Product

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.

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.

Grepr connects seamlessly with Splunk, using machine learning to cut log volume and costs by 90 percent while preserving full data access and visibility.

Structured logging turns unstructured text logs into clear, queryable data that improves visibility, collaboration, and operational efficiency.

Grepr uses AI and semantic analysis to reduce observability data by 90% while retaining complete visibility for faster, more cost-efficient incident response.

Grepr automates backfill with webhooks and rule-based triggers, restoring full log data on demand for faster, more efficient incident investigation.

Grepr’s Intelligent Observability Engine uses pipelines, clustering, and adaptive sampling to process and reduce log data by up to 90% while preserving full visibility.

Grepr reduces observability costs by up to 98% through intelligent data summarisation while preserving complete access to all logs when needed.

Grepr uses machine learning to cut log volume by 90% while keeping every log searchable and recoverable, giving teams lower costs and full control.