03 · What You Need to Know
Comparing the Main AI Deployment Arrangements Available to Researchers
Why deployment architecture matters independently of model capability
An AI model and the service through which it is accessed are not the same thing.
Model capability concerns what the system can do. Deployment architecture concerns where and how it operates, which organizations participate in processing, what controls are available, and who maintains the infrastructure.
The same open-weight model might run on a researcher's workstation, an institutional server, or rented cloud infrastructure. Its underlying parameters may remain unchanged, but its security, operating cost, availability, and processing environment can differ substantially.
Conversely, a commercial provider may offer several models through one service, with different capabilities or contractual arrangements.
Researchers should therefore evaluate both the model and the environment in which it will be used.
What is public cloud AI?
For research planning, public cloud AI commonly refers to externally hosted AI services available through general commercial access arrangements, such as public-facing chat applications or standard APIs.
These services may be convenient because researchers do not need to purchase specialized hardware or maintain the underlying inference infrastructure.
They can support exploratory work, analysis of public information, and other authorized research tasks.
However, the applicable data-handling terms, model availability, usage limits, and administrative controls depend on the particular service and account arrangement.
Public availability should not be interpreted as permission to submit confidential research data. Researchers must verify whether the intended processing is authorized and appropriately protected.
The term public cloud does not necessarily mean that submitted information becomes publicly visible. It describes a service-delivery arrangement, not a universal statement about data disclosure.
What is enterprise AI?
Enterprise AI generally refers to commercial AI services offered through organizational arrangements that may include administrative controls, contractual commitments, identity management, support, and configurable data-governance features.
Depending on the provider and agreement, enterprise services may offer controls over retention, user access, processing arrangements, and organizational administration.
However, enterprise is a commercial designation rather than a uniform technical or legal standard.
Researchers should inspect the actual contract, service documentation, deployment settings, and data-processing terms.
An enterprise subscription does not automatically establish that information remains on university premises, that all processing occurs in a particular jurisdiction, or that the service satisfies every applicable research requirement.
Its principal advantage may be organizational management and provider-supported infrastructure rather than direct control over model weights or underlying computing systems.
What is institutionally hosted AI?
Institutionally hosted AI refers to an arrangement in which a university or research organization provides an AI environment under its own operational or governance responsibilities.
The institution may operate models on university-owned servers, manage models on external cloud infrastructure, or coordinate access through a centrally administered research platform.
Institutional hosting can provide shared computing resources, controlled research workspaces, technical support, and documented model configurations.
However, the actual data-processing architecture must be verified. An AI service accessible through university credentials may still be operated by an external commercial provider.
The benefits of institutionally hosted AI as shared research infrastructure depend on sustainable technical support, appropriate governance, and sufficient computing capacity.
What is local AI?
Local AI refers to models executed on computing infrastructure controlled by the researcher or institution, rather than through an external model inference service.
A researcher might run an open-weight model on a workstation or use an internally controlled server.
Local deployment can reduce dependence on external inference providers and may allow preservation of particular model files and software environments.
However, it introduces responsibilities for hardware, software maintenance, security, storage, and technical troubleshooting.
Researchers should distinguish local execution from complete network isolation. Some locally installed applications still connect to external services.
The comparison between local and cloud AI models is therefore more meaningful when it addresses the actual processing architecture rather than relying on labels.
How do the four arrangements compare?
| Consideration |
Public cloud |
Enterprise |
Institutional |
Local |
| Infrastructure operation |
External provider |
Usually external provider |
Institution or approved operator |
Researcher or institution |
| Administrative control |
Varies by service |
Often organization-oriented |
Defined by institutional governance |
Depends on local administration |
| Data-processing control |
Provider-dependent |
Contract- and configuration-dependent |
Architecture-dependent |
Potentially direct control |
| Model version control |
Provider-dependent |
Provider-dependent |
May support preserved versions |
May support preserved weights |
| Computing capacity |
Provider-managed |
Provider-managed |
Limited by institutional resources or contracts |
Limited by available hardware |
| Primary cost structure |
Subscription or usage charges |
Organizational agreement or usage charges |
Shared infrastructure and operational costs |
Hardware and operating costs |
| Technical maintenance |
Primarily provider-managed |
Shared organizational and provider responsibilities |
Institutional or contracted support |
Researcher or local technical team |
These categories can overlap. An institution may subscribe to an enterprise cloud service, or it may operate a locally hosted model for university-wide access.
Consequently, the table describes common arrangements rather than mutually exclusive technical classifications.
Start with the sensitivity and permitted use of research data
Data governance should be evaluated before selecting a convenient AI service.
Researchers should determine whether their materials contain identifiable participant information, confidential institutional records, unpublished proprietary data, or information subject to contractual restrictions.
They must then establish which processing arrangements are authorized under participant consent, ethics approval, institutional requirements, and applicable law.
For example, a study involving publicly available journal abstracts may permit a wider range of processing arrangements than one involving identifiable clinical interviews.
Even where data are de-identified, researchers should consider whether individuals could reasonably be re-identified through contextual details or linked information.
The NIST Privacy Framework provides a structured approach to identifying and managing privacy risks. Its principles can help institutions evaluate different processing arrangements without assuming that any one architecture is automatically safe.
Does local AI always provide the strongest confidentiality protection?
No. Local deployment may avoid transmission to an external inference provider, but the system still requires secure storage, access controls, logging arrangements, software maintenance, and appropriate retention procedures.
A poorly managed workstation can create significant confidentiality risks.
Conversely, an approved enterprise or institutional service may provide strong technical and contractual safeguards.
Researchers should evaluate actual exposure pathways rather than equating physical proximity with security.
The confidentiality risks that remain in locally deployed AI include unauthorized access, secondary storage, and disclosure through generated outputs.
For consequential research, the choice should be supported by institutional security and data-protection assessment where appropriate.
How should researchers compare scientific performance?
Model performance should be evaluated using evidence relevant to the intended research task.
A general benchmark ranking may help identify candidates, but it does not establish that a model performs adequately on a particular dataset, language, discipline, or population.
Researchers should establish performance requirements and evaluate candidate models using representative inputs and independently verified reference outcomes.
For classification tasks, relevant measures may include accuracy, sensitivity, precision, and consequential error patterns.
For information extraction, researchers may examine correctness, completeness, and the handling of ambiguous source material.
A model that meets security requirements but fails essential scientific criteria is not an adequate choice. Equally, a highly capable model should not be used when its data-processing arrangement is unauthorized.
Both conditions must be satisfied.
Why model stability and version control matter
AI systems used as research instruments may need to remain sufficiently stable throughout data collection and analysis.
Commercial services can change their underlying models, system configurations, or processing behavior. Some providers offer fixed versions or dated snapshots, while others provide less control.
Local and institutionally managed deployments may permit researchers to preserve exact model checkpoints and relevant software artifacts.
However, preserved weights alone do not guarantee identical outputs. Inference software, numerical precision, hardware, and randomization may also affect results.
Researchers should document model identifiers, processing dates, prompts, configurations, and any changes that could influence interpretation.
Where the study depends on stable measurements, locking the model to a specific version may be methodologically appropriate.
What happens when a provider changes or withdraws a model?
Dependence on a commercial AI service introduces continuity risks.
A provider may update an underlying model, change usage limits, revise pricing, or discontinue access to a particular version.
These changes can affect research projects that require repeated processing over long periods.
Researchers should examine version-retirement policies, export capabilities, service dependencies, and contingency arrangements.
Local deployment may reduce dependence on external model availability, but it introduces its own continuity requirements, including hardware replacement and software preservation.
Institutionally hosted systems also depend on university funding and technical support.
No deployment arrangement eliminates every continuity risk.
How should total costs be compared?
Comparing a monthly subscription fee with the purchase price of a GPU is insufficient.
The relevant quantity is the total cost of achieving the required research outcome over the expected project period.
Cloud services may charge for subscriptions, tokens, requests, computing time, storage, or additional features.
Local infrastructure may involve equipment, electricity, maintenance, software administration, storage, and technical personnel.
Institutional services may distribute these costs across several projects, although shared infrastructure still consumes resources.
Human validation and correction should also be considered when comparing models with different error rates.
The reporting of AI computational costs can help researchers distinguish actual expenditure from resource consumption and estimated replication costs.
Does the cheapest deployment arrangement produce the best value?
Not necessarily.
An inexpensive model may require extensive manual correction or repeated processing. A more expensive service may provide capabilities that materially reduce consequential errors.
Conversely, premium access may provide little additional scientific value for a narrow, well-defined task.
Researchers should first determine which arrangements satisfy the essential methodological and governance requirements.
Cost can then help distinguish among acceptable alternatives.
This approach is consistent with selecting lower-cost or open models when their performance is adequate.
How does deployment affect collaboration?
Collaborative research may involve institutions with different computing resources, subscriptions, procurement arrangements, and data-access permissions.
A cloud service may simplify access for geographically distributed researchers, subject to contractual and data-governance conditions.
A locally installed model may be more difficult to share unless collaborators can reproduce the environment or access a controlled institutional service.
Institutional hosting can provide common workspaces and centrally managed model configurations, but availability may be limited to authorized institutional users.
Research teams should establish who can access the system, who controls the data, and how collaborators can independently inspect or verify outputs.
When only one institution can operate the model, the collaboration may develop dependencies that affect reproducibility and participation.
When should researchers consider environmental and resource costs?
Environmental considerations become more relevant when AI processing is extensive or alternative methods differ substantially in resource requirements.
Local hardware, institutional computing, and commercial cloud services all consume electricity and depend on physical infrastructure.
However, environmental impacts depend on workload, hardware efficiency, utilization, electricity sourcing, and the boundaries of the assessment.
Researchers should not assume that cloud deployment is always more efficient or that local execution necessarily has a lower footprint.
For substantial workloads, the environmental and resource costs of AI at scale may reasonably influence the choice among scientifically adequate arrangements.
Why a hybrid arrangement may be appropriate
Research teams do not necessarily need to use one AI deployment arrangement for every activity.
A project might use a public service for non-sensitive preliminary exploration, an approved institutional model for confidential participant data, and specialized cloud computing for a computationally demanding experiment involving appropriately authorized information.
Such arrangements may improve flexibility, but they also introduce additional documentation and governance responsibilities.
Researchers must identify which system processed each category of information and which model produced each analytical output.
Outputs from different models should not be assumed interchangeable merely because they serve a similar purpose.
Hybrid deployment is defensible when each component has a clear role and the complete workflow remains methodologically coherent.
What should a deployment decision record contain?
A documented deployment decision should identify the research task, data classification, candidate arrangements, essential performance criteria, applicable processing restrictions, and reasons for the selected option.
It should also record relevant model versions, technical configurations, contractual assumptions, estimated costs, and continuity risks.
For studies involving sensitive information, institutional review or approval may be necessary before processing begins.
The NIST AI Risk Management Framework provides a useful foundation for examining governance, context, measurement, and risk management across AI systems.
Documentation should be proportionate to the consequences of the AI-assisted activity. A minor drafting task does not necessarily require the same assessment as automated processing of identifiable participant records.
