Modern systems produce too much data to monitor effectively. Traditional observability tools collect everything first, analyze later, and overwhelm engineers with noise, delays, and unnecessary cost. Detection should be fast, adaptive, and meaningful — not reactive and expensive.
Endless log volume hides the few signals that actually matter.
Detection lags behind production — teams react after impact.
Costs scale faster than insight, straining observability budgets.
Dstl8 distills repetitive, redundant logs at the edge to only the essential information needed to solve problems. Powered by Möbius AI, Dstl8 summarizes patterns, normalizes formats, and spots high-value signals for immediate visibility. Correlates what’s happening across services and workloads. Detects emerging patterns and links related anomalies to surface early warning signs that single-system tools miss. Learns from each event. Adjusts sensitivity and alerting based on past patterns, refining what “normal” looks like for every service and environment.
Distills raw telemetry at the edge to reduce noise and surface early signals.
Correlates patterns across services to spot emerging issues in context.
Continuously monitors and learns from to refine alerts and reduce false positives.
Detect emerging issues before they hit production. Cut false positives with inference and adaptive learning. Reduce log volume and alert fatigue simultaneously. Help developers and SREs surface problems faster to accelerate troubleshooting and fixing.
