Europe SaaS AI FinOps allocation diagnostic example
A transparent synthetic proof-of-work showing how AICloudStrategist maps European SaaS cloud, LLM/API, GPU, vector database, Kubernetes and commitment spend into owner-visible CFO/CTO review queues before promising any optimisation work.
Honesty label
This is a simulated internal proof-of-work asset. It uses synthetic Europe SaaS spend rows only. It is not a real customer case study, not production data, not a testimonial, not a FinOps certification claim, not GDPR or EU AI Act compliance evidence, and not proof that any company achieved savings, revenue, funding, uptime, performance, security, ranking or compliance outcomes.
Why this asset exists
AICS has a Europe SaaS AI FinOps allocation checklist. This proof page shows the measurable logic behind that offer: identify unowned or untagged cloud/AI spend, low-utilisation GPU rows, stale review cadence, Kubernetes cost ownership and evidence that may need GDPR-aware redaction before CFO/CTO discussion.
Synthetic input summary
| Metric | Result from synthetic sample | Diagnostic meaning |
|---|---|---|
| Workload rows reviewed | 12 | Small sample for demonstrating method, not a real SaaS export. |
| Monthly cloud/AI spend modelled | €11,370 | Spend pool requiring owner, product, review and evidence visibility. |
| AI/LLM/GPU/vector spend | €8,740 (76.9%) | AI spend needs separate attribution from normal infrastructure cost. |
| GPU-associated spend | €2,990 (26.3%) | GPU workloads need utilisation and queueing review before scale-up. |
| Kubernetes namespace-associated spend | €9,550 (84.0%) | Namespace ownership can be used as a practical allocation starting point. |
| Spend without product tag | €3,130 (27.5%) | Unallocated spend cannot be safely discussed in unit-economics or board reviews. |
| Spend outside 30-day review cadence | €7,050 (62.0%) | Spend is moving without a current finance/engineering decision loop. |
| Low-utilisation GPU cost to review | €2,990 (26.3%) | This is a review queue, not a savings promise. |
| Spend needing GDPR-aware evidence handling | €2,370 (20.8%) | Screenshots/exports may need redaction before sharing outside the operating team. |
CFO/CTO review queue from the synthetic run
| Priority | Workload | Platform | Owner | Evidence issue | Monthly cost |
|---|---|---|---|---|---|
| 1 | GPU Batch Scoring | GCP | ML | Last reviewed 39 days ago; GPU utilization 32% | €2,310 |
| 2 | Customer Success Copilot | Azure | CS | Missing product tag; last reviewed 47 days ago; possible personal-data evidence | €940 |
| 3 | Unused Reserved Capacity | AWS | Finance | Missing product tag; last reviewed 91 days ago | €1,100 |
| 4 | Legacy Kubernetes Workers | AWS | DevOps | Last reviewed 58 days ago | €990 |
| 5 | Notebook Experiment Pool | Azure | ML | Missing product tag; last reviewed 64 days ago; GPU utilization 21% | €680 |
| 6 | Embedding Backfill Job | Azure | Data | Last reviewed 33 days ago | €620 |
| 7 | Observability Hot Logs | AWS | DevOps | Possible personal-data evidence | €540 |
Owner-dashboard proof pack
- Billing exports or screenshots by AWS account, Azure subscription, GCP project, Kubernetes namespace, owner and environment.
- AI spend register separating LLM/API, agents, embeddings, vector database, model-evaluation, GPU and Kubernetes rows.
- Product-tag coverage report with temporary exception log for unallocated workloads.
- GPU utilisation snapshot, queueing assumptions and next review owner.
- GDPR-aware evidence handling note for any screenshot/export that may include personal data or customer identifiers.
- Monthly CFO/CTO decision pack with movement, blocker, owner action and next review date columns.
- Written approval before any public case study, logo, testimonial, savings, compliance or outcome claim.
Reproducible artifact
The source model and generated markdown report are stored internally at /home/agent/.hermes/aicloudstrategist/case-studies/simulated-europe-saas-ai-finops-allocation-2026-07-12/. Inputs are explicit in sample_eu_saas_spend.csv; calculations are deterministic and labelled as simulated.
Verification: python3 eu_saas_finops_diagnostic.py regenerated the report with rows=12, total_cost_eur=11370, ai_spend_eur=8740, gpu_spend_eur=2990, untagged_spend_eur=3130, stale_review_spend_eur=7050, low_gpu_spend_eur=2990 and gdpr_sensitive_spend_eur=2370. Input SHA256: 9ddff3e15103258277e343d03eef84329b9014b6a7bb3e06a31fec06e0b124b8.
Claim boundary
No real European SaaS client, production data, logo, testimonial, FinOps certification, official platform partnership, GDPR compliance, EU AI Act compliance, legal advice, tax advice, accounting advice, security certification, audit attestation, savings, cost-reduction percentage, revenue, ranking, funding, uptime, performance or AI accuracy guarantee is claimed. This means no legal advice, no security guarantee and no revenue guarantee. Real implementation would require explicit scope, data-handling approval, payment route and written authorization before any public proof use.