Engineering Guides

Practical engineering guides focused on logging, pipelines, architecture, scaling patterns, and troubleshooting. These pieces stand on their own, offering useful insights for any engineer, whether they use Grepr or not.

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Engineering Guides

The Observability Data Hoarder's Guide to Letting Go

Your Datadog bill keeps climbing because teams store more data to solve a retrieval problem, and that has never actually worked.
March 31, 2026
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Engineering Guides

APM Traces vs. Application Logs: What's the Difference and Why It Matters

Application logs capture developer-written context about business logic and internal state; APM traces automatically record request flow and performance across services, and understanding the difference explains why both inflate your observability bill.
March 26, 2026
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Engineering Guides

What Is App Logging and Why It's Making Your Observability Bill Explode

A breakdown of why app logging bills grow faster than your user base, and how to cut volume without losing observability coverage.
March 18, 2026
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Engineering Guides

Regain Control of Your Datadog Spend

Modern microservices applications generate petabytes of observability data monthly, and most of it is noise Datadog still charges you to store.
February 27, 2026
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Engineering Guides

How to Reduce New Relic Costs With Grepr: A Step-by-Step Setup Guide

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.
February 3, 2026
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Engineering Guides

Privacy and Data Ownership in Observability Pipelines

Grepr lets you keep your raw log data in your own S3 bucket while still getting the benefits of a managed observability platform.
January 28, 2026
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Engineering Guides

You're Paying for Data You'll Never Use

The logging paradox forces organizations to index everything at massive cost because they cannot predict which fraction of data a future incident will require.
January 22, 2026
Engineering Guides

5 Signs Your Observability Pipeline is Costing You Too Much

Most observability overspending comes from paying premium prices to store logs nobody queries.
January 9, 2026
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Engineering Guides

The Hidden Cost Crisis in Observability: What Your Team Needs to Know in 2026

Observability spending hit $28.5 billion in 2025, and 96% of organizations are now actively working to bring costs under control.
January 8, 2026
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Engineering Guides

Why First Mile Log Processing Reduces Costs Before Ingestion

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.
January 2, 2026
Grepr vs. Mezmo FAQs Q: Does Mezmo replace my existing observability platform? A: It sits alongside it, which is part of the friction. Mezmo provides its own AI chat interface for querying observability data, but your existing platform (Datadog, New Relic, Splunk, etc.) stays in place too. Engineers end up with two places to look for answers, and reconciling those takes time to sort out in practice. Q: How much data volume reduction can Mezmo deliver? A: Up to 50%, with ongoing manual configuration of pipeline filters. That number depends on how much time your team invests in building and maintaining those rules. As services change, so does the maintenance burden. Q: Will Grepr disrupt how my engineers currently work? A: No changes to existing workflows are required. Grepr reconfigures the existing agents to route through it, then handles everything automatically. Engineers keep using the same dashboards, the same alerting rules, and the same query syntax they already know. Q: What's the difference between Mezmo's pipeline and Grepr's pipeline? A: Mezmo's pipeline configuration is manual end-to-end: sources, sinks, filters, all of it. A misconfiguration can actually increase your data volume. Grepr sets up the source, sink, and data store once, then the AI continuously manages a working set of semantic pattern filters on its own, typically around 200,000 rules for high-volume environments. Q: How does backfill work in Grepr compared to Mezmo? A: In Mezmo, a rehydration job is submitted manually through the web dashboard, and it pulls everything from the selected time window whether you need it or not. Grepr lets you query retained data using Datadog, New Relic, or Splunk syntax, validate it, and submit a targeted backfill. More commonly, the backfill fires automatically when an observability alert triggers a webhook.
Engineering Guides

Remove Sensitive Data From Your Logs With the SQL Transform

Grepr's SQL transform enables real-time redaction of sensitive data like passwords from log events before they reach your data lake or monitoring platform, using familiar SQL syntax within your log processing pipeline.
December 29, 2025
Abstract technology visualization showing an observability pipeline with three stages: collection sources for logs, metrics, and traces in violet, blue, and teal converging from the left, flowing through a central hexagonal transformation processor with internal routing nodes, then splitting into two output paths with sparkle markers indicating high-value data routing to analysis tools in green above and box markers indicating long-term data routing to layered storage in blue below
Engineering Guides

What Is an Observability Pipeline (and Why It Matters More Than Ever)

Modern observability generates too much telemetry data and too little insight, and Grepr solves this by providing an intelligent observability platform that automates data processing, routing, and storage to cut costs by over 90% while preserving full visibility.
October 27, 2025

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