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.
Observability was never reliability - it was a proxy that's finally broken under the weight of non-linear complexity, runaway telemetry costs, and a fundamental inability to move from seeing problems to stopping them.
Modern microservices applications generate petabytes of observability data monthly, and most of it is noise Datadog still charges you to store.
Observability was supposed to help teams control complexity in the cloud era. For many organizations, it has become one of the fastest-growing line items in the budget.
The observability industry keeps building smarter tools on top of the same noisy data, and a recent post from a Sr. Engineering Manager at Walmart shows exactly why that approach hits a wall.
The same discipline required to right-size AI compute infrastructure applies directly to observability, where defaulting to ingest everything drives massive costs for data that never gets queried.
Grepr reduces New Relic costs by applying ML-based log reduction upstream of ingest, summarizing high-volume patterns while preserving unique events, anomalies, and any logs referenced by your existing dashboards and alerts.
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