On-Prem AI Deployment · Last reviewed
Private AI in your data center. Nothing leaves your perimeter.
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.
Break-even math: Self-Hosted LLM Deployment- 0 bytes
- Leave your perimeter
- Prompts, documents, embeddings, and telemetry stay inside your network, in your jurisdiction.
- ~$0.47/M
- what a 70B costs you per million tokens, measured
- INT8-optimized production stack at high utilization — our published benchmark, not a vendor claim. See the data
- 158 TPS
- on a $499 board you own
- Published Jetson benchmark with raw CSVs — the measured floor of what private AI can cost. See the data
What goes wrong
GDPR and residency rules vs US cloud APIs
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.
Sovereign-cloud pricing without sovereign control
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.
The expertise gap between wanting and running private AI
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.
How this plays out in practice
Residency by architecture beats residency by contract
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.
The measured-numbers difference
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.
What we check first
The first pass looks for the smallest change that moves the bill: routing, caching, prompt control, serving tuning, or a deeper break-even audit.
- Residency and compliance boundary: what data exists, where it may flow, what GDPR/sector rules require
- Facility fit: your data center, your chosen colocation partner, or edge sites — we deploy into it
- Hardware sizing from measured data: edge boards to L40S/H100 clusters, quantization-aware
- Serving stack: vLLM/Ollama-class deployment, model routing, caching, batch policy
- Evaluation and monitoring inside the perimeter — no telemetry leaves your network
- Operations handover or managed on-call: upgrades, capacity, incident response
Deployment tiers, from measured benchmarks
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 the audit works
Deployment consult
30 minutes: compliance constraints, workload shape, existing facilities. You leave with a candidate architecture and tier.
Sizing and TCO
Hardware specification and total cost of ownership from measured benchmarks — CAPEX, power, operations floor, and the API-pricing crossover for your volume.
Deploy inside your perimeter
Serving stack, models, evals, and monitoring installed in your data center or chosen colo facility, in your jurisdiction.
Operate or hand over
Managed operations with in-perimeter telemetry, or full handover with runbooks and training.
A measured example
$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 auditYour facility, your data, our deployment engineering.
Bring your compliance constraints and workload shape. The consult maps them to a concrete architecture: hardware, serving stack, residency boundaries, and operating cost.
Common questions
Does NavyaAI provide the data center or colocation facility?
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.
How does on-prem AI deployment help with GDPR?
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.
What does a private LLM deployment cost to run?
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.
Which models can run on-premise?
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.
Can you operate the stack after deployment?
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.
We're not in the EU — is this still relevant?
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.