For a boutique consulting firm, do not ask “local or cloud?” in isolation. Ask: where may this workload process data, what does it require, and who owns the result?
This article proposes a workload-by-workload decision sheet. It is not an industry standard or a compliance certificate.
What is an on-premise AI platform?
In this decision sheet, an on-premise AI platform is the local part of an AI operating environment: the models, tools, data path, operator responsibilities, and acceptance tests assigned to a private box. Microsoft describes local AI as processing on the device rather than transferring data to the cloud, while noting that local operation is constrained by device hardware and leaves maintenance and data-security responsibility with the user (Microsoft).
“On-premise” should therefore describe a documented boundary, not a vague promise. Define it for each workload with these fields:
- Input data
- Permitted processing location
- Required model and tools
- Output recipient
- Human approval
- Connectivity dependency
- Update owner
- Acceptance tests
Then assign the workload to local, cloud, or hybrid components. That sequence is the evaluation method proposed in this article.
The distinction matters because cloud use requires data transfer and connectivity, while local processing can reduce network latency but remains limited by the device hardware (Microsoft). “Can reduce” is conditional. It is not a latency guarantee.
When should a consulting firm choose on-premise AI?
Choose at the workload level. Microsoft lists privacy, compliance, security, resource availability, accessibility, collaboration, cost, maintenance, updates, performance, latency, scalability, connectivity, model size, tooling, customization, and control as factors in the cloud-versus-local decision (Microsoft).
For this decision sheet, a local component is a candidate when the workload’s permitted processing location is local and the required model and tools fit the available box, because Microsoft identifies hardware resources, model size, tooling, customization, and control among the decision factors (Microsoft). A cloud component is a candidate when the workload permits transfer and depends on cloud accessibility, collaboration, tooling, or provider-managed updates, which Microsoft also identifies among the decision factors (Microsoft). A hybrid boundary is a candidate when different parts of the same workload receive different location decisions. These are proposed selection rules, not claims that any model is universally preferable.
Use this workload-boundary table before discussing products:
| Decision field | What to record | Local | Cloud | Hybrid |
|---|---|---|---|---|
| Input data | Exact input category for the workload | Mark only if its permitted processing location is the box | Mark only if transfer is permitted | Mark which inputs stay local and which may transfer |
| Processing location | Permitted location for each processing step | Box | Cloud service | Explicit split by step |
| Model and tools | Required model and tool set | Confirm they operate within the box constraint | Confirm the permitted cloud service provides them | Assign each requirement to one side |
| Output recipient | Person or system allowed to receive output | Record the local recipient | Record the cloud-path recipient | Record recipients for both paths |
| Human approval | Required approval point | Name the local approval | Name the cloud-path approval | Name approval at the boundary |
| Connectivity | Whether the workload depends on a connection | Record what happens when it is unavailable | Cloud use requires connectivity (Microsoft) | Record which steps depend on it |
| Updates | Person or provider responsible | The user owns local maintenance and updates (Microsoft) | Cloud providers handle cloud-service maintenance and updates (Microsoft) | Name the owner on each side |
| Acceptance tests | Test and pass condition for the chosen boundary | Test on the actual box | Test through the permitted cloud path | Test local, transfer, and cloud-assigned steps |
Do not complete the table once for “AI.” Complete it for each named workload you intend to accept.
See the AI Jungle OS cockpit for a done-with-you approach in which the AI workforce runs on a private box that remains yours. For supporting context, read the private AI guide and the box-versus-SaaS guide.
What hardware does an on-premise AI platform need?
There is no responsible generic hardware answer in this brief. Microsoft states that local AI capabilities are limited by device hardware and identifies resource availability plus model size and complexity as decision factors (Microsoft). Hardware selection should therefore follow the workload sheet, not precede it.
Use this buyer sequence:
- List the required model and tools for the workload.
- Record the hardware available on the proposed private box.
- Define acceptance tests for the workload on that box.
- Accept the local boundary only if the actual setup passes those tests.
- Reassign unsupported steps to a permitted cloud or hybrid component, or change the workload requirement.
Do not copy a processor, memory, storage, or accelerator figure from an unrelated deployment. Put every hardware assumption into the brief and verify it on the box being accepted. The self-hosted AI starter kit provides further implementation context without replacing workload-specific testing.
How does on-premise AI compare with cloud AI?
Neither column is a blanket security verdict. Microsoft presents the choice as a trade-off across privacy, security, available resources, collaboration, cost, maintenance, performance, scale, connectivity, model complexity, tooling, and control (Microsoft).
| Factor | On-premise or local component | Cloud component | Buyer’s decision |
|---|---|---|---|
| Data path | Processing occurs on the device, so processed data can stay there (Microsoft) | Use requires transferring data to cloud services (Microsoft) | Record what may process and transfer where |
| Responsibility | The user is responsible for local data security, maintenance, and updates (Microsoft) | The cloud provider handles maintenance and updates for its cloud service (Microsoft) | Name the owner for every component |
| Hardware | Capability is limited by device hardware (Microsoft) | Cloud choice is evaluated partly through scalability and resource availability (Microsoft) | Test the required workload against the selected resource |
| Connectivity | Local processing can operate without sending the workload to a cloud service (Microsoft) | Cloud operation requires connectivity (Microsoft) | State the connection dependency |
| Latency | Local processing can reduce network latency (Microsoft) | Cloud processing includes a network path (Microsoft) | Set and test an acceptance condition instead of assuming performance |
| Tooling and control | Customization, control, hardware, and tools are factors in the local-versus-cloud decision (Microsoft) | Tooling ecosystem and accessibility are factors in the local-versus-cloud decision (Microsoft) | Require the exact model and tools in the workload sheet |
The output of this comparison should be a component map. It should not be a slogan about all-local or all-cloud architecture. See AI agent architecture for a related view of how agent components fit together.
Which security controls belong in the platform boundary?
Start with the paths already named in the workload sheet: input, processing location, output recipient, human approval, connectivity, and updates. Make each path testable and assign its operator.
The OWASP project for LLM application security lists Prompt Injection and Sensitive Information Disclosure among its risks (OWASP). That supports explicitly testing input handling and sensitive-output controls. It does not support a claim that an on-premise platform removes either risk.
Use these proposed controls in the acceptance sheet:
- Input handling: define inputs the workload may accept, then test handling against that definition. Prompt Injection is a listed LLM application risk (OWASP).
- Sensitive output: define which recipients may receive which output, then test the output path against that rule. Sensitive Information Disclosure is a listed LLM application risk (OWASP).
- Processing location: verify that each workload step runs only in its permitted location.
- Human approval: record the required approval and include it in acceptance testing.
- Connectivity boundary: identify which steps require a connection and which data, if any, may cross that path.
- Update ownership: name the person or provider responsible for every component. Microsoft assigns local maintenance and data-security responsibility to the user and cloud-service maintenance to the provider (Microsoft).
The NIST AI Risk Management Framework is voluntary and is intended to improve the incorporation of trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems (NIST). It does not certify compliance. Use the security and governance guide and the security page to continue your review.
How do you evaluate an on-premise AI solution?
Evaluate the solution against accepted workloads on the hardware and paths you intend to operate. The following is the copyable checklist proposed by this article:
ON-PREMISE AI EVALUATION CHECKLIST [ ] Workload has a specific name and owner. [ ] Input data is listed. [ ] Permitted processing location is recorded. [ ] Required model and tools are named. [ ] Output recipient is named. [ ] Human approval is defined. [ ] Connectivity dependency is recorded. [ ] Update owner is named for every component. [ ] Hardware constraint is recorded for the actual box. [ ] Input-handling acceptance test is defined. [ ] Sensitive-output acceptance test is defined. [ ] Processing-location acceptance test is defined. [ ] Connectivity acceptance test is defined. [ ] Update responsibility is accepted. [ ] Each test has a pass condition. [ ] Local, cloud, or hybrid assignment follows the test result.
Keep the checklist attached to the workload boundary. A successful test for one workload does not, by itself, accept a different workload. For a broader introduction to the operating model, see Private AI: what it changes.
What should the deployment brief contain?
The deployment brief should turn every decision into an item an operator can inspect. Use one section per workload and include:
For one workload, complete this article’s proposed worksheet in a fixed order. Name the input data precisely enough to distinguish what enters the workflow, then record the permitted processing location for each step. Write the exact model and tool requirement rather than a broad capability label. Name the person or system permitted to receive the output, followed by the human approval point and the person responsible for it. State whether each step depends on connectivity and what the worksheet assigns when that connection is unavailable. Name the update owner for every local, cloud, or hybrid component. Finally, write an acceptance test for the workload: identify the input used, the expected processing path, the allowed recipient, the required approval, and a clear pass condition. Keep these entries together so the buyer can compare the intended boundary with the tested boundary before assigning the workload.
- the workload name and owner;
- its input data and permitted processing location;
- its required model and tools;
- its output recipient and human-approval point;
- its connectivity dependency;
- its update owner;
- its hardware constraint on the proposed box;
- its acceptance tests and pass conditions;
- its local, cloud, or hybrid component assignment.
Add the completed workload-boundary table and the accepted evaluation checklist to that brief. If pricing is part of the buying process, use the pricing page rather than placing an assumed amount into the technical decision.
Under this proposed method, accept a deployment boundary only when every intended workload has a permitted data path, an accountable operator, a viable hardware and connectivity path, and passed acceptance tests. That turns the private box into a defined AI cockpit decision rather than an undefined infrastructure purchase.
Written by Tileo, who operates a portfolio of internet businesses on this same cockpit.
