{
  "id": 9551348,
  "title": "The Observability Tax: When Monitoring Costs Exceed Downtime Costs",
  "url": "https://urgent.news/2026/09/24/the-observability-tax-when-monitoring-costs-exceed-downtime-costs",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-24T11:41:23.000Z",
  "source": {
    "name": "DevOps.com",
    "slug": "devops-com",
    "url": "https://devops.com/the-observability-tax-when-monitoring-costs-exceed-downtime-costs/"
  },
  "original_language": "en",
  "account": "In recent times, a curious phenomenon has emerged within the observability market. Teams, striving to minimize downtime costs, have inadvertently transformed their observability solutions into one of the costliest components of their infrastructure budget. Discussions with various teams reveal that some spend between six and seven figures annually on their observability stack, surpassing the estimated financial impact of potential downtime. This paradox raises questions about the efficacy of current observability strategies.\n\nThe issue stems from well-meaning intentions. Teams begin by instrumenting their services with metrics, traces, and logs. This data proves to be valuable and often prompts teams to collect more. They introduce custom dashboards, enable distributed tracing across every service boundary, and configure high-resolution metrics, anticipating a need for such granularity during incidents. Each decision appears justified individually. However, when combined, these actions create a vast telemetry firehose, necessitating significant resources for ingestion, storage, and querying. Much of this data remains unexamined, leading to substantial financial waste.\n\nObservability vendors do not encourage this behavior; instead, they profit from increased data volume. Consequently, the products they offer incentivize teams to instrument everything, retain all data, and correlate all telemetry. Marketing promises \"full observability,\" while invoices reveal exorbitant charges for unutilized logs, traces, and metrics. The solution lies in treating observability as any other engineering investment, employing a cost-benefit analysis. Teams should first determine what they actually examine during incidents, rather than what they could theoretically observe. In most cases, this constitutes a small fraction of the collected data. Subsequently, teams should work backward, identifying which telemetry directly supports incident detection, diagnosis, and resolution. Maintaining high resolution for this critical data while implementing sampling, aggregation, or reduced retention for the rest can optimize observability costs.\n\nThe OpenTelemetry framework aids by decoupling instrumentation from specific vendors, yet it does not address the root cause of over-collection. This requires engineering discipline and a recognition that \"less data, better curated\" is a more mature observability approach than indiscriminately collecting vast amounts of data. Ultimately, an observability stack should expedite incident resolution, not render it more expensive than the downtime it aims to prevent.",
  "summary": "Observability costs can spiral when teams collect more telemetry than they actually use. A more mature approach prioritizes the data that directly improves incident detection, diagnosis and recovery.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}