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Folium framework
Runtime placement is a business decision before it is a model decision.
This framework helps teams decide where AI work belongs by privacy, latency, cost, source location, action risk, supportability, and ownership.
Why it matters
This framework gives the buyer a language for the decision.
AI becomes unprofitable when every workload is routed through the most expensive or exposed path. Placement should follow the job.
How to use it
01
Classify workload
Name sensitivity, consequence, latency, repetition, source location, and scale.
02
Compare routes
Evaluate cloud API, private endpoint, local model, hybrid, batch, or blocked-until-ready.
03
Create route contract
Document fallback, logging, owner, cost review, and support expectations.
Operating rubric
What weak and strong states look like.
Privacy
Weak state All work leaves the business by default.
Target state Sensitive work has local, private, redacted, or blocked routes.
Cost
Weak state Every task uses the same model class.
Target state Simple tasks use smaller, cached, local, structured, or non-AI routes where useful.
Latency
Weak state The route ignores operational timing.
Target state Real-time and batch paths are separated.
Fallback
Weak state Provider failure stops the workflow.
Target state Fallback and degraded mode are defined.
Decision matrix
Turn signals into action and ownership.
Signal
Action
Owner
Sensitive customer data
Prefer local, private, redacted, or blocked route
Data owner
High volume repeated work
Review smaller, cached, or local route
AI operations owner
Best quality requires frontier model
Use cloud route with controls
Workflow owner
Useful outputs
What the framework should leave behind.
Placement decision tree
Route contract
Cost and privacy comparison
Fallback plan
Monitoring signals
Related paths
Move from framework to Folium delivery.
FAQ
How buyers should read the framework.
Is local AI always better?
No. Local AI is valuable for the right workload. Some tasks still belong in cloud, private endpoints, hybrid routes, or non-AI software.
Can CPU-friendly AI be profitable?
Yes, when the task is focused, the model is right-sized, and the workflow does not need broad frontier-model capability.
Start here
Use the framework, then build the first controlled lane.
Folium can translate the score, matrix, or map into workflow scope, system design, data boundary, launch gate, and operating handoff.
Common questions
Questions this page answers.
Is local AI always better?
No. Local AI is valuable for the right workload. Some tasks still belong in cloud, private endpoints, hybrid routes, or non-AI software.
Can CPU-friendly AI be profitable?
Yes, when the task is focused, the model is right-sized, and the workflow does not need broad frontier-model capability.