Direct answer
Match the boundary to the workflow.
Neither model removes the need to define permissions, review outputs, control actions, and decide which work stays with licensed staff.
A useful comparison starts with one process and the information it touches. Labels such as "private," "enterprise," or "local" do not settle the operating details on their own.
Definitions
What local and cloud mean in this guide
Local AI
Model inference runs on hardware owned by the agency and installed at its location. The system may still connect to approved email, documents, agency software, update services, or remote support. Each connection needs its own rule.
Cloud AI
Model inference runs on infrastructure operated outside the agency. Retention settings, administrative controls, integrations, deployment choices, and contract terms vary by service and plan. Review the current terms for the exact option under consideration.
Operating model
Compare responsibilities, not slogans.
| Decision factor | Local AI | Cloud AI |
|---|---|---|
| Inference | Runs on agency hardware at the agency's location. | Runs on infrastructure operated outside the agency. |
| Ownership | The agency owns its hardware, data, and custom configurations. Base models keep their licenses. | Ownership and portability depend on the selected service and contract. |
| Setup | Requires hardware selection, implementation, and an operating plan. | Usually requires less client-owned infrastructure to begin. |
| Operations | The system needs monitoring, maintenance, update evaluation, and recovery procedures. | The provider runs the service. The agency still manages accounts, permissions, use, and vendor review. |
| Integrations | Connections are built around the approved workflow and current systems. | Available connections depend on the service, plan, and configuration. |
| Cost model | Advisor Intelligence uses upfront implementation and hardware followed by fixed monthly management. | Pricing may depend on seats, usage, features, or contract terms. |
| Likely fit | Local inference and client control justify dedicated infrastructure. | Ease of access and lower infrastructure responsibility matter more. |
This table compares operating models. It does not claim that either model is automatically safer, cheaper, faster, or more accurate.
Fit
When each model deserves a closer look
Local may fit
The inference boundary is firm.
The agency requires inference on hardware at its location and is prepared to own that infrastructure.
Cloud may fit
Fast access matters more.
The agency wants to begin without dedicated hardware and the selected service passes its review.
Local may fit
The workflow is stable enough to maintain.
A repeatable administrative process can be bounded, tested, and operated over time.
Cloud may fit
The service already fits the job.
Its current controls, integrations, and commercial terms match the intended use.
Different workflows may justify different models. Set the boundary around the information and action involved instead of forcing one rule on every task.
Before buying
Collect evidence for the factors that change the decision.
| Factor | Evidence to collect | Decision owner |
|---|---|---|
| Workflow and data path | Named task, inputs, outputs, systems touched, retention points, and external transfers. | ________________ |
| Task quality | Results on the same representative test set, including expected failure modes and review time. | ________________ |
| Capacity and availability | Measured latency, throughput, concurrency, service limits, outage behavior, and manual fallback. | ________________ |
| Identity and audit | User permissions, administrative controls, action logs, approval records, and offboarding process. | ________________ |
| Data controls | Current contract terms for retention, model training, subprocessors, data region, deletion, and legal process. | ________________ |
| Operations and recovery | Patch ownership, monitoring, backups, restore test, hardware or service replacement, and support access. | ________________ |
| Portability and exit | Export formats, configuration/code rights, model and integration dependencies, and termination steps. | ________________ |
| Three-year cost | Implementation, hardware, seats or usage, support, internal labor, upgrades, downtime, and switching cost. | ________________ |
| Contract allocation | Service levels, warranties, exclusions, incident duties, indemnity, insurance, and approval from qualified reviewers. | ________________ |
If the evidence is missing or no one owns the decision, the operating model is not ready for approval.
Questions
Common points of confusion
Does local AI mean the system never uses the internet?
No. Local describes where model inference runs. Integrations, updates, monitoring, and approved remote support may require network connections. The final design should document each one.
Is local AI automatically safer?
No. Inference location is one design choice. Permissions, network controls, physical access, updates, backups, integrations, and operating procedures still matter.
Do we have to replace our current software?
No. Advisor Intelligence starts with the approved systems the agency already uses. Whether a specific connection is practical is decided during assessment.
Can the system act without human review?
Every Advisor Intelligence workflow begins under human review. More independence is considered only after results meet the agreed standard and the agency approves the change.