Grepr Blog

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.

Grepr connects directly with Datadog to reduce log volume and costs by up to 90 percent while keeping every log accessible for analysis and compliance.

Grepr decouples data collection from observability platforms, cutting costs and eliminating vendor lock-in while retaining complete visibility and control.

Grepr uses unsupervised machine learning to reduce log volume by over 90% while preserving important data through smart, configurable aggregation. It passes low-frequency messages through unmodified, allows engineers to retain specific parameters like user IDs, and supports backfilling logs via API triggers when deeper detail is needed—such as during support tickets. For added flexibility, trace sampling can capture full logs for a subset of users, and all original logs are archived in a searchable data lake. This gives teams control, reduces noise, and enables cost-effective observability without sacrificing access to critical information.