Grepr is an intelligent observability pipeline that optimizes, analyzes, and routes data in real time, sitting between your agents and observability platform. Utilizing machine learning and a rules engine, it efficiently detects data patterns, filters out repetitive information, and forwards only essential summaries or unique messages. This seamless integration helps organizations significantly cut observability costs by up to 90%, enable long-term data retention, and make valuable insights available for business reporting and AI, all with minimal configuration changes.

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.

With apologies to George Orwell. Not all Observability data is salient all the time, some data is required all the time but most data is only germane when investigating an issue.