AI and cloud compute cost advisor
Govern generative AI, GPU and cloud spend from your India GCC. A transparent engineering model with every assumption on screen.
Enter your monthly run rate. The model shows current spend, an engineered optimised baseline, and what an India based FinOps and AI platform cell inside your GCC would cost to deliver it.
Measured SM or tensor core activity, not allocation.
Share of spend attributable to an owner, product or cost centre.
| Spend pool | Current per month | Optimised per month | Reduction |
|---|---|---|---|
| Generative AI inference | $7,227 | $3,629 | 50% |
| GPU training and fine tuning | $43,958 | $20,352 | 54% |
| Core cloud compute, storage and egress | $235,000 | $154,385 | 34% |
Cost per 1,000 AI requests: $4 today, $2 optimised.
GPUs needed at 70% utilisation: 10.3 versus 16 today.
Stage: Walk on the FinOps Foundation crawl, walk, run scale.
4 engineers in India: $208,000 a year.
Same team onshore: $740,000 a year.
India cell saves $532,000 a year before any cloud saving.
Engineering levers modelled
Cached input billed at roughly 10% of listAI
Up to 90% lower per token on simple intentsAI
50% discount on eligible trafficAI
Fewer GPUs for the same useful workGPU
35% committed, up to 65% spotGPU
Non production off hours, 10 to 15% rightsizingCloud
About 35% on covered steady state computeCloud
Tiering, CDN, private links, region placementCloud
Assumptions and method
- Token prices are indicative 2026 list prices per million tokens by tier; cached input at about 10% of list; batch APIs at 50% of list.
- GPU on demand hourly rates are indicative hyperscaler list prices; committed capacity at 35% off, spot at 65% off; 730 hours per month.
- Optimised baseline targets 40% cache hit, 25% batch share, 40% routing to small models, 70% GPU utilisation, 50% committed and 20% spot GPU, 70% savings plan coverage, 70% idle recovery, 12% rightsizing, 25% storage tiering and 20% egress reduction.
- Realisable saving scales with tagging coverage: unattributed spend cannot be governed, so realisation ranges from 55% at zero tagging to 100% at full coverage.
- Fully loaded FinOps or platform engineer cost: about USD 185,000 onshore versus USD 52,000 in Pune, Bangalore or Hyderabad.
- Indicative estimates only. Validate against your billing exports (AWS CUR, Azure Cost Management, GCP Billing export) and FOCUS formatted data.
Frequently asked questions
What is compute cost advisory for a GCC?
It is the discipline of measuring, attributing and reducing AI and cloud spend, covering large language model tokens, GPU capacity and core cloud services, run by a FinOps and AI platform cell inside your India Global Capability Centre so the parent company gets engineering led governance at India cost.
How much can a GCC save on AI and cloud spend?
Enterprises typically find 20 to 40 percent of cloud spend recoverable through idle shutdown, rightsizing, commitment coverage and storage tiering. Generative AI inference often drops 50 to 80 percent through prompt caching, batch APIs and routing simple requests to small models. GPU estates at 40 to 50 percent utilisation can often run the same work on 30 to 40 percent fewer GPUs.
Why run FinOps from India?
A senior FinOps or platform engineer costs roughly USD 50,000 to 55,000 fully loaded in Pune, Bangalore or Hyderabad versus about USD 185,000 in the United States or United Kingdom, and India has deep AWS, Azure and Google Cloud certified talent plus a follow the sun overlap for daily anomaly reviews.
Which metrics should a board track?
Cost per 1,000 AI requests, cost per model training run, GPU utilisation, commitment coverage, share of spend with owner tags, anomaly detection time and unit cost per customer or transaction. The advisor reports the first four directly.
Does data localisation affect cloud cost in India?
Workloads subject to the Digital Personal Data Protection Act 2023 or RBI data localisation can run in Mumbai or Hyderabad hyperscaler regions. Placing compute near the data also reduces egress charges, which the advisor models as a design lever.
Are the prices in the tool exact?
No. They are indicative 2026 list prices and conservative discount assumptions, all shown on screen. A formal assessment uses your billing exports in the FinOps Open Cost and Usage Specification format.
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