Splunk is the platform most teams already run, and the most common source of cost pressure.
We are a vendor-agnostic team of observability and security architects. We put a vendor-neutral control layer in front of Splunk, filter and route at the source, and reduce what flows into the per-GB estate without losing a single event.
Why does Splunk cost keep climbing?
Splunk licenses by daily GB ingested, and renewal uplift commonly runs around 9% a year. A contract grows year over year even when you add no new data sources, because the price floor moves and ingest volume rarely shrinks on its own.
Most of that volume earns nothing. Firewall allow logs are typically 60-70% of total volume and almost nobody searches them. Duplicate events commonly account for 30-50% of what gets indexed. You are paying analytics-tier pricing for data with no detection value.
The fix is not ripping Splunk out. The fix is deciding what reaches the per-GB index and what lands in cheap open-format storage at full fidelity. Without a control point in front of Splunk, that decision happens nowhere and everything flows straight into the estate.
~9%
Typical Splunk annual renewal uplift, compounding year over year
60-70%
Firewall allow logs as a share of total volume (typical)
40-70%
Typical ingest reduction once a control layer filters at the source
How do you reduce Splunk ingest cost without losing data?
You place a vendor-neutral control layer in front of Splunk. It inspects every event in flight, keeps a full-fidelity copy in cheap open-format storage, and forwards only the high-value security and operational data into the per-GB index. Your renewal uplift then applies to a much smaller base.
We know Splunk at config level: forwarders, inputs.conf, props and transforms, Technology Add-ons, and CIM. That depth is why filtering, normalizing, and routing in front of Splunk does not break the searches and dashboards your team relies on. Cribl is one of four platforms we use to build this layer, alongside Splunk, Microsoft Sentinel, and Datadog.
- Filter and route at the source so firewall allow logs, debug output, and routine events land in open-format storage at a fraction of analytics-tier cost.
- Keep full fidelity. Nothing is dropped silently. Filtered data stays queryable for investigation, audit, and compliance support.
- Deduplicate the 30-50% of events that are typically redundant before they ever reach the per-GB index.
- Normalize and shape data once in the control layer instead of maintaining parsing logic per source inside Splunk.
- Run Splunk alongside a candidate platform in parallel using multi-destination routing, so you can test with live data before any commitment.
How does Logmetry deliver this on a real Splunk estate?
Zbigniew Gajuk, our Co-Founder and Chief Observability and Security Architect, has led and delivered this work at Fortune 500 scale across 26+ years. Every Splunk environment carries years of custom Technology Add-ons, parsing rules, and search-time field extractions, so we map that surface before we change a single route.
When Splunk is no longer the right fit, you do not have to leave. We optimize what you have, or we run a clean parallel-run migration to another platform. The recommendation is honest, the pros and cons are laid out, and the choice is yours.
Frequently asked questions
Will this break my Splunk searches or dashboards?
No. The data that reaches Splunk is the high-value data your searches and dashboards already query, so SPL and dashboards keep working unchanged. Filtered data stays in open-format storage and remains available for investigation and compliance support. We validate in parallel before cutting over.
How much can we reduce Splunk ingest?
A 40-70% reduction is typical, depending on data composition. The biggest gains come from firewall allow logs at 60-70% of most volumes, duplicate events at 30-50%, and verbose operational logs with no detection value. Your number depends on your environment.
Do we have to leave Splunk?
No. Splunk is the platform most teams already run, and for many it stays the right fit once the control layer trims wasted ingest. If it stops being the right fit, you choose: we optimize what you have, or we run a clean parallel-run migration to another platform.
Does this work for Splunk Cloud and self-managed alike?
Yes. The control layer sits in front of both deployment models. Splunk Cloud estates often see strong returns because cost per GB is fixed by your contract tier, so cutting ingest at the source directly lowers what you pay against that tier.
Ready to explore this further?
Let's discuss how this applies to your environment.
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