# On-Prem vs Cloud ERP AI for Regulated Manufacturers > Source: https://netray.co/erp-help/cio-on-prem-vs-cloud-erp-ai-regulated-manufacturers > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: ERP Help ## Short answer For ITAR, CMMC, and export-controlled manufacturers, the deciding question is not on-prem versus cloud in general but whether controlled unclassified information (CUI) or export-controlled technical data ever leaves your boundary to reach an AI model. An on-prem or private-VPC-deployed AI layer keeps that data inside your control boundary; a vendor's shared cloud AI service usually does not, regardless of how the core ERP itself is hosted. ## Kind cio ## System cio ## Erp On-prem vs cloud ERP (regulated manufacturing) ## Module IT strategy / regulated manufacturing compliance ## Query on-prem vs cloud ERP AI for regulated manufacturers ## Alternate Queries - AI for ERP without sending data to the cloud - on-prem AI for defense manufacturers ERP - ITAR CMMC ERP AI on-prem - air-gapped AI ERP regulated industry ## Applies To Manufacturers subject to ITAR, EAR, CMMC 2.0, or DFARS 252.204-7012, running SyteLine, LN, SAP, or other ERPs ## Steps ### How to evaluate on-prem versus cloud AI for a regulated manufacturing ERP **Items:** Classify your ERP data first: identify which tables, fields, and documents contain CUI or export-controlled technical data versus data with no such restriction (most operational data - PO status, general inventory counts - is not controlled). Confirm with compliance/legal whether any AI processing of controlled data is permitted in a vendor's shared multi-tenant cloud service under your current CMMC/ITAR posture, since most compliance teams answer no for CUI specifically. Evaluate an on-prem or single-tenant private-VPC AI deployment option that never sends controlled fields to a shared external service, keeping data inside your existing authorization boundary. Where the ERP itself is already cloud-hosted (a Tier 2 SaaS ERP, for example), verify whether the vendor's cloud hosting is already FedRAMP-authorized or otherwise compliant, separately from whether its AI add-on features are. Scope the AI layer's access to exclude controlled fields entirely where possible (general operational Q&A does not need access to export-controlled technical drawings, for example), reducing what has to be secured to on-prem in the first place. Get a written data flow diagram from any AI vendor showing exactly where data is processed and stored, and require it as part of the security assessment, not just a compliance checkbox in the sales process. Budget for the higher infrastructure and support cost of an on-prem or private-VPC AI deployment compared to a shared cloud service, since this is the direct tradeoff for keeping data in the boundary. ## Sections ### The real question is data flow, not hosting model labels **Body:** "On-prem versus cloud" is a proxy for the real compliance question: does controlled data leave your authorization boundary to reach a shared, multi-tenant AI service you do not control. A cloud-hosted ERP that is itself FedRAMP-authorized does not automatically make its AI add-on features compliant for CUI processing - the AI feature may call out to a separate, less-scrutinized service. Conversely, an on-prem ERP with an AI layer deployed in your own private VPC or on your own hardware can be fully compliant even though it is technically "in the cloud" infrastructure-wise, because the data never leaves an authorization boundary you control. This distinction matters because it changes the evaluation from a blanket "cloud AI is disqualified" position (which is overly restrictive and gives up real value on non-controlled data) to a more precise one: classify the data, and apply the strict on-prem/private-VPC requirement only to the fields and documents that actually need it. ### What does not need to be on-prem **Body:** A large share of ERP data in most manufacturing environments is not CUI or export-controlled: general operational status, non-technical inventory counts, standard AP/AR data, most scheduling information. AI features that only touch this category of data can often use a vendor's standard cloud service without triggering the same compliance review, which materially widens what a CIO can deploy quickly versus what needs the slower, more expensive on-prem or private-VPC path. The practical move is scoping the AI layer's data access narrowly per use case rather than granting broad access and then trying to restrict it after the fact. ### What genuinely needs on-prem or private-VPC deployment **Body:** Any AI feature that touches export-controlled technical data packages, drawings, specifications tied to a controlled item, or CUI as defined under your CMMC/DFARS scope, needs to run in a deployment model where that data does not leave your authorization boundary - typically an on-prem deployment or a private VPC under your own cloud tenancy, using a model that does not send data to a third-party's shared inference service. This is a more expensive and slower path to stand up than a shared cloud AI feature, and it should be scoped only to the use cases that actually require it, not applied blanket across the whole ERP. ## Pitfalls - Assuming a FedRAMP-authorized cloud ERP automatically makes its AI add-on features compliant for CUI. - Applying a blanket on-prem requirement to all AI use cases instead of only the ones touching controlled data. - Not getting a written data flow diagram from the AI vendor showing exactly where processing happens. - Treating compliance sign-off as a one-time gate instead of revisiting it as AI features and data access scope change. - Underestimating the infrastructure and support cost delta between a shared cloud AI service and an on-prem/private-VPC deployment. - Skipping data classification and discovering controlled fields were exposed to a cloud AI feature only after an audit. ## Ai Angle ERPray and SyteRay support on-prem and private-VPC deployment specifically for this reason: manufacturers under ITAR, EAR, or CMMC 2.0 can ground the AI layer on their ERP data (including SyteLine, LN, and other Infor systems common in defense supply chains) without controlled fields ever reaching a shared external inference service, while still scoping broader, non-controlled operational Q&A more flexibly where compliance allows it. ## Faq ### Does our ERP being cloud-hosted mean we cannot use AI features on it at all under CMMC? **Answer:** No. It means AI features touching CUI or export-controlled data need a deployment model (on-prem or private-VPC) that keeps that data inside your authorization boundary; AI features touching only non-controlled operational data can often use the vendor's standard cloud service. ### Is an on-prem AI deployment significantly more expensive than a cloud AI feature? **Answer:** Yes, generally, due to the infrastructure and support overhead of running the model and grounding infrastructure yourself. Scoping which use cases actually require it (versus which can use non-controlled data in a cloud service) keeps this cost proportionate. ### Who should decide which ERP fields count as CUI or export-controlled for this purpose? **Answer:** Your compliance or export control officer, not IT alone, since the classification determines legal exposure. IT's role is implementing the technical controls (scoping AI data access) once the classification is set. ### Can we start with a cloud AI pilot on non-controlled data while building the on-prem case for controlled data? **Answer:** Yes, and this is a common, sensible sequencing: prove AI value on lower-risk operational data in the cloud first, then justify the higher cost of an on-prem or private-VPC deployment for controlled-data use cases with a clearer business case. ## Search terms on-prem AI ERP regulated manufacturing, CMMC AI ERP, ITAR AI ERP data, air-gapped ERP AI, cloud vs on-prem ERP AI defense, export controlled data AI ERP, private VPC AI ERP manufacturing --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json