Benchmarks — Proof in Dollars
Measured reductions from real engagements — the evidence base behind the calculator.
Customers already pay for this outcome. Here are two proofs: a live customer, and a result we delivered.
No customer has agreed to publish any figure on this page. Figures marked with an asterisk (*) need further research — team validation and, where a customer is named, that customer's written agreement — before any client-facing use. Customer-confirmed numbers unlock the market message: prospects, Cribl sales, strategic partners.
Quest — paying for this outcome right now
~$1M+/yr of Splunk*
Quest runs 435 SVCs of Splunk — that count is confirmed. SVCs are the unit Splunk Cloud bills for compute, and cutting SVC use is the whole game. The yearly dollar figure is an educated estimate from that count.*
$36K for 96 hours → ~15% cut*
Quest pays Cribl $36K for 96 hours of a Resident Services Consultant to cut use by about 15% — roughly $150K/yr saved.*
That is about 4:1 first-year ROI* on the consulting spend. And the savings come back every year. A real customer already pays for exactly the outcome we deliver. The demand is proven.
Why it matters for us: the AI-assisted X10 version delivers the same SVC cut faster, with senior review. It turns a one-time 96-hour job into continuous, subscription tuning — the Assess → Remediate → Monitor motion.
SVC = Splunk Virtual Compute, the unit Splunk Cloud charges for workload. The 435-SVC count is confirmed. The engagement figures ($36K / 96h / ~15% / ~$150K/yr / 4:1) await a traced source.*
HEB — a VisiCore-delivered result
VisiCore's Cribl data-shaping work for HEB carries a savings figure of about $2.1M* — a minimum the customer calculated, framed against per-terabyte SIEM-migration cost and recorded in November 2025 engagement notes. That is not a vendor case study. It is our team, our methods, and the customer's own math.
~$2.1M saved*
Delivered by VisiCore engineers. Proof we can find and remove real cost, not just describe it.
The method codifies
The first job took about 40 hours. Once we capture the method, the next takes a fraction of that. That is how the hours per outcome keep falling.
And it's conservative
Public Cribl material backs real ingest cuts, including a 2025 retail case with about 25% lower daily Splunk ingest. Treat the Quest and HEB numbers as the stronger internal proofs until someone confirms the exact RSC economics independently.
The throughline
Demand is proven
Quest pays for it. The market pays for it. The only question is who captures it.
We can deliver it
HEB proves we already have. Adding AI makes it faster and recurring.
The reduction benchmark table
Every number below comes from VisiCore engagement records, and every row is pending human review.* This table is the evidence base behind the Savings Calculator. Give it a customer's daily ingest volume and source mix, and these benchmarks turn into a defensible dollar estimate.
| Source type | Documented reduction | Confidence | Evidence |
|---|---|---|---|
| FortiGate firewall (full data shaping) | 74–75% (2.6 TB/day saved) | Production, measured | HEB data-shaping pipeline, Nov 2025 |
| FortiGate (KV→CSV pack only) | ~30% (0.7 TB/day) | Production, measured | Quest RSC engagement, 2026 |
| FortiGate ("start"-event drop) | ~27% of firewall volume | Measured; client security team confirmed no impact | Quest RSC, May 2026 |
| FortiGate (full transform) | 50%+ | Projected — next source in queue, not yet cut over | Quest RSC, Jun 2026 |
| FortiGate (independent corroboration) | 58% across ¼ of total ingest | Production, third-party environment | Deloitte POC demo, Mar 2026 |
| Zscaler web (ZIA/NSS) | 50%+ (1,200 → 535 bytes/event) | Measured on validation index | Quest RSC, Jun 2026 |
| CrowdStrike (managed-assets sourcetype) | 68% | Measured on scratch index; methodology documented | HEB data shaping, Nov 2025 |
| Palo Alto | 26–45% (+12% from trailing-comma truncation) | Measured, single engagement | Deloitte, Nov 2025 |
| JSON→CSV reserialization (any JSON source) | 60–68% | Method-level result, applies across sources | Deloitte, Nov 2025 |
| NetFlow (FADA-processed) | 30–70% | Vendor-assisted + POC | Deloitte UPMA, Jun 2026 |
| Dynatrace metrics (JSON→CSV) | 56–57% with zero fields or events dropped | POC | Deloitte POC demo, Mar 2026 |
| Typical high-volume source | 50–70% | VisiCore's stated working range | Deloitte POC demo, Mar 2026 |
Rules of thumb for the dollar math
- A 20% volume cut yields roughly a 10% SVC cut (Quest-derived).*
- A firewall source that was about 25% of total ingest gave a 25% SVC cut across all searches once tuned (Deloitte POC).*
- Processing a GB at ingest time costs a fraction of indexing and storing that same GB later — so every GB you cut upstream is the high-value GB.*
Nothing here is contractually guaranteed. "Guaranteed X% reduction" wording is an open decision. It needs a contract guarantee construct that does not exist yet. Until then, quote these as results recorded from named engagements, never as promises.
Planning estimates — pending measurement
The rows below are guesses, not measurements. They exist so the calculator can cover a full environment today. Each one needs a real before-and-after measurement on a live engagement. Until then, the calculator tags them "est." and never mixes them with the measured table above.
The field references name what each source emits. That vendor documentation is where the "How (planned)" column starts.
| Source type | Estimated reduction | How (planned) |
|---|---|---|
| Palo Alto threat logs | 30–50% | Drop informational severities, dedupe repeats, trim fields |
| Palo Alto GlobalProtect (VPN) | 40–60% | Drop keepalives and portal chatter |
| Cisco ASA / Firepower | 45–70% | Dedupe built/teardown pairs, drop header noise |
| Check Point firewall | 30–50% | Field trim, drop candidates on accept logs |
| Zscaler Firewall (ZIA FW) | 40–60% | Field trim, drop allowed-traffic noise |
| DNS (Infoblox / BIND / Windows) | 50–80% | Drop known-good NOERROR responses, aggregate repeats |
| DHCP | 40–60% | Dedupe renew chatter |
| F5 LTM (load balancer) | 30–60% | Trim access-log fields, drop health checks |
| F5 VPN / APM | 40–60% | Drop keepalives, dedupe session events |
| NetScaler / other load balancers | 30–60% | Same pattern as F5 LTM |
| Cisco switch / router syslog | 30–50% | Drop link-flap and chatter classes |
| Windows Event Logs | 30–55% | Drop noisy event IDs, trim XML, dedupe |
| Windows Sysmon | 30–60% | Tune noisy operational events |
| Linux syslog / auditd | 30–60% | Dedupe cron and systemd noise, trim auditd fields |
| AWS CloudTrail | 40–70% | Drop read-only Describe/List duplicates |
| AWS VPC Flow Logs | 40–70% | Aggregate, drop known-good intra-VPC flows |
| Azure Activity / NSG Flow | 40–60% | Same pattern as the AWS sources |
| Entra ID / Okta sign-in | 30–50% | Drop non-interactive duplicates, JSON→CSV |
| Cisco Umbrella | 50–75% | DNS-style dedupe and aggregation |
| Web servers (Nginx / Apache / IIS) | 30–60% | Drop static-asset 200s, trim user-agent strings |
| Kubernetes / container logs | 30–60% | Drop health probes, dedupe restart storms |
| VMware vSphere metrics | 25–40% | JSON→CSV reserialization |
Each estimate turns into a measured benchmark the first time we run a before-and-after on a live engagement. Fill in the real number, drop the "est." tag, and move the row up to the measured table.
Sources
- Quest: the 435-SVC count is confirmed. The dollar and engagement figures (~$1M+/yr Splunk; Cribl RSC $36K/96h → ~15%) await a traced source — validate with the team and the customer, and check against the Cribl Resident Services Consultant brief.
- HEB ~$2.1M: customer-calculated minimum on a per-terabyte SIEM-migration cost basis, recorded in November 2025 engagement notes; team and customer validation pending.
- Benchmark table: VisiCore engagement records — HEB data-shaping sessions (Nov 2025), Quest RSC weekly statuses and working sessions (2026), Deloitte troubleshooting notes (Nov 2025) and POC demo (Mar 2026), Deloitte UPMA NetFlow briefings (Jun 2026).
- Splunk SVC unit cost varies a lot by volume and customer agreement.
- Cribl public ingest-reduction example: American Retailer case study, 2025.