Engineering Guides

Filter rules force you to guess which data matters before you need it. Pattern-based sampling with full retention doesn't.

Kubernetes environments drown observability platforms in redundant log data, and Grepr uses semantic machine learning to reduce that volume by up to 90 percent while preserving every raw event in low-cost S3 storage.

Unstructured logs make parsing, searching, and alerting harder while driving up storage costs, so this guide covers the structured logging practices that matter most for production systems in 2026.

The ingestion bill is visible. The storage bill is the one that compounds.

Healthcheck logs generate millions of identical lines per day and silently inflate your observability bill, but filtering them at the right layer can cut total log volume by 15 to 40 percent.

Modern microservices applications generate petabytes of observability data monthly, and most of it is noise Datadog still charges you to store.

Grepr reduces New Relic costs by applying ML-based log reduction upstream of ingest, summarizing high-volume patterns while preserving unique events, anomalies, and any logs referenced by your existing dashboards and alerts.

Grepr lets you keep your raw log data in your own S3 bucket while still getting the benefits of a managed observability platform.

The logging paradox forces organizations to index everything at massive cost because they cannot predict which fraction of data a future incident will require.

Most observability overspending comes from paying premium prices to store logs nobody queries.

Observability spending hit $28.5 billion in 2025, and 96% of organizations are now actively working to bring costs under control.

First mile log processing with Grepr filters and routes logs before they reach expensive observability platforms, reducing costs by 90% while preserving 100% visibility by sending high-signal data to premium platforms and routing routine logs to low-cost storage.