Configuration-aware
Application location, data location and inference location are evaluated separately.
Private AI decision engineThe right AI stack.
The right AI stack.
For your work, data and hardware.
STACKELIER matches professional use cases to evidence-backed private AI configurations. It checks the task, privacy boundary and hardware before it recommends anything.
50seed tools
30models
10runtimes
15/15verified reference stacks
Why STACKELIER exists
“Self-hosted” is not a privacy answer.
A self-hosted interface can still send inference to a cloud API. A powerful local model can still be a poor choice for your hardware. Stackelier evaluates the configuration, not the marketing label.
Actually runnable
Quantization, RAM/VRAM, context and runtime matter before a model earns a hardware fit.
Claims need sources
Privacy-critical claims are tied to authoritative documentation and revalidated when material changes occur.
The Stackelier Method
Task + Privacy + Hardware + Evidence = Fit
Task
What must the AI actually accomplish?
Privacy
Where may your data, storage and inference occur?
Hardware
What can your workstation or server realistically run?
Evidence
Can each important claim be verified and kept current?
Ranking integrity: organic Match Scores cannot be purchased.