response shifts from bug fixing to team coordination for understanding during this phase. Service owners must define clear boundaries through tagging, while meaningful health checks and actual runbooks that mirror production behavior need implementation. Organizations must perform investments in simulations and chaos engineering because they intentionally break systems in development environments to build readiness for actual breakdowns.
Complexity shows no linear growth pattern when systems increase in scale. Adding ten services results in hundreds of additional system failure points. System interactions form the spaces where failures commonly reside. System failures remain inactive until infrequent traffic patterns and unusual edge cases activate them.
At scale, failures rarely show up as clear exceptions. They surface as latency, partial degradation, or worse — complete silence. I’ve seen systems return 200 OK responses while core business logic quietly failed due to race conditions triggered only during regional failovers. Health checks stayed green, but users couldn’t complete transactions. That’s when chaos engineering became critical.
We began injecting failures into staging – simulating latency, packet loss, and network partitions with tools like Istio Fault Injection – to uncover issues that only appeared under real-world conditions. One of the hardest bugs we found surfaced only when gRPC retries compounded under load, overwhelming downstream services.
AI played a key role in catching what humans couldn’t. Our observability platform continuously analyzed telemetry from logs, traces, metrics, and deployment events, correlating patterns and surfacing anomalies that traditional monitoring would miss. It helped detect early signs of failure, traced their impact across services, and flagged unexpected behavior even when everything looked healthy on the surface.
This kind of intelligence is vital in systems that process critical workloads, such as financial transactions, where even a 60-second outage can mean millions lost. At unicorn scale, uptime isn’t a metric, it’s a business guarantee. And catching silent failures before they cascade requires both simulation and AI working together.
The cultural aspect of observability remains unaddressed despite being more significant than its technical aspects. Your tooling requires team trust and active usage to function effectively at large scale. Developers need to properly instrument their code while providing accurate API documentation, and taking ownership responsibilities beyond code review stages. You cannot have teams tossing features over the wall.
Tools need appropriate processes to function properly. Most companies that operate at large scales select SLO-based alerting (service-level objectives) instead of raw error counts for their alerting systems. The system sends alerts only when actual availability targets come into risk of being violated, thus preventing unnecessary 3 a.m. wake-ups for minor user-impactless error spikes. Google introduced this approach in their Site Reliability Engineering handbook and the approach became standard for teams managing billion-dollar-stake systems.
The journey from startup to unicorn requires more than feature development. Organizations together with their systems need to survive under high pressure conditions. Five-nines represents more than a status symbol; it stands as a promise to users alongside commitments to both business operations and engineering staff. The path to reach this goal does not depend on dashboard improvements. The path to this goal depends on three fundamental elements which include observability and ownership together with understanding complex systems.
Reaching unicorn status requires more than increasing engineer numbers or server capacity. Your system requires all operational elements to maintain visibility while being easily understood and maintained. The dangerous bugs that cause the most problems exist between teams and tools when operations reach scale. Observability represents the only method to detect these issues before users become aware of them.
Bio
Oleksandr (Alex) Holovach is a software engineer and co-founder of Kubiks.ai, an observability platform that provides end-to-end visibility for Kubernetes and cloud-native systems. He previously served as a Programming Team Lead at airSlate Inc., where he built core integrations that helped drive the company to a $1B valuation, and has held senior engineering roles at companies including Google, TAG – The Aspen Group, and currently Prove Identity Inc. Alex specializes in observability, distributed systems, and high-scale backend infrastructure, with expertise in building developer tools that enhance productivity and reduce system downtime.
