On-Premise vs Cloud AI for Hospitals: Which Vendors Deploy Behind Your Firewall?

Short answer: healthcare AI vendors fall into four deployment models. Only two — on-premise deployments and self-hosted open-weight models — keep inference behind the hospital firewall. Cloud-only vendors and private-cloud deployments process patient data outside your building, under a Business Associate Agreement. The fastest test: ask whether the product still works with all outbound traffic blocked.

The four ways healthcare AI vendors deploy

Deployment model	Where inference runs	PHI leaves your network?	Works with outbound traffic blocked?	Typical fit
Cloud-only (AI API or SaaS)	Vendor’s cloud	Yes	No	Fast start, largest models, broad tasks
Private cloud (your cloud tenant)	A cloud account you control	Yes, to the cloud provider	No	Hospitals already standardized on one cloud
Self-hosted open-weight (do-it-yourself)	Your servers	No	Yes	Hospitals with an in-house ML and MLOps team
On-premise, vendor-deployed	Your servers, installed and tuned by the vendor	No, in normal operation	Yes	Hospitals that want local control without building an ML team

1. Cloud-only vendors

Most AI products work this way: documents or prompts are sent to the vendor’s cloud, processed by a hosted model, and the result is returned. Because the vendor processes PHI, HIPAA requires a Business Associate Agreement, and your security team has to review the vendor’s data handling, retention, and sub-processors. The upside is speed and access to the largest models.

2. Private-cloud deployments

The model runs in a cloud account your organization controls, which gives you more say over configuration and logging. Patient data still leaves your building for the cloud provider’s data center, so the provider’s BAA and your cloud security posture both matter.

3. Self-hosted open-weight models

Your team downloads an open-weight model, runs it on hospital servers, and builds everything around it: the inference server, access controls, audit logging, evaluation, updates, and any fine-tuning. Data stays inside, but you own every operational and compliance task.

4. On-premise, vendor-deployed

A vendor installs the model and platform on hardware inside your network — or certifies hardware you already own — tunes it for specific workflows, and supports it. Inference, logs, and model storage stay on your infrastructure. This is the model GofarAI Labs uses.

How to tell if a vendor is really on-premise

“Private,” “secure,” and “HIPAA-ready” can describe any of the four models above. These questions separate them:

1. Where does inference physically run? Ask for the server location, not the data-center region.
2. Does the product work with all outbound traffic blocked? If not, data or metadata is leaving your network.
3. Does the software send telemetry, usage data, or crash reports? “No PHI” is not the same as “no outbound calls.”
4. Where are prompts, outputs, and logs stored, and who can read them?
5. Who owns model weights or adapters trained on your data, and does the vendor keep copies?
6. How are updates delivered? Automatic cloud updates require outbound access; signed offline packages do not.
7. What does the audit log record? Look for actor, timestamp, and data scope on every request.
8. Can departments be isolated so behavioral-health data under 42 CFR Part 2 never mixes with other domains?
9. Can you swap models as better open-weight models are released, or are you locked to one vendor’s roadmap?

On-premise vs cloud: the honest trade-offs

	On-premise	Cloud-only
Patient data location	Stays on your infrastructure	Processed on vendor infrastructure under a BAA
Security review	Focused on your own environment	Includes the vendor, its cloud, and sub-processors
Model size	Small to mid-size open-weight models (7B–14B is typical)	Largest frontier models available
Task fit	Narrow, well-defined workflows, tuned locally	Broad, open-ended tasks
Hardware	Your GPU server (a single 24GB card covers many document workflows)	None on site
Speed	Usually slower per request	Usually faster per request
Works offline	Yes	No

Neither model is right for every hospital. If a workflow touches PHI, needs to run behind a strict egress firewall, or involves behavioral-health data, on-premise is usually the simpler path. If the task is broad and the data is not sensitive, cloud tools may be the faster choice.

Where GofarAI Labs fits

GofarAI Labs is an on-premise, vendor-deployed option. We specify and install the GPU server — or certify yours — deploy tuned open-weight models, and add workflow tools one at a time, starting with document intake. The platform is designed to run with no outbound network dependencies, logs every inference, and receives updates as signed offline packages. Adapters trained on your data belong to you and stay on your hardware.

See the five-layer platform · Read the security model · How on-premise fine-tuning works · On-premise AI for ICD-10 & CPT coding

The core platform and PDF processing workflow are working prototypes available for hospital pilot evaluation.

Frequently asked questions

Which AI vendors can deploy behind a hospital firewall?

Vendors that offer an on-premise or self-hosted deployment, where the model and inference server run on hardware inside your network. Cloud-only vendors, including AI features inside cloud SaaS products, process data on their own infrastructure. Ask every vendor whether the product still works with all outbound traffic blocked; only a true on-premise deployment can say yes.

Is on-premise AI automatically HIPAA compliant?

No. HIPAA compliance is a property of a specific deployment inside a specific hospital, confirmed by that hospital’s own review. On-premise deployment removes the external model provider from the data path, which simplifies that review, but you still need access controls, audit logging, encryption, and a risk analysis.

Do we need a BAA with an on-premise AI vendor?

It depends on whether the vendor’s people or systems create, receive, maintain, or transmit PHI. If the software runs entirely in your environment and the vendor never touches patient data, the software alone may not make them a business associate. If vendor staff work with PHI — for example during on-site fine-tuning or support — a Business Associate Agreement is typically required. Confirm the specifics with your counsel. See also: Can a hospital run an LLM without a BAA?

Can a small on-premise model match a large cloud model?

On narrowly defined workflows, often yes. A smaller open-weight model tuned and evaluated against local requirements can outperform a larger general-purpose model on that specific job. For broad, open-ended tasks, the largest cloud models still have an edge.

What hardware does an on-premise hospital LLM need?

For document workflows, a single 24GB GPU runs a quantized 8–14B open-weight model well. Larger deployments use multi-GPU servers, which also allow department isolation between models.