Grepr Blog

Live View clones your production pipeline so you can test configuration changes against real data streams without any deployment risk.

Grepr was recognized by Gartner as a Cool Vendor in AI for IT Operations for its ability to give AI driven systems cleaner signal, lower cost, and real-time pattern detection that powers advanced LLM workflows.

Grepr cuts Grafana Cloud log costs by up to ninety percent through a simple configuration change that redirects your existing shippers to use semantic machine learning for automatic pattern aggregation while preserving all raw data in low cost storage.

Grok lets teams fill the gaps when automatic field detection misses something, giving Grepr a simple way to extract key details from unstructured logs and keep data accurate.

Grepr pipeline exceptions let engineers surface full raw logs on demand during incidents while keeping everyday log reduction in place.

Grepr’s Signature Sampling brings high-fidelity observability to modern systems by capturing every unique execution path without the noise or cost of traditional APM.

FOSSA cut Datadog observability costs by over 90% with Grepr, reducing log volume instantly while keeping full visibility, no workflow changes, and zero data loss.

Modern observability generates too much telemetry data and too little insight, and Grepr solves this by providing an intelligent observability platform that automates data processing, routing, and storage to cut costs by over 90% while preserving full visibility.

Grepr enables financial institutions to stay compliant with DORA by maintaining full log visibility and audit readiness at a fraction of traditional costs.

Jitsu used Grepr to cut Datadog log volume and costs by over 90 percent while keeping complete visibility, fast troubleshooting, and 13-month log retention.

Grepr integrates with Cloudflare to reduce log volume and costs while retaining complete visibility into traffic, performance, and security insights.

Grepr helps teams gain clear visibility into Kubernetes audit logs by filtering noise, detecting anomalies, and keeping observability costs low.