The seven decisions
1. Trigger profile
What’s pushing toward sovereign? (Regulated data class / inference economics / auditability / air-gap requirement)
2. Regulatory scope
Which regulators bind you? (DORA, NIS2, sectoral, sovereign-cloud mandate, GDPR cross-border)
3. Model selection
Open-weight vs proprietary. Common 2026 open-weight: Llama, Mistral, Qwen, DeepSeek, Phi, Gemma. Choice depends on:
- Language requirement (multilingual vs English)
- Workload type (chat / RAG / code / vision / embedding)
- License (commercial use, attribution, redistribution)
- Capability target
4. Hardware sizing
- NVIDIA H100/H200 — workhorse for fine-tuning and high-throughput inference
- NVIDIA Blackwell — newer, highest memory bandwidth
- NVIDIA L40S — 48GB, multi-tenant inference fleet
- NVIDIA A100 — cost-effective, second-hand market
- AMD MI300/MI325 — alternative when ecosystem fits
5. Multi-tenancy model
- Single-tenant: lab / single-team / PoC
- Multi-tenant via Tenant CRD: enterprise platform / customer-facing
- Cluster-per-tenant: highest isolation, operationally expensive
6. Sovereignty controls
- Encryption keys customer-controlled (HSM)
- Supplier transparency to second hop
- Audit-trail completeness in regulator-consumable formats
- Air-gap option for most sensitive workloads
7. Operational model
- Customer-operated (you run it)
- Vendor-operated (Ænix or similar runs it)
- Hybrid (you operate; vendor 2nd-line)
How decisions interlock
The seven aren’t independent. Trigger profile shapes regulatory scope; regulatory scope shapes sovereignty controls; sovereignty controls shape operational model; operational model affects model selection feasibility.
Common combinations
Pattern 1: Regulated finance + sustained inference + multi-tenant DORA + Article 28 controls + multi-tenant Tenant CRD + customer-controlled keys + Ænix-managed operations + open-weight (Llama 70B class) on H100/L40S fleet.
Pattern 2: Public sector + air-gapped + classified data Sovereign-cloud mandate + air-gap + customer-operated + open-weight (Llama / Phi) on customer hardware.
Pattern 3: AI startup + sustained 24/7 inference + customer-facing No specific regulator + cost economics trigger + multi-tenant + customer-operated + open-weight + GPU mix matched to workload.
How to use the decision guide
Walk through the flowchart. Note your answers. Architecture options narrow naturally.
For specific engagement see Sovereign AI services.
Test yourself: seven sovereign-AI decisions
5 questions · ~2 min