Skip to main content

​
The Economics of Reliability

In a digital-first economy, reliability is not just an operational concern—it is a financial one. WHAWIT transforms observability from a passive cost center into a strategic asset that protects revenue and recovers engineering capacity. Based on the research whitepaper Intelligent Observability and Autonomous Reliability Engineering, here is the economic case for WHAWIT.

​
1. The Real Cost of Downtime

The cost of downtime has escalated dramatically.
  • $9,000 per minute: The average cost of downtime across industries is now estimated at roughly $540,000 per hour.
  • $1 Million+ per hour: For 45% of enterprises, high-impact outages exceed $1 million per hour in lost revenue and damages.
  • The MTTR Multiplier: Every minute added to Mean Time To Recovery (MTTR) directly multiplies these losses.
WHAWIT Impact: By automating incident analysis and surfacing root causes in minutes, WHAWIT targets a 30-50% reduction in MTTR. For a company facing even a few major incidents a year, this translates to millions of dollars in protected revenue.

​
2. Recovering “Lost” Engineering Time

Engineering teams are expensive, yet organizations routinely waste their capacity on low-value toil.
  • 20-30% Wasted Time: Studies consistently show that engineers spend 20-30% of their time on unplanned work—triage, manual debugging, and “hunting” through logs.
  • The FTE Drain: In a team of 20 engineers, this “tax” is equivalent to losing 5 full-time engineers to non-productive work.
WHAWIT Impact: WHAWIT automates the heavy lifting of diagnosis. By reducing the cognitive load and manual effort of incident response, it effectively returns 1.5 to 2 FTEs of productivity back to your roadmap from that same 20-person team.

​
3. Escaping the Telemetry Cost Trap

Organizations are drowning in data but starving for insight.
  • Data Explosion: Log volumes are growing at 250% year-over-year.
  • Low Signal-to-Noise: Over half of collected logs are never looked at, yet they incur storage and ingestion costs.
  • The Truncation Risk: To manage costs, 98% of companies cap or filter data, often blindly, creating blind spots that risk missing critical outages.
WHAWIT Impact: WHAWIT sits on top of your existing stack. It extracts higher value from the data you already pay for, turning raw telemetry into actionable intelligence without requiring you to store even more data. It allows you to maximize the yield of your current observability investment.

​
4. Compounding Returns through Autonomous Improvement

Traditional tools stop when the fire is out. WHAWIT continues working to prevent the next one.
  • The Feedback Loop: WHAWIT links incidents back to code and configuration, suggesting specific fixes and patches.
  • Asset Appreciation: Instead of just fixing a break, WHAWIT hardens the codebase. Over time, this lowers the frequency of future incidents.
Summary: WHAWIT pays for itself through three clear levers:
  1. Direct Revenue Protection (Lower MTTR).
  2. Operational Efficiency (Recovered Engineering Hours).
  3. Asset Optimization (Better yield from existing tools and self-healing code).