Product Features

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.

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

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.

Grepr helps teams keep full visibility while reducing observability data volume and costs through intelligent summarization and instant backfill.

Vector and Grepr both route observability data between sources and sinks, but they take fundamentally different approaches. Vector offers extensive manual configuration options, while Grepr uses machine learning to automatically optimize your data pipeline and cut costs by 90%.

Grepr reduces Datadog browser logging costs by 90% by receiving all frontend logs, storing them cost-effectively, and using AI to filter repetitive data while maintaining full query access to original logs through a Datadog-compatible dashboard.

This tutorial demonstrates reducing log volume by 90% by adding Grepr between Fluent Bit and New Relic to filter noise while retaining raw data in low-cost storage for on-demand backfilling.

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.