[ implementation · decision framework ]
Choose the AI boundary from the workload outward.
There is no universally “sovereign,” private, cheap, or fast AI deployment. A useful choice is one that meets a defined task and quality threshold with a visible data flow, acceptable failure modes, and an accountable owner.
Privacy and data flow
Local processing can reduce a particular transfer to a remote service. It does not settle retention, telemetry, device access, backups, model updates, or who can view outputs. Draw the data flow before making a privacy claim.
Capability and quality
Evaluate the task-specific quality threshold—not a model leaderboard. Larger remote services may offer broader reasoning, retrieval, or multimodal capability; compact local models may be sufficient for classification, extraction, assistance, or constrained generation.
Cost and ownership
Cloud costs are usually variable and operational; local execution shifts cost toward supported hardware, integration, model distribution, battery or power, and support. Price the full lifecycle and a growth case, not just a demo.
Latency and availability
Local execution can avoid network round trips, while remote systems can still be fast on good networks. Measure end-to-end response time, including retrieval, retries, queues, and first-use model download.
Resilience and control
Offline capability can improve continuity for a bounded local task. It does not eliminate dependence on operating systems, chip supply, app stores, model publishers, or other services. Identify the remaining dependencies and a fallback.
Device requirements
Model memory, storage, acceleration support, thermal behavior, battery, operating-system version, accessibility, and fleet variation determine whether a local promise works in practice. Test the least capable supported device.
A six-question gate
- What specific decision or action will this system improve—and what human remains responsible?
- What inputs, outputs, metadata, and logs move where, for how long, and under whose authority?
- What minimum quality, response time, and availability are acceptable for this use?
- Which devices and networks must work, including the weakest supported case?
- What happens when the model, network, vendor, or device fails?
- What will be measured before expansion: error, escalation, cost, latency, support burden, and unintended data flow?
Local first
Use when a bounded task works on real target hardware and gains materially from local handling, offline operation, or responsive interaction.
Cloud first
Use when shared context, centralized operations, scale, or capability is necessary and the data and service dependency are justified.
Hybrid
Keep capture, filtering, redaction, or bounded inference local; escalate only authorized, necessary information for remote capability.
Start with the audience, then run a bounded pilot
[ Browser-local planner ]
Turn these conditions into a reviewable planning record.
The AI Boundary Planner makes the chosen inputs, recommendation logic, testing gaps, and limits explicit. It does not rank vendors or certify privacy, security, cost, or performance.
Open AI Boundary Planner →[ Organization readiness ]
Identify the operating evidence needed before you scale.
The Architecture Readiness Assessment inventories data, devices, model lifecycle, access, outage fallback, and accountability, then produces a self-reported gap register and implementation sequence. It is not a security assessment or certification.
Open Architecture Readiness Assessment →[ Reference architectures ]
Start from a bounded pattern, then test it in your context.
Explore concrete patterns for field capture, school accessibility support, and internal approved-document search. Each includes a data flow, assumptions, measurements, failure path, and sources—rather than a claim that it is right for every organization.
Browse reference architectures →[ MPS implementation library ]
Choose a boundary, then test it.
Decision framework
Compare privacy, capability, cost, latency, resilience, and device fit.
On-device vs cloud
The canonical workload-level deployment comparison.
Individuals
Choose a bounded, reversible personal workflow.
Schools
Plan classroom use with student-data and access boundaries.
Small organizations
Start with a managed, testable operational use case.
Developers
Build and measure a local, cloud, or hybrid boundary.
Glossary
Plain-language deployment and provenance terms.
Methodology & sources
What this library measures, assumes, and does not claim.
For the workload comparison behind this framework, read on-device AI vs cloud AI. For claim-review workflow rather than architecture selection, use the separate MPS Preflight.