Industrial AI — Decision Guide
Private LLM vs Public AI for Manufacturing: What Every Plant Leader Should Know
The decisive difference between private and public AI for manufacturing is data sovereignty. With a private LLM, your factory data — production records, layouts, costs, process knowledge, quality logs — stays within your infrastructure and never trains a shared model. With public AI (ChatGPT, Copilot, Microsoft Fabric AI), your inputs are processed on third-party servers and may contribute to model training unless you have an enterprise agreement with specific data processing terms.
What data manufacturers actually share with public AI — and why it matters
When production staff use general-purpose AI tools for work tasks, the data they submit represents some of the most competitively sensitive information the company holds. These are the categories of data that regularly enter public AI systems in manufacturing environments:
When an engineer pastes a shift report into ChatGPT to summarise it, they are sending operational data outside the company's control. Most enterprise AI policies prohibit this — but enforcement is difficult when generic tools are faster than internal alternatives. A private LLM solves the problem at the infrastructure level: there is no public endpoint to paste data into.
The five dimensions of comparison
Data sovereignty
Private LLM
Your data stays within your own infrastructure. No data is transmitted to external servers during inference.
Public AI
Your inputs are processed on the vendor's shared or dedicated cloud infrastructure. Data leaves your network on every query.
Model training
Private LLM
Your operational data never improves the vendor's shared model. What you query stays yours.
Public AI
Without an enterprise agreement with explicit data processing terms, your inputs may contribute to model training. Enterprise agreements mitigate this but do not eliminate cloud processing.
Domain accuracy
Private LLM
Trained on real factory transformation cases. Understands OEE, takt time, production scheduling, and maintenance reasoning in operational context.
Public AI
Strong general language capability. Poor at factory-specific operational reasoning — can define terms but cannot reason about your specific production constraints.
Compliance
Private LLM
GDPR, ISO 27001, IEC 62443, and NDA compliance is straightforward — data never leaves your perimeter, so audit evidence is generated within your own systems.
Public AI
Requires a Data Processing Agreement with the vendor. Audit evidence depends on the vendor's logging and export capabilities. More complex to satisfy enterprise security reviews.
Total cost
Private LLM
Higher upfront (infrastructure and integration). Lower marginal cost per query. Predictable at scale. No per-token billing risk.
Public AI
Low upfront. Variable cost at scale — per-token pricing compounds quickly with high query volumes. Hidden costs include data governance effort and compliance work.
When public AI is acceptable in manufacturing
This is not a blanket argument against public AI. There are legitimate use cases in manufacturing organisations where general-purpose AI tools are appropriate, effective, and not a data risk:
General research and industry benchmarking
Writing assistance for non-sensitive content
Internal training materials that contain no operational data
Public-facing communication drafts
The boundary is operational data.
Anything that touches your production process, quality records, costs, or supplier relationships should not go through a shared model. The practical question is not "is this sensitive?" — it is "could a competitor benefit from knowing this?" If yes, it should not go to a shared AI endpoint.
The hybrid approach most manufacturers are moving toward
In practice, the most effective AI strategy for manufacturing organisations is not a binary choice between public and private — it is a deliberate architecture with a clear policy boundary between the two:
Non-sensitive tasks at the organisational boundary
Writing assistance, general research, public-facing content, internal training materials that contain no operational data. These tasks benefit from the breadth of general models and carry no meaningful data risk.
Operational decisions, factory data, compliance-relevant use cases
Root cause analysis, maintenance decisions, production scheduling, quality management, procurement reasoning, regulatory reporting — everything that touches operational data stays within a private, domain-trained model.
A written, enforced boundary between the two
The hybrid model only works if the boundary is explicit and enforced — not left to individual judgment. The policy should define which data categories and which use cases belong in each environment, with practical examples that production staff can apply without consulting legal counsel.
Frequently asked questions
Can we use ChatGPT Enterprise and still be safe for manufacturing data?
ChatGPT Enterprise offers a contractual promise that your data is not used for model training. However, it is still processed on OpenAI's infrastructure — data leaves your network. For classified operational data, intellectual property, or regulated environments, this may not satisfy your security requirements or audit obligations.
What does "private LLM" actually mean — is it just a marketing term?
No, but it is misused. A true private LLM means the model runs in infrastructure you control — either on-premise servers in your network, or in a dedicated cloud tenant where your data is logically and physically isolated. DBR77 Vector supports all three modes: on-premise, private API (dedicated tenant), and serverless GPU with isolated execution.
Is a private LLM as capable as GPT-4 or Claude for manufacturing tasks?
For general language tasks — writing emails, summarizing documents — general-purpose models are broader. For manufacturing-specific reasoning — root cause analysis, OEE interpretation, maintenance scheduling, compliance reporting — a domain-trained model consistently outperforms general-purpose models. DBR77 Vector was built on hundreds of real factory transformation cases specifically because general models struggled with operational precision.
How much does a private LLM cost compared to using public AI APIs?
Public AI APIs typically cost $0.01–0.06 per 1K tokens. At manufacturing scale — thousands of daily operational queries — this adds up quickly and unpredictably. Private LLM costs are capacity-based: you pay for infrastructure, not per query. At scale (>500 queries/day), private deployment is usually more cost-effective and predictable.
What compliance frameworks apply to AI in manufacturing?
Key frameworks include GDPR (data residency and processing agreements), ISO 27001 (information security), IEC 62443 (industrial cybersecurity), and industry-specific standards (IATF 16949 for automotive, FDA 21 CFR Part 11 for pharmaceutical). DBR77 Vector holds ISO 27001 and SOC 2 certifications. On-premise deployment simplifies compliance for most of these frameworks because data never leaves your perimeter.
Can we start with public AI and migrate to private later?
Yes, but migration has hidden costs: retraining employees on new interfaces, re-mapping integrations, establishing new governance processes, and cleaning up data exposure that occurred during the public AI phase. Most clients who start with private deployment avoid these migration costs. A pilot on a non-critical workflow is the lowest-risk starting point.
Evaluate DBR77 Vector for your manufacturing environment
DBR77 Vector deploys on-premise, through a private API, or serverless — with complete data sovereignty, ISO 27001 certification, and a full audit trail. See the deployment options or talk to a manufacturing AI specialist.