This blog post shows how to reduce log volume by up to 90% by integrating New Relic with Grepr. Using a simple Docker-based microservices demo, we walk through configuring Fluent Bit to ship logs to New Relic, then show how easily Grepr can be inserted into the pipeline to intelligently filter out noise. The result is cleaner, more actionable log data, reduced observability spend, and no disruption to existing workflows. All raw data is retained in low-cost storage and can be backfilled on demand—helping teams stay in control of both their visibility and their budget.
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Use Grepr to Avoid Observability Vendor Lock-In
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.

Product
Aggregate my log volume by 90%, yet still find anything I need? How is that possible?
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.

Product
All Observability Data Is Equal But Some Is More Equal Than Others
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.