Why this is a real dilemma in 2026
Until mid-2025 the choice was simple: ChatGPT Enterprise or nothing. Open-source models were weaker, hardware was expensive, and deploying them needed an ML team. In 2026 the picture has flipped. Llama 3 70B reaches GPT-4-level quality on most business tasks. An RTX 4090 costs ~€2,300 and fits a 7-8B model. Ollama reduces deployment to a single command.
For law firms, clinics, finance teams and the public sector a real question has appeared: send data to OpenAI, or deploy AI on your own premises?
ChatGPT Enterprise — what you get
Microsoft, OpenAI and Anthropic sell Enterprise SLAs with guarantees:
- Data is not used to train models.
- Encryption in transit and at rest.
- SOC 2 Type 2, ISO 27001, HIPAA BAA (US).
- Logs accessible to administrators.
- SSO and user management.
OpenAI lists ChatGPT Enterprise at around $60 per user per month (negotiated, with team minimums). Microsoft 365 Copilot is $30 per user per month on top of an M365 license. Claude Team is around $25 per user.
For a 30-person company: ChatGPT Enterprise is roughly $21,600/year, and Copilot adds about $10,800/year.
Local Llama 3 — what you get
Llama 3 (8B or 70B) from Meta, licensed for commercial use up to 700M monthly active users. Mistral, Qwen, DeepSeek — alternatives with no such limit.
Hardware (one-time cost):
- Llama 3 8B — RTX 4090 24GB, server ~€3,500–6,000.
- Llama 3 70B — A100 40GB or H100,
€35,000–55,000, or GPU rental at Hetzner / OVH (€1.20–3.50/hr). - Mixtral 8x7B — 2x RTX 4090, ~€6,000–8,000.
Open-source stack:
- Ollama or vLLM — model runtime.
- Open WebUI — ChatGPT-like interface with SSO.
- LangChain or LlamaIndex — RAG, integrations.
- Qdrant or Weaviate — vector database for RAG.
GDPR art. 32 — what the law says
Article 32 requires "appropriate technical and organisational measures" to secure processing. For AI in practice this means:
- Risk assessment: do personal data flow to a third country (e.g. US)?
- Data Processing Agreement: must exist with OpenAI / Microsoft / Anthropic.
- Transfer outside the EEA: requires Standard Contractual Clauses or Data Privacy Framework.
- Logs and audit: who processed which data, when.
ChatGPT Enterprise provides a DPA and declares DPF (US) compliance. But in court you must prove the data was not used for training or exposed to US government access (the Schrems II problem is still alive).
A local Llama 3 removes the entire issue: data never leaves your infrastructure. The GDPR audit narrows down to your own server hardening.
NIS2 — supplier control
NIS2 (transposed into national law across the EU in 2024–2025) requires essential and important entities to:
- Manage IT supply-chain risk.
- Assess cloud service providers.
- Run incident response procedures involving suppliers.
OpenAI and Anthropic are non-EU suppliers. That doesn't block cooperation, but it adds documentation, contract clauses and a "what if OpenAI is down 24h or changes licensing" plan. Local AI shrinks this area to zero.
Quality — where the GPT-4 lead ends
Mid-2026 benchmarks (MMLU, HumanEval, MATH) show:
- GPT-4 Turbo: ~86-88% MMLU, best at complex reasoning and code.
- Llama 3 70B: ~82-83% MMLU, matches on text generation, RAG, classification, summarization.
- Llama 3 8B: ~68-70% MMLU, weaker at reasoning but enough for many internal workflows.
For 80% of business tasks (draft documents, summaries, mail classification, FAQ answers from a knowledge base) the difference is imperceptible. For tasks needing creative reasoning (strategic research, code) GPT-4 still leads.
Break-even — when on-prem makes sense
Rough math:
| Users | ChatGPT Enterprise / year | Llama 3 70B on-prem (3 years) |
|---|---|---|
| 10 | ~€6,500 | ~€58,000 (hardware+deploy+ops) |
| 30 | ~€20,000 | ~€60,000 |
| 50 | ~€33,000 | ~€65,000 |
| 100 | ~€65,000 | ~€75,000 |
The break-even is around 80-100 users over 3 years. Below that, SaaS is cheaper. Above that, on-prem wins on cost alone — without even counting the "data never leaves the company" value.
When to choose which
Pick ChatGPT Enterprise / Copilot if:
- You have fewer than 50 users and a short planning horizon.
- Data is non-sensitive (marketing, general docs, presentations).
- You don't have an internal IT team to maintain a GPU server.
- You need the highest reasoning quality (code, research).
Pick local Llama 3 if:
- You work with sensitive data (client files, medical records, financial data).
- You have 50+ active users.
- You require full control over the model and query logs.
- Your regulator (financial supervisor, data protection authority, NIS2) requires strict supplier control.
The hybrid approach
In practice most of our clients pick a hybrid:
- Copilot or ChatGPT Enterprise for 80% of daily tasks (mail, presentations, general research).
- Local Llama 3 with RAG for sensitive data (case files, legal docs, medical records, proprietary code).
This combines the speed of SaaS where there is no risk with on-prem security where there is.
What's next
If you're wondering which variant fits your company, book an on-premise AI consultation. We'll audit data types and processes, calculate the real cost of both variants and show where to start with a pilot. If you'd rather read about AI automation first, start with AI automation and workflow.