Grepr Blog

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’s Live Edit lets you test and validate pipeline changes safely in a temporary clone, so you can fine-tune processing without affecting live data.

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.

FOSSA cut Datadog logging costs by 95% with Grepr, keeping full visibility and dashboards intact while scaling their software supply chain platform.

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.

Grepr is building the foundation for AI-powered monitoring that understands context, reduces noise, and helps engineers catch issues before they escalate.

Grepr SQL Operators let you reshape live log data into real time insights using simple SQL, giving you full control over metrics, traces, and alerts.