""A central issue here is the fact that, as systems scale, telemetry scales even faster," explained Azulay. "Every service creates metrics. Every request generates traces.... and logs multiply as the velocity of deployment increases. This is the structural reality of distributed systems." He points to research from Omdia that suggests organisations consistently "under-instrument" their environments, not because they lack the tools to do so, but because they can't afford to fully use them."
"Among the top challenges cited: 32% report cost inefficiencies tied to volume-based pricing. 29% cite high data storage costs for logs, metrics and traces. 28% struggle with observability costs rising faster than infrastructure itself. Sampling, simples? "In practice, this means teams buy sophisticated platforms, then deliberately starve them of the data they need to deliver value. Thus, sampling, filtering and selective l"
Modern observability has delivered measurable value through metrics, logs and traces for cloud-native and always-on systems. The economics of legacy observability platforms no longer scale with distributed systems because telemetry volume increases faster than services. Engineering teams are adopting more tools and vendors, but many under-instrument their environments because full instrumentation is unaffordable. Omdia research shows 32% report volume-based pricing inefficiencies, 29% cite high data storage costs, and 28% say observability costs rise faster than infrastructure. Teams therefore resort to sampling, filtering and selective retention that reduce platform effectiveness.
Read at Techzine Global
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