Observability Accelerator

Your Elasticsearch Bill Doesn't Have to Keep Growing

Log Reduction Engine cuts storage costs 50-70% by keeping high-value logs at full fidelity and eliminating repetitive noise at ingestion. Zero observability gaps. Documented ROI before you start.

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verified 67% Storage Reduction — Documented engineering 60+ Elasticsearch Implementations emoji_events Elastic Innovation Award 2023
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The Storage Cost Problem Nobody Budgeted For

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Storage Costs Grow 30-50% Per Quarter

As services scale, Elasticsearch log volume grows unbounded. Every new microservice, every additional environment adds terabytes your budget didn't account for.

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Blind Deletion Breaks Observability

When costs force action, the default response is blunt: delete old data, reduce retention windows. You save on storage and lose the evidence you need when production breaks.

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Manual Retention Policies Miss What Matters

Manual ILM policies can't classify log events by diagnostic value, can't detect anomalies worth preserving, and can't project savings before implementation.

How Log Reduction Engine Works

1

Analyze

Scans your existing log streams and classifies every event by diagnostic value — anomaly-bearing, diagnostic, repetitive, or informational.

2

Configure

Sampling rules set per log type. High-value events kept at full fidelity. Repetitive success events sampled or aggregated. You control the rules.

3

Forecast

The cost forecasting engine projects your post-implementation storage volume and cost savings using your actual log data.

4

Deploy

Ingest pipeline processors deploy into your existing Elasticsearch pipeline. Log reduction runs automatically. A live dashboard shows savings in real time.

Horizontal data flow diagram showing raw log events entering a geometric classification layer, where high-value events pass through at full fidelity while repetitive events are sampled, resulting in a reduced output stream.

Raw logs in → ML classification → intelligent sampling → reduced storage, full observability

See Your Savings Before You Commit

The cost forecasting engine analyzes your current Elasticsearch log streams — volume, composition, redundancy patterns — and projects your post-reduction storage footprint. No estimates. No benchmarks from other companies. Your data, your projected savings, before implementation begins.

Projected savings calculated from YOUR data — not vendor estimates.
Get Your Storage Cost Estimate

Six Capabilities That Cut Storage Without Cutting Visibility

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Intelligent Log Sampling

Keeps anomalies, errors, and slow operations at full fidelity. Samples repetitive success events that consume storage without adding diagnostic value.

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Cardinality Reduction

Collapses high-cardinality field values — dynamic URLs, session IDs, request tokens — that inflate index size without adding insight.

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Cost Forecasting

Pre-implementation analysis projects storage savings with specificity. See your expected ROI before any changes are deployed.

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Per-Log-Type Retention

Different retention rules for different log types. Error logs retained longer. Debug logs sampled aggressively. Granular control replaces blunt retention.

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Anomaly Preservation Guarantee

ML-based anomaly detection ensures anomalous events are never sampled out. Incident investigation capability stays intact by design.

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Live Cost Dashboard

Real-time Kibana dashboard shows storage consumption before and after, cost savings per day, and sampling efficiency metrics.

Documented Results From Production Deployments

2.4TB → 800GB
67% storage reduction — documented in production
$18K → $6K/mo
Monthly Elasticsearch storage cost reduction
Unchanged
Observability coverage — all anomaly and error events preserved
Implementation cost recovered in first operational period.

Before Log Reduction Engine

2.4TB of logs. $18K/month storage cost. 70% repetitive success events consuming storage. Cost trajectory unsustainable.

After Log Reduction Engine

800GB of high-value data. $6K/month storage cost. Error logs, anomalies, and slow operations at full fidelity. Same alert coverage, documented savings.

Where Log Reduction Engine Fits

Log Reduction Engine is a primary deliverable in Observability Modernization Sprint engagements alongside Topology Builder and Alarm Noise Suppression. Available standalone with custom implementation and dedicated cost forecasting analysis.

Linked Accelerators
hub Topology Builder radar Blast Radius notifications_off Alarm Noise Suppression
Linked Engagements
Observability Modernization Sprint Elastic Health Check + 2-Week PoC Managed Services

15+ Production Deployments. 50-70% Storage Reduction. Documented.

Gaming: 67% FinTech: 52% SaaS: 61%
“Reduced Elasticsearch storage from 2.4TB to 800GB. Monthly costs dropped from $18K to $6K.”
— VP Engineering, Gaming company
Explore 15+ Observability Case Studies →

Common Questions

No. The anomaly preservation guarantee ensures anomalous and error events are never sampled. Only repetitive success events are reduced. Engineering teams report no change in incident investigation capability after implementation.
Log Reduction Engine analyzes your current log stream patterns — volume, composition, redundancy rates — and projects post-sampling storage volume. You see expected savings before any changes are made to your cluster.
Yes. Log Reduction Engine layers on top of existing ingest pipelines via Elasticsearch processor plugins. No architectural changes required. Configurable per log type and per index.
Debug logs, verbose success logs, and routine health check events are primary sampling targets. Error logs, slow operation logs, anomaly events, and security events are always kept at full fidelity.
Available both ways. Included in Observability and cost optimization engagements as a standard deliverable. Available standalone with custom implementation and dedicated cost forecasting analysis.

Stop Paying for Logs Nobody Reads

Log Reduction Engine shows you the savings before you commit. Documented 50-70% storage cost reduction across 15+ production deployments.

Schedule 15-Minute Demo Get Storage Cost Estimate
View Observability Case Studies →
schedule 24-Hour Response SLA. Demo scheduled within 72 hours.
Schedule 15-Minute Demo