How to Cut Your Splunk Bill by 60-80% Without Losing Data

SIEM licensing costs scale directly with data volume. As organizations collect more logs for security and compliance, Splunk bills grow proportionally. Many enterprises now spend millions annually on SIEM licensing alone.

The math is straightforward but painful. A 1 TB/day Splunk deployment costs approximately $150,000-200,000 annually. At 5 TB/day, costs exceed $500,000. These numbers assume standard enterprise pricing without premium add-ons.

The Volume Problem

Most log data is repetitive. Firewall denies, health checks, authentication attempts, and status messages generate thousands of identical or near-identical events per minute. Each duplicate event counts against SIEM ingestion limits.

Consider a typical enterprise environment:

In this scenario, 830 GB of daily ingestion provides no additional security value. Organizations pay full price for redundant data.

Pre-Processing Architecture

LogZilla deploys between log sources and the SIEM. All events flow through LogZilla first, where deduplication, filtering, and enrichment occur before forwarding to downstream systems.

text
Wrap: OnCopyFullscreen
[Log Sources] → [LogZilla] → [Splunk/SIEM]
                    ↓
              [Long-term Storage]
              [Real-time Alerting]
              [AI Analysis]

This architecture provides several advantages:

• Immediate forwarding: First occurrence of each event forwards instantly
• Accurate counts: Duplicate events tracked with precise occurrence counts
• Full retention: All events stored in LogZilla for compliance and forensics
• Selective forwarding: Rules determine what reaches the SIEM

Deduplication Technology

LogZilla uses patented deduplication technology that identifies duplicate events in real-time. The system maintains configurable hold windows during which identical events consolidate into single records with occurrence counts.

Key capabilities:

• Field-based matching: Define which fields determine uniqueness
• Time windows: Configure consolidation periods per source type
• Threshold alerts: Trigger on occurrence counts, not individual events
• Pattern recognition: Identify near-duplicates with minor variations

How Field-Based Matching Works

Traditional deduplication requires exact string matches. LogZilla takes a smarter approach by allowing administrators to define which fields determine uniqueness.

Consider a firewall deny log:

text
Wrap: OnCopyFullscreen
Dec 7 14:32:01 fw-01 deny src=192.168.1.100 dst=10.0.0.5 port=443 count=1
Dec 7 14:32:02 fw-01 deny src=192.168.1.100 dst=10.0.0.5 port=443 count=1
Dec 7 14:32:03 fw-01 deny src=192.168.1.100 dst=10.0.0.5 port=443 count=1

These three events differ only in timestamp. With field-based matching configured to ignore timestamp, LogZilla consolidates them into a single event with count=3. The SIEM receives one event instead of three, reducing ingestion by 67% for this source.

Administrators configure matching rules per source type:

text
Wrap: OnCopyFullscreen
Rule: firewall-deny-dedup
  Match Fields: src, dst, port, action
  Ignore Fields: timestamp, sequence_number
  Hold Window: 60 seconds
  Forward: first occurrence immediately

Configuring Hold Windows

Hold windows determine how long LogZilla waits before finalizing a deduplicated event. Shorter windows provide faster forwarding but less consolidation. Longer windows maximize deduplication but delay final counts.

Recommended hold windows by source type:

Near-Duplicate Detection

Some events are nearly identical but contain minor variations that prevent exact matching. LogZilla's pattern recognition identifies these near-duplicates:

• Sequence numbers: Incrementing counters that differ per event
• Session IDs: Unique identifiers for the same logical session
• Minor timestamp variations: Millisecond differences in timestamps
• Formatting differences: Same data with different field ordering

Pattern rules extract the meaningful content and ignore noise fields, enabling deduplication across events that would otherwise appear unique.

Real Cost Scenarios

Scenario 1: Mid-Size Enterprise (1 TB/day)

Scenario 2: Large Enterprise (5 TB/day)

Scenario 3: Enterprise with Event Storms

Organizations experiencing regular event storms see even greater savings. Network outages, security incidents, and infrastructure failures generate massive duplicate volumes. LogZilla consolidates these events while maintaining alerting capability.

One LogZilla customer eliminated 4,000 false positive tickets per week by consolidating duplicate alerts into single actionable notifications.

Understanding Event Storm Economics

Event storms occur during infrastructure incidents, security events, or misconfigurations. A single network outage can generate millions of duplicate events in minutes as every affected device reports the same problem repeatedly.

Example: Core Router Failure

When a core router fails, downstream devices generate alerts:

At 500 bytes per event, this 30-minute storm generates 366 MB of log data. Most of these events report the same root cause: the core router failure. Without deduplication, Splunk ingests 732,000 events. With LogZilla deduplication, Splunk receives perhaps 1,000 unique events with accurate occurrence counts.

Monthly Impact

Organizations experiencing two event storms per month see significant cost differences:

The savings from event storm handling alone often justify LogZilla deployment.

Filtering Strategies Beyond Deduplication

Deduplication addresses duplicate events. Filtering addresses events that provide no security or operational value regardless of uniqueness.

Events Safe to Filter

Some log sources generate events that never contribute to security investigations or operational troubleshooting:

• Successful health checks: "Service X is healthy" repeated every 30 seconds
• Routine scheduled tasks: Cron job completions with no errors
• Debug-level application logs: Verbose output useful only during development
• Informational network events: Interface statistics polled every minute

Filtering Configuration

LogZilla filtering rules specify which events to drop, forward, or store locally:

text
Wrap: OnCopyFullscreen
Rule: drop-health-checks
  Match: message contains "health check passed"
  Action: drop
  
Rule: local-only-debug
  Match: severity = debug
  Action: store locally, do not forward
  
Rule: forward-security
  Match: facility = security OR severity <= warning
  Action: forward to Splunk immediately

Calculating Filter Impact

Audit current log sources to identify filtering candidates:

Combined with deduplication, filtering can reduce SIEM ingestion by 80-90%.

Implementation Approach

Phase 1: Assessment (Week 1)

1. Inventory current log sources and daily volumes
2. Identify high-volume, repetitive sources
3. Calculate current SIEM costs per source type
4. Establish baseline metrics for comparison

Phase 2: Pilot Deployment (Weeks 2-3)

1. Deploy LogZilla in parallel with existing infrastructure
2. Configure deduplication rules for top 3 volume sources
3. Measure reduction rates and validate data integrity
4. Verify alerting and search functionality

Phase 3: Production Rollout (Weeks 4-6)

1. Expand deduplication to all applicable sources
2. Configure selective forwarding rules
3. Implement threshold-based alerting
4. Validate compliance requirements

Phase 4: Optimization (Ongoing)

1. Tune hold windows based on operational patterns
2. Add new sources as infrastructure grows
3. Review forwarding rules quarterly
4. Track cost savings against baseline

Maintaining Security Visibility

Cost reduction cannot compromise security. LogZilla ensures full visibility through several mechanisms:

• First-event forwarding: Initial occurrence reaches SIEM immediately
• Threshold alerts: High occurrence counts trigger notifications
• Full searchability: All events searchable in LogZilla regardless of forwarding status
• Compliance retention: Meet regulatory requirements with LogZilla storage
• AI analysis: LogZilla AI Copilot analyzes all events, not just forwarded subset

Micro-FAQ

How does log deduplication reduce SIEM costs?

Log deduplication identifies and consolidates repeated events before they reach the SIEM. Since most SIEMs charge by ingested volume, reducing duplicate events directly lowers licensing costs.

Does deduplication lose important security data?

No. LogZilla forwards the first occurrence immediately and maintains accurate occurrence counts for duplicates. Full event history remains searchable in LogZilla.

Can LogZilla work alongside existing Splunk deployments?

Yes. LogZilla deploys in front of Splunk as a pre-processor, filtering and deduplicating events before forwarding to Splunk. No changes to existing Splunk configurations required.

What types of logs benefit most from deduplication?

High-volume, repetitive sources like firewall denies, health checks, authentication logs, and network device status messages typically show 80-95% reduction rates.

Next Steps

Organizations can reduce SIEM costs by 60-80% without sacrificing security visibility. The key is pre-processing logs before SIEM ingestion, eliminating redundant data while preserving full event history for compliance and forensics.

Download SIEM Offload Economics (PDF)

Watch AI-powered log analysis demos to see how LogZilla adds AI capability while reducing SIEM costs.