TCO Comparison
Sovereign AI economics for high-volume regulated workloads.
A modeled comparison of owned sovereign AI infrastructure, rented GPU capacity, and managed public AI APIs for a 1 trillion token per month banking workload.
70% input and 30% output with an 80% fast-tier and 20% flagship routing mix.
Includes N+1 datacenter pod, annual operating team, Federis license, and SI implementation.
Compared with ₹87.86 Cr for Gemini Enterprise API and ₹110.82 Cr for OpenAI Enterprise API.
Comparison basis
The analysis models a mid-tier Indian bank processing 1 trillion tokens per month with context-heavy RAG traffic, tiered model routing, N+1 high availability, infrastructure operations, and enterprise-volume API discounts.
Why sovereign mode changes the economics
At sustained token volume, public API spend scales linearly with usage. An owned sovereign lane shifts the bank toward reusable GPU capacity, controlled local model execution, and recurring costs dominated by operations, platform license, and hardware upkeep rather than per-token API invoices.
Modeled Assumptions
Workload and infrastructure basis
The comparison is scoped to a high-volume banking workload with enterprise operating assumptions.
- Workload
-
1 trillion tokens/month
- Traffic mix
-
70% input / 30% output
- Routing mix
-
80% fast utility / 20% flagship reasoning
- Exchange rate
-
₹95.67 per USD
- Owned hardware
-
3 HGX B300 servers, 24 GPUs total
- Availability
-
N+1 high availability pod
Savings with an owned sovereign lane
₹30.22 Cr owned sovereign TCO vs ₹89.86 Cr Gemini Enterprise API.
₹30.22 Cr owned sovereign TCO vs ₹112.82 Cr OpenAI Enterprise API.
₹9.24 Cr recurring sovereign cost vs ₹87.86 Cr Gemini Enterprise API.
₹9.24 Cr recurring sovereign cost vs ₹110.82 Cr OpenAI Enterprise API.
Master TCO Matrix
Owned sovereign infrastructure versus public API economics
All values are shown in INR crore and follow the discounted enterprise API scenario.
Public API Cost Reference
Rate-card and negotiated API exposure
The comparison includes both official blended rates and aggressive enterprise-volume discounts.
Modeled blended rate of $0.324 per million tokens.
Modeled blended rate of $0.82 per million tokens.
Modeled enterprise discount for a 12 trillion token/year contract.
Modeled enterprise discount for a 12 trillion token/year contract.
Figures are directional and should be refreshed during procurement using current vendor rate cards, negotiated commercial terms, hardware pricing, depreciation policy, and datacenter costs.
Strategic result
The modeled Year-1 owned sovereign TCO is materially below discounted managed API alternatives, while Year-2 recurring economics are even stronger. The same infrastructure decision also supports data localization, auditability, and operational independence.
- Best suited to sustained, high-volume workloads where token usage is predictable and data cannot leave approved environments.
- Federis remains the governance control plane; GPU, storage, policy, identity, and observability systems stay pluggable and separately deployable.
Model your own workload
Share your token volume, model mix and hosting constraints, and we will rework this comparison with your numbers.