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Where does your infrastructure spend leak — and why? (5–7 min survey)

We combine architecture + telemetry + billing for cloud and on-prem in one UI: an interactive Canvas (drag/connect services, click routes, see cost & metrics directly on the diagram) → overspend highlighting, recommendations, $/route, and Before/After in $.

Deployment: cloud or in your perimeter. The survey is anonymous.

(You can leave your contact information below for a demo)

1. Your role?

2. Company size (employees)?

3. Where do your core workloads run?

4. How painful are these (0 = not a problem, 5 = very painful)?

(We track KPIs like $/route and before/after impact in dollars)

Unpredictable infrastructure spend

Surprise egress/NAT costs

K8s/VM/DB overprovisioning (idle resources)

Expensive observability (logs/traces/APM)

Retry storms/timeouts; expensive request chains

Config drift (as-built ≠ as-designed / IaC)

Slow change approvals / change management (CAB) (takes too long to ship changes)

Hard to attribute $ to specific services/routes (cost attribution)

Idle environments wasting capacity (staging/dev/test) (cloud + on-prem)

Licenses and support costs higher than expected (VMware, DB, observability, etc.) (often on-prem + cloud)

CI/CD cost (runner clusters, artifacts, test databases) is higher than expected (cloud + on-prem)

5. For each set, pick the MOST valuable and LEAST valuable item

Use the up and down arrow keys to change the rank.

Cost per Route / $ per endpoint — attribute spend to services and request paths (cost attribution)

Traffic Matrix — NAT/egress/inter-AZ/inter-DC traffic and cost across edges/routes

Preflight & Guardrails — policies before runs (warn/block), budget/SLO caps, drift prevention

Auto-Patches — generate standard fixes (PR / Helm / Terraform)

K8s Rightsizing — requests/limits tuning, detect idle and overprovisioned zones

Autoscale Advisor — HPA/KEDA: metric selection, scaling steps, stabilization

DB/IO Advisor — connection pools, indexes, IOPS/latency, limits and recommendations

Observability Cost Optimizer — sampling, log levels, PII, ingest/retention cost control

CI/CD Cost Optimizer — runners, build queues, caches, pipeline cost attribution

Idle Environments Control — detect and auto-stop idle dev/stage/test environments

Registry/Artifacts & Test Data Cleanup — retention for container images/artifacts + test DB/snapshots cleanup

License & Support Spend Control — license/support cost attribution and optimization (VMware/DB/Obs/SaaS)

6. Monthly price for the platform (excluding pass-through cloud spend)

Enter a number in USD per month. Numbers only (no ‘k’, no commas)

a) Too cheap (quality concerns)

b) A bargain

c) Expensive but still worth considering

d) Too expensive

7. Assume the package includes:
BI (cost analytics): $/route, driver breakdown (compute/network/database/observability), traffic cost map (NAT/egress/inter-AZ), and before/after impact in $

We estimate the cost of specific routes/services and quantify change impact in dollars

Would you buy at $1,000/month?

Yes

8. What matters most to optimize in your on-prem / internal environment?

Use the up and down arrow keys to change the rank.

CI: runner clusters & build queues (time/capacity)

Idle environments (dev/stage/test): scheduling / auto-stop

Registry / Artifacts / Build cache: growth + retention policy

Test databases / snapshots / copies: volume + retention policy

Overprovisioning (VM/K8s): excess capacity / low utilization

On-prem observability: logs/metrics/traces (volume/retention/storage)

Licenses & support (VMware/DB/Observability/SaaS)

9. Would you install our on-prem agents/connectors?

Yes (no issue)

Yes, read-only only

Air-gapped only (no outbound internet)

10. What data could you realistically provide (even aggregated)?

11. Which pilot format is most feasible?

Pilot = a short engagement to validate value (savings/performance) with clear before/after evidence

2 weeks: assessment + report + 1–2 quick fixes

1 month: assessment + fixes + “before/after” verification run

Report only (no fixes)

Cost attribution only ($ tied to services/routes)

12. How would you measure success?

13. Would you consider a success fee on verified savings?

Yes

Depends (text)

14. Open-ended: Describe your last cost or performance “surprise” (1–3 sentences)

15. Mini-case #1 (cloud):
“Spend suddenly increased; suspected NAT/egress/logs”
What do you do first?

15.1. How long does it usually take to find the root cause?

15.2. What is hardest?

Finding the “owner / culprit” (service/route)

Proving the fix and getting approval

Data complexity / multiple systems

Access/security/policies

No clear owners/accountability

Other (text)

16. Mini-case #2 (on-prem):
“CI runners + idle envs + artifacts keep growing and consume resources.”
What do you actually do today?

Set/limit artifact & registry retention

Turn off idle environments on a schedule

Consolidate runner pools / autoscale

Optimize build caching

Reduce test DBs/copies/snapshots

Almost nothing — “no time / backlog”

Leave your contact details (if you’d like a demo or follow-up)

Phone

Messengers (WhatsApp, Telegram, Other)