How Grepr uses continuous pattern extraction and AI to detect anomalies and build monitoring strategies to prevent failures.
Every AI conversation at the summit dragged a cost conversation behind it, and most teams already knew which one was winning.
Grepr's free tier is live: perpetual, no credit card, 4 TB of logs and 8 TB of traces a month to run on your own data.
Grepr's Monitored Objects approach groups metrics by entity, aggregates normal behavior, and only sends detailed data to Datadog when anomalies occur, dramatically reducing billable custom metrics and hosts without sacrificing troubleshooting capability.
Three advanced techniques for reducing log volumes by 90% or more, including automatic pattern sampling, logarithmic sampling, and sampling with automatic backfilling, each designed to scale for enterprise environments without sacrificing critical troubleshooting data.
Four foundational techniques for reducing log volumes, including severity thresholds, log-to-metrics conversion, uniform sampling, and drop rules, along with the tradeoffs engineering teams should weigh before implementing each one.
No engineer has ever been excited about configuring tools to drop metrics and logs just to stay within budget. But that's exactly what observability has become for most teams: an expensive, frustrating compromise between cost and visibility. Grepr was built to end that tradeoff.
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