Cloud, on-premises, or open-source AI?

Choose infrastructure from the workload’s data and access boundary, latency, connectivity, capability, change control, portability, staffing, and total operating burden. Managed cloud, private cloud, on-premises, open-source self-hosting, and hybrid designs can each fit. None is a universal security or control answer.

Record three decisions independently: who grants the right to use or modify the model, where inference and storage run, and who patches, evaluates, scales, monitors, and recovers the service. Combining these questions under one label hides responsibility and makes comparisons unreliable.

For example, open-source model weights do not provide an operated service. A managed endpoint does not remove the organization’s responsibility for access, data use, workflow behavior, or provider review. State the chosen position on each axis before comparing architectures.

List what enters the model or retrieval service, what is stored, what appears in logs, which administrators can reach it, and where output returns. Include backups, evaluation sets, support access, observability, and temporary processing. A narrow diagram of the inference call can omit the paths that matter most.

On-premises deployment does not establish privacy by itself, and cloud infrastructure does not remove it by itself. A poorly controlled local service can expose sensitive material, while a managed service can operate within a carefully defined boundary. Judge the controls and data flow that exist, not the location label alone.

Identify the capability the work needs: context size, response quality, structured output, tool use, throughput, hardware fit, or specialized model behavior. Measure latency from work entering to reaching a useful end state. A fast model does not repair a slow review queue or unstable integration.

Define connectivity and continuity requirements. Some work can wait for a managed service to recover. Other work needs a local or manual path during disconnection. Name the degraded mode, how unfinished items are located, and how the operation reconciles work after normal service returns.

Managed services can reduce infrastructure work, but the organization still reviews model changes, access, data terms, output behavior, and workflow effects. Self-hosting adds responsibility for hardware capacity, serving software, patches, security response, model versions, evaluation, and incident recovery.

Compare those duties with available staff and response expectations. A design that depends on rare expertise must include coverage, documentation, and recovery. Portability also requires work: compatible interfaces, stored evaluation cases, exportable records, and a maintained way to replace a provider or model.

Include infrastructure, licenses, network transfer, observability, security, evaluation, upgrades, support, downtime, and staff time. A lower unit price can carry a higher operating burden. A higher managed price can be reasonable when it removes work the organization is not prepared to own.

Hybrid designs fit when separate workloads have separate justified boundaries, such as local processing for disconnected work and managed capability for approved material. They also duplicate routing, controls, evaluation, monitoring, and recovery. Keep one deployment path unless the second path answers a named requirement.

• The workload and data flows are known well enough to draw an access boundary.
• Latency and disconnected-operation needs are measured at the workflow level.
• Staffing and support responsibility are assigned for the selected deployment.
• A degraded mode exists for material connectivity, provider, or local infrastructure failures.

• Infrastructure is selected before the workload and data flow are defined.
• A self-hosted deployment has no owner for patching, evaluation, serving, or recovery.
• Hybrid complexity is added without a workload boundary that requires it.
• The workflow has no degraded mode for lost connectivity or unavailable infrastructure.

Answer these questions for the workload before selecting a provider, model license, or deployment location.

• What data enters, leaves, persists, appears in logs, or enters an evaluation set?

• Who can administer each processing, storage, network, and support path?

• Which model capability and workflow latency are required for useful operation?

• Must any work continue during lost external connectivity?

• What is the degraded mode, and how is unfinished work reconciled?

• Who owns patches, versions, capacity, monitoring, evaluation, and incidents?

• Which changes require renewed security or operational review?

• Can records, prompts, evaluation cases, and interfaces move to another deployment?

• What is the total burden across infrastructure, providers, staff, and recovery?

• If the design is hybrid, which distinct boundary makes each path necessary?

A sound architecture fits the workload boundary and leaves its operating duties with people prepared to own them. No deployment location or license can compensate for missing access controls, change review, monitoring, or recovery.

Record the assumptions behind the choice and the conditions that would change it. Capability, providers, models, and internal staffing will move; a clear boundary makes the next decision easier to evaluate.

Continue with the complete resource library or read our implementation approach.