Reducing Your Splunk Bill: How to Cut Costs in Half Without Sacrificing Security Visibility

The era of paying for data waste is over. While most organizations accept spiraling Splunk costs as the price of security visibility, savvy security teams know that intelligent data optimization doesn't just cut costs—it improves detection.

Organizations generate massive data volumes without budget flexibility to match, creating a false trade-off between visibility and cost. In reality, the problem isn't data volume—it's data intelligence. Companies that continue feeding everything into their SIEM are living in a world that's quickly becoming obsolete.

The solution requires fundamentally changing when and how data is processed—moving from reactive data analysis to proactive data intelligence, processing and enriching telemetry before it ever reaches expensive storage systems.

Most Splunk environments are drowning in data that adds cost without adding insight.

Legacy thinking treats all data as equally valuable, but the reality is starkly different.

Duplicate Events and Payload Bloat: Systems generate multiple logs for identical events while including dozens of unused fields—timestamps in multiple formats, internal system IDs, verbose debug information—all billable data providing zero investigative value.

Low-Signal Noise Overwhelming Detection: The vast majority of logs contribute nothing to threat detection. Routine status updates, scheduled tasks, and normal operations rarely factor into security incidents, yet consume the same costs as critical security events.

Unstructured Data Requiring Expensive Processing: Raw logs demand parsing, normalization, and enrichment within Splunk, consuming compute resources and slowing query performance. Every transformation happens after you've already paid for ingestion.

This waste forces pressure on stagnant budgets and creates dangerous compromises like:

• Selectively ingesting data (creating blind spots)

• Aggressive retention policies (limiting historical investigation)

• Avoiding new log sources (slowing threat detection)

• Disabling expensive features (reducing capability)

These aren't cost optimizations—they're security risks disguised as budget management.

The real-time shift eliminates latency, reduces ingestion overhead, and allows instant action before logs hit expensive analytics tools. Unlike traditional observability tools that analyze data after it lands, Onum processes and adds value to telemetry at the point of ingest. This shift-left approach transforms how organizations handle security data by extracting value immediately, not after expensive storage.

Traditional: Collect → Store → Process → Analyze → Act
Onum: Collect → Process → Enrich → Route → Act

Onum's real-time processing engine:

Filters with Surgical Precision: Field-level logic identifies and drops or re-routes low-value events without compromising security context. This is intelligent reduction that preserves investigation capability.

Enriches Before Ingestion: Threat intelligence, user context, and asset metadata are added at the point of collection. Your Splunk environment receives pre-enriched, analysis-ready data instead of raw logs requiring expensive processing.

Eliminates Redundancy Intelligently: Duplicate detection happens in real-time, consolidating related events into composite logs that provide better context while reducing volume.

Applies Context-Aware Sampling: Maintains complete visibility for security-relevant events while intelligently sampling routine operational logs based on content, not just volume.

Most pipeline tools focus on reducing volume using regex filters or simple routing. Onum goes further by applying real-time enrichment and analysis at the edge making intelligent, content-aware decisions before data ever touches your SIEM. It’s not just smaller data—it’s smarter, context-rich, and immediately useful.

The financial impact of intelligent data processing becomes clear when you examine the pain points of Splunk's various pricing models. Each pricing model brings unique cost pressures, but all share a common vulnerability: they charge for data volume regardless of data value. Consider how your current Splunk costs scale with these pricing realities:

These aren't theoretical savings—they're immediate financial improvements that compound over time. When you reduce data volume by 50%, you're not just cutting this year's licensing costs in half. You're also reducing infrastructure overhead, improving system performance, and freeing the budget for strategic security investments.

The savings potential becomes tangible when you apply intelligent processing to real-world Splunk environments. Here's how organizations typically calculate their return on investment: Most enterprise security teams we work with process between 500GB and 15TB daily through Splunk. Using conservative estimates, here's what intelligent data reduction delivers:

Step 1: Assess Current Annual Splunk Spend

Total Annual Cost = (Daily GB × Splunk Rate per GB × 365) + Infrastructure + Operational Costs

Step 2: Apply Conservative Reduction Estimate

Annual Savings = Total Annual Cost × Achievable Reduction % × Pricing Tier Impact Factor

Where:

• Achievable Reduction % = 25-35% (this is conservative; we typically see reduction rates between 40-60%)

• Pricing Tier Impact Factor = 0.7-0.8 (accounts for tiered pricing effects)

Step 3: Account for Implementation

Net First-Year Savings = Annual Savings - (Implementation Cost + Learning Curve Impact)

Environment:

• Current daily ingest: 500 GB

• Splunk Cloud pricing: $250/GB annually (negotiated rate)

• Current annual licensing: $45.6M

• Infrastructure/operational costs: $8.4M

• Total current cost: $54M annually

Conservative Optimization Results:

• Achieved reduction: 30% over 6 months

• Pricing tier impact: 0.75 (some savings lost to tiered pricing)

• Net data reduction cost impact: 22.5%

Financial Impact:

• Annual Savings = $54M × 0.225 = $12.15M

• Implementation Cost = $2.8M (professional services + internal time)

• Year 1 Net Savings = $9.35M

• Year 2+ Annual Savings = $12.15M

Break-even timeline: 3.5 months
3-year ROI: 285%

Why These Numbers Are Conservative

Many customers achieve higher savings, but this model deliberately underestimates savings potential by:

• Using lower-end reduction percentages

• Accounting for pricing tier impacts

• Including implementation costs upfront

• Assuming gradual rollout over 6 months

Transitioning to intelligent data processing doesn't require disruptive migrations. Here's how organizations implement Onum:

Phase 1 (Week 1-2): Deploy assessment tools, categorize sources by value, benchmark costs
Phase 2 (Week 3-4): Configure visual pipelines, implement deduplication, set sampling policies
Phase 3 (Week 5-6): Add threat intelligence, normalize formats, enable Sigma rules
Phase 4 (Ongoing): Monitor savings, track metrics, refine with AI recommendations

Typical Findings:

• 60-70% of log volume provides minimal security value

• Top 5 sources represent 80% of ingestion costs

• 40-50% of indexed fields are never queried

Results Timeline: 25-30% savings month one, 40-50% by month three, up to 60% with optimization.

User-Friendly UX: Onum's drag-and-drop interface eliminates complex scripting and reduces configuration errors.

While reducing Splunk costs creates immediate financial benefits, the true competitive advantage comes from real-time data intelligence capabilities.

Traditional Approach: Collect everything, analyze later, respond after detection
Onum Approach: Process intelligently, detect immediately, respond in real-time

Organizations with optimized telemetry experience measurably better security outcomes:

Faster Threat Detection: Pre-enriched data with threat context enables immediate correlation and alerting, reducing mean time to detection from minutes to milliseconds.

Higher Quality Alerts: Noise reduction and intelligent filtering dramatically decrease false positives while ensuring critical events receive immediate attention.

Enhanced Investigation Efficiency: Analysts work with structured, contextualized data instead of raw logs, accelerating incident response and forensic analysis.

Intelligent pipelines provide strategic advantage as data grows exponentially:

• Scale monitoring without proportional cost increases

• Adapt quickly to emerging threats

• Shift budget to proactive security initiatives

When you're not constrained by per-GB pricing models, you can expand coverage to previously unmonitored systems. When your infrastructure isn't processing redundant logs, you can invest in advanced detection capabilities. When your analysts aren't buried in noise, they can focus on threat hunting and strategic security improvements.

This is the era of real-time data intelligence. Organizations that embrace intelligent processing will outpace those still trapped in reactive, volume-based approaches.

The paradigm has shifted. Leading security teams no longer accept the false trade-off between visibility and cost. They've moved to a model where data is observed, enriched, and acted upon in milliseconds before it ever reaches expensive storage and analytics platforms.

Onum gives you surgical control over what data needs immediate analysis versus what can be filtered, enriched, or redirected—all without creating security blind spots. This isn't just cost optimization; it's security evolution.

Curious how much Splunk waste you can eliminate—without sacrificing visibility? See how Onum delivers real-time intelligence that cuts costs and boosts detection.

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