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.
Goldsky's logging costs had outpaced their value. After a four-week rollout, Grepr reduced their indexed logs from 5.7 billion to 250 million messages, cutting Datadog spend by 96% while keeping their observability workflows intact.
Grepr uses AI to automate observability data filtering with 90% less manual configuration than Cribl's powerful but complex platform that requires dedicated teams and custom query language expertise.
Grepr uses machine learning to filter observability noise at ingest, storing all raw data in low-cost storage while forwarding only meaningful signals to your existing tools like Datadog, Splunk, and New Relic.
Grepr is an intelligent observability engine that uses machine learning and a rules engine to detect patterns, suppress noise, and forward only essential data to your existing platforms, cutting costs by up to 90% with a single configuration change.
Grepr automatically parses your existing Datadog alerts and routes the logs that power them through unchanged, letting you cut observability costs in production without rewriting a single alert.
Grepr receives logs from Splunk Heavy Forwarders via S2S, compresses data by 90% while retaining everything in a queryable data lake with SPL support and one-click backfill to Splunk when needed.
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