Modern edge-to-cloud systems are composed of many independent software and hardware components working together in a distributed environment. While this architecture enables scale and flexibility, it also increases complexity. One of the most underestimated costs in these systems is the lack of observability into their internal state.
In several teams I’ve worked with, incidents didn’t begin with alarms and crashes. They began with uncertainty. Teams struggled to understand what the system was actually doing, leading to long debugging sessions, conflicting hypotheses, and growing pressure as deadlines slipped.
Why Logs Alone Aren’t Enough
Logs are timestamped text records that describe events in a system. They are widely used, easy to add, and often the first diagnostic tool developers reach for during incidents. Logging libraries exist for almost every programming language, and logs are effective at capturing errors and unexpected behavior.
However, as systems evolve from monolithic architectures to distributed edge-to-cloud microservice-based systems, logs quickly become insufficient on their own. Understanding a failure often requires manually correlating log entries across multiple components, devices and services. Valuable engineering time is spent debugging by searching and guessing where the problem actually resides.
To gain real observability in distributed edge-to-cloud systems, logs must be completed with metrics and traces.
Metrics provides quantitative data about a systems behavior and health over time — such as CPU usage, memory consumption, queue depth, or error rates.on a device. They allow teams to detect trends, spot degradation early, and trigger alerts when thresholds are crossed.
Traces captures the end-to-end path of a request as it moves from the edge, through the network, and into backend systems. They make it possible to understand latency, bottlenecks, and failure propagation across systems boundaries — insight that logs alone rarely provide.
Why Observability Must Be Designed Early
As software systems move into production and evolve through maintenance and customer-driven changes, the original intent with the system gradually erodes. Over time, complexity increases, assumptions change, and institutional knowledge fades.
When an incident occurs, teams are forced to reason about the system under pressure. In these situations, what is measured determines how the team reacts. If critical signals are missing, engineers are left to get their understanding of system behavior through partial information, assumptions and guesswork.
This guesswork has real consequences. Debugging slows down, decisions become reactive, stress increases, and delivery times slip. Entire teams are pulled into incident response, often for extended periods, while normal development comes to a halt.
Retrofitting understanding into a system during an incident is both costly and unreliable — especially when the people responding were not the ones who originally built the system.
Observability is significantly cheaper and more effective when designed into a system from the beginning. When the right signals are present early, teams are better prepared to respond when things go wrong.
Observability as Part of an Architecture Audit
When conducting IoT architecture audits at combotto.io, observability is a fundamental assessment dimension. We evaluate whether teams can answer critical operations questions before incidents occur — and whether the system provides the signals needed to reason about failures effectively.
In many cases, improving observability early prevents weeks of reactive fire-fighting later.