Written by Jakub Rusinowski · Last updated 2026-07-08 · Prices verified 2026-03-01
A local LLM processes personal data entirely on infrastructure you control, which removes the hardest GDPR problems of cloud AI in one move: no third-country transfers, no processor agreements with model vendors, no dependence on a provider's retention promises. Cloud AI can be operated GDPR-compliantly — the major providers offer DPAs, EU data residency options, and zero-retention API terms — but compliance becomes an ongoing legal relationship instead of an architectural fact. For EU companies handling sensitive categories of data, on-premise is the materially simpler defensible position. (This page is practitioner guidance, not legal advice.)
The compliance dimension — what each architecture makes you responsible for.
| Dimension | Local | Cloud |
|---|---|---|
| Data leaves your infrastructure | No — inference happens on your hardware | Yes — every prompt is processed by the provider |
| Third-country transfer analysis (Ch. V GDPR) | Not triggered by inference | Required unless provably EU-contained; relies on adequacy/SCCs |
| Processor contract (Art. 28 DPA) | Not needed for the model itself | Required with the API vendor; terms change over time |
| Records of processing / DPIA scope | Simpler: one system, your controls | Must cover the provider's processing, subprocessors, retention |
| Data subject requests (access/erasure) | Your logs, your deletion — fully in your hands | Depends on provider tooling and retention windows |
| Model quality available | 30–70B open models (Qwen, Llama, Mistral) | Frontier models — the quality ceiling |
| Cost at 1M tokens/day | ~$119/month (build amortized + electricity) | ~$143/month (GPT-4o); less on smaller models |
Department-scale internal assistant: ~1M tokens/day of documents that must stay in-house — 1M tokens/day, RTX 4090 24GB Enthusiast vs GPT-4o (OpenAI), electricity $0.15/kWh. Adjust every input in the interactive calculator on this page.
| Cloud cost / month | $143 (GPT-4o, $2.5/M input + $10/M output) |
| Local cost / month (24-mo TCO) | $119 — $11.25 electricity + hardware amortization |
| Hardware up-front | $2,590 (RTX 4090 24GB Enthusiast) |
| Break-even | Month 20 — cumulative cloud spend passes local |
Estimates: 70/30 input/output mix, 24-month amortization, no resale value, load-time electricity only. Cloud prices last verified: 2026-03-01. Hardware street price checked: 2026-07-06.
GDPR doesn't prohibit cloud AI. What it does is attach obligations to every hop your personal data takes: you need a lawful basis for the processing, an Article 28 data-processing agreement with anyone who processes it for you, a Chapter V transfer mechanism if that processing happens outside the EU/EEA, and the ability to honor access and erasure requests wherever the data went. Every one of those obligations is satisfiable with a cloud provider — and every one of them is *ongoing work* that must survive vendor terms changing, subprocessor lists growing, and adequacy decisions being challenged (ask anyone who built on Privacy Shield before *Schrems II* invalidated it in 2020).
A local deployment collapses that stack. When inference runs on a machine in your office or EU datacenter rack, the model vendor never becomes a processor — there is no vendor in the data path. Open-weight models like Qwen 3, Llama 3.3, and Mistral Small are downloaded once (model weights contain no personal data) and run air-gapped if you want. The GDPR questions that remain — lawful basis, internal access control, retention of *your own* logs — are questions you already answer for every internal system.
Fairness requires the other side. OpenAI, Anthropic, Google, and Microsoft (Azure OpenAI) all offer enterprise API terms with data-processing agreements, no-training commitments, and configurable retention; Azure and Google offer EU-region processing, and Mistral's La Plateforme is EU-hosted by default. A careful legal team can build a defensible GDPR position on any of them, and thousands of EU companies have. The costs are the legal review itself, the monitoring of terms and subprocessors over time, the DPIA that now spans another organization, and residual transfer risk where US-headquartered providers are involved (the CLOUD Act discussion doesn't disappear because a DPA is signed).
The practical question for a mid-size company is rarely "is cloud AI legal?" — it's "which position do I want to defend in an audit, a customer security review, or a works-council negotiation?" "The data never leaves our infrastructure" is a one-sentence answer. The cloud equivalent is a binder.
For Article 9 data — health, biometrics, beliefs — and for regulated verticals (legal privilege, banking secrecy, German §203 professional secrecy), the calculus tilts hard toward local. Many professional bodies and DPAs have issued cautious-to-negative guidance on feeding such data to third-party AI services. A local model isn't automatically compliant (access control and logging still matter), but it removes the disclosure-to-a-third-party event that triggers the strictest analysis. This is why hospitals, law firms, and public-sector bodies are disproportionately represented among on-premise LLM adopters.
Here's the pleasant surprise: at department scale, the compliant option is *not* the expensive one. An RTX 4090 workstation (~$2,590, see /build) running Qwen 3 32B handles ~1M tokens/day of internal assistant load for about $119/month amortized (hardware over 24 months + electricity). The same volume through GPT-4o is ~$143/month — before you count the legal review hours, which bill higher than the hardware. Break-even against frontier API pricing lands around month 20; against the compliance-overhead delta it's arguably immediate. Run your own volumes in the calculator below.
Cloud AI can be made GDPR-compliant; local AI mostly starts that way. If your data is sensitive, your customers audit you, or your counsel bills by the hour, on-premise turns a permanent legal workstream into a one-time infrastructure decision — and at department scale it costs about the same as the API bill it replaces. This page is practitioner guidance, not legal advice; involve your DPO for the specifics.
Jakub Rusinowski, the founder of LLM Configurator, runs corporate workshops and lectures on deploying local LLMs — hardware sizing, model selection, compliance-friendly architectures, and hands-on setup for your team. Ask about a workshop, or join the biweekly local AI digest.
Methodology & assumptions. All cost figures are estimates from one shared model (lib/costCompare.ts): cloud costs = tokens/day × published per-1M-token prices at a 70/30 input/output split × 30 days; local costs = hardware amortized over 24 months with no resale value + electricity for load time only at your rate, with machine count scaled when volume exceeds one machine’s throughput. Cloud prices carry per-entry source URLs and verification dates; hardware prices come from the curated /build catalog (street prices with check dates). Real bills vary with usage mix, discounts, and idle power — treat break-even months as directional, not contractual.