NavyaAI designs, deploys, and operates production LLM and RAG stacks on-premise or in the colocation facility you choose — sized from a single edge board to multi-GPU clusters. Built for EU and UK organizations where GDPR, data residency, and sector rules make US-cloud AI APIs a non-starter, and for any team whose data simply cannot leave the building.
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Leave your perimeter
~$0.47/M
Measured 70B serving cost
158 TPS
On a $499 edge board
Legal keeps asking where the prompts go. For EU personal data, regulated sectors, and post-Schrems-II risk postures, sending documents to US-operated AI APIs is a compliance problem architecture can simply remove.
EU-region managed AI still runs on someone else's stack, under someone else's terms, at a premium. On-prem and colo deployments give you the control you're paying sovereignty prices for.
Quantization, serving stacks, GPU sizing, evals, and on-call operations are why most on-prem AI plans stall at the whiteboard. That layer is exactly what we deliver — with published benchmarks to prove the numbers.
Deep Dive
EU-region API endpoints and data-processing addenda manage transfer risk on paper. On-prem deployment removes it physically: the model runs where the data lives. For DPOs and regulators, 'the data never leaves our facility in our jurisdiction' is a one-sentence answer that no clause-stack matches — and it is robust to the next Schrems-style ruling, because there is no transfer to invalidate.
On-prem AI proposals usually run on vendor throughput claims and optimistic utilization. Ours run on data we published: edge boards benchmarked to their failure points (with the failures in the report), 70B serving measured to the dollar per million tokens, and crossover math showing exactly when private beats API pricing. You see the economics before buying hardware — and the same telemetry after deployment proves them.
Audit Focus
The first pass is designed to identify the smallest useful intervention: routing, caching, prompt control, serving tuning, or a deeper break-even audit.
Every tier is sized from data NavyaAI has published — not vendor marketing numbers.
| Tier | Hardware class | Measured basis |
|---|---|---|
| Edge / branch site | Jetson Orin Nano class, <15W | 158 tokens/sec aggregate at 16 users, ~$14/month all-in — published report with raw CSVs |
| Departmental | Single L40S-class GPU server | 7B-13B-class serving; ~$3.09/M token cloud-rental baseline to beat |
| Production cluster | 2x H100-class, HA pair | 70B-class INT8 serving measured at ~$0.47/M tokens at high utilization |
| Regulated multi-site | Cluster + edge fleet | Central + branch architecture; sizing via our Edge LLM Sizing Agent |
How It Works
Step 1
30 minutes: compliance constraints, workload shape, existing facilities. You leave with a candidate architecture and tier.
Step 2
Hardware specification and total cost of ownership from measured benchmarks — CAPEX, power, operations floor, and the API-pricing crossover for your volume.
Step 3
Serving stack, models, evals, and monitoring installed in your data center or chosen colo facility, in your jurisdiction.
Step 4
Managed operations with in-perimeter telemetry, or full handover with runbooks and training.
Bring your compliance constraints and workload shape. The consult maps them to a concrete architecture: hardware, serving stack, residency boundaries, and operating cost.
$47K → $28K
Case study: a Llama 3 70B production workload moved from 4 GPUs to 2 with INT8 quantization, KV-cache pruning, and serving changes — a 42% monthly cost cut with 2.3x throughput.
Read the full auditFAQ
No — and that is deliberate. We deploy into your data center or the colocation facility you choose in your jurisdiction, so residency, physical access, and contractual control stay entirely yours. We advise on facility selection and handle everything from the rack up: hardware specification, deployment, serving stack, and operations.
It removes the hardest problem — cross-border data transfer — by architecture. Prompts, documents, embeddings, and model outputs never leave your infrastructure in your jurisdiction, so there is no US-cloud transfer to assess, no third-party AI processor in the chain for that data path, and a much simpler DPIA. We design the deployment so your DPO can draw the data-flow diagram in one box.
From our measured data: an edge-class deployment runs about $14/month per board all-in; a 70B-class production stack served INT8 at high utilization measured ~$0.47 per million tokens plus a $1,500-$2,500/month operations floor; hardware CAPEX depends on tier. The consult produces a concrete TCO for your workload — the same math as our public on-prem cost calculator.
Open-weight models — Llama, Gemma, Mistral, Qwen class — from 270M edge models to 70B+ production models, quantized to fit your hardware tier. Model choice is driven by your quality bar and measured throughput on your target hardware, which we benchmark before committing.
Yes — either full handover to your team (docs, runbooks, training) or a managed arrangement: monitoring, upgrades, capacity planning, and incident response, with all telemetry staying inside your network. Most regulated clients start managed and take over operations within a year.
Yes. The same architecture serves HIPAA-adjacent US healthcare workloads, financial-services data that cannot leave the firm, air-gapped and defense-adjacent environments, and any team whose unit economics beat API pricing at their volume — typically above ~24M tokens/month on small models per our published crossover data.
