Platform costs do not have to keep climbing. Through licence optimisation, data pipeline engineering, and platform consolidation, we help organisations reduce their SIEM and observability spend by 30-50% while improving data quality and coverage.
How We Work: The Cost Optimisation Process
Cost optimisation is not a one-off project. It requires an initial intensive review followed by ongoing monitoring and continuous improvement. Our process ensures you see quick wins fast while building sustainable long-term cost control.
Audit (2-3 weeks): We review licence utilisation, data ingestion patterns, tool overlap, and contract terms. This produces a clear picture of where money is being wasted and where the biggest savings opportunities lie.
Analyse (1-2 weeks): We model the impact of different optimisation scenarios, quantify potential savings, and build a business case with projected ROI. This includes pipeline design, licence right-sizing recommendations, and consolidation options.
Optimise (4-8 weeks): We implement the agreed changes: deploying pipelines, reconfiguring data routing, right-sizing licences, and retiring redundant tools. Every change is validated to ensure no loss of security or observability coverage.
Monitor (ongoing): Through our Operate service, we continuously monitor data volumes, licence utilisation, and cost trends. We catch cost drift early and implement ongoing optimisations as your environment evolves.
Three Levers for Cost Reduction
Apto approaches cost optimisation through three complementary levers. Used together, they typically deliver 30-50% reduction in total platform spend.
1. Licence Optimisation
Most organisations are not using what they have already paid for. We audit licence utilisation across Splunk, Sentinel, Datadog, New Relic, and other platforms to identify shelfware, over-provisioning, and opportunities to right-size. This includes reviewing contract terms, identifying unused features, and helping with vendor renewal negotiations.
Typical findings: 20-40% of licensed capacity is underutilised. Features purchased during initial deployment are never activated. Contract terms have not been reviewed since the original purchase. Multi-year commitments are locking in prices that no longer reflect market rates.
2. Data Volume Reduction
This is the biggest lever for most organisations. SIEM and observability licences are typically priced on data ingestion volume. By introducing intelligent data pipelines using Cribl or OpenTelemetry, we can filter, deduplicate, route, and tier data so that only high-value data reaches expensive platforms. Lower-value data is routed to cheaper storage like data lakes or archive.
Typical findings: 30-50% of ingested data is duplicated, low-value, or never queried. Verbose logging from development and staging environments is sent to production SIEM at full cost. Data that should be archived is being stored in hot, expensive tiers.
3. Platform Consolidation
Many organisations have accumulated overlapping tools across teams: one for security, one for infrastructure monitoring, one for APM. We assess tool overlap, identify consolidation opportunities, and help rationalise the platform landscape. Fewer tools means lower licence costs, reduced operational overhead, and simpler data management.
The Cost Spiral
Platform costs are the number one pain point in every conversation we have. SIEM and observability licences are expensive, and costs have a tendency to spiral upward in a vicious cycle that feels impossible to escape.
More data sources come online. Ingestion volumes grow. Licence costs increase. To manage the complexity, organisations add more tools. More tools mean more operational overhead, which requires more staff. And the cycle continues.
The root cause is almost always the same: there is no systematic approach to managing what data goes where, whether it is needed, and whether the cost is justified by the value it delivers. Most organisations are paying for data they do not use, tools that overlap, and licences that are not right-sized.
Pipeline Optimisation Pays for Itself
Data pipeline engineering through Cribl or OpenTelemetry is the single most impactful cost optimisation technique available. By placing an intelligent routing layer between your data sources and your analytics platforms, you gain complete control over what data goes where and at what cost.
In the example above, a 100TB/day environment was spending £720,000 per year routing all data to an expensive SIEM tier. By introducing Cribl Stream as a routing layer, 45TB of lower-value data was redirected to a data lake at a fraction of the cost. The remaining 55TB of high-value security and operational data continued to the SIEM. Annual savings: £324,000, with no loss of security coverage.
The pipeline investment typically pays for itself within the first 3 to 6 months of operation. After that, the savings are pure cost reduction on an ongoing basis.
Platform-Specific Cost Optimisation
Each platform has its own cost model and optimisation opportunities. Apto brings deep expertise across all major platforms.
Case Study: £180K Annual Savings Through Pipeline Engineering
The Challenge
A UK enterprise client was facing a SIEM platform licence renewal with costs projected to increase by 35% due to data volume growth. The security team had been told to cut ingestion, but had no visibility into what data was being sent or whether it was needed. Cutting the wrong data sources risked creating security blind spots.
The Approach
Apto conducted a data mapping exercise and cost audit. We identified that 45% of ingested data was either duplicated, verbose developer logging, or low-value network telemetry that was never queried. We deployed Cribl Stream as a data routing layer, implementing intelligent filtering, deduplication, and tiered routing. High-value security data continued to the SIEM platform; lower-value data was routed to S3 for long-term archive at a fraction of the cost.
The Outcome
Ingestion reduced by 45%, saving £180,000 per year in licence costs. All security-relevant data was preserved with zero detection coverage loss. The Cribl deployment paid for itself within 4 months. Data quality actually improved because deduplication and normalisation cleaned up inconsistent source formatting. The renewal was negotiated at a lower tier, locking in savings for 3 years.
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