STACKELIER
Find my stack
Private AI by profession

Private AI for Developers

Local coding assistants and agents have different runtime, context, memory and hardware demands. STACKELIER evaluates the complete configuration instead of assuming model parameter count alone determines fit.

Find my stack →How STACKELIER scores fit
Workflows

Private AI workflows for Developers

The useful configuration depends on what the work actually contains, not only on the profession label.

Use case

Local coding assistance

Keep source-code assistance on a controlled execution path while matching the model and runtime to the developer workstation.

Use case

Agentic coding workflows

Evaluate agents as complete systems, including the model endpoint, tool execution path, context needs and hardware requirements.

Use case

Private codebase analysis

Analyze proprietary repositories without assuming that a local editor extension also means local inference.

Decision priorities

What changes the recommendation

Priority 01

Source-code data path

Use this as a constraint when comparing reference stacks and when completing the Finder.

Priority 02

Context and repository size

Use this as a constraint when comparing reference stacks and when completing the Finder.

Priority 03

Runtime and accelerator compatibility

Use this as a constraint when comparing reference stacks and when completing the Finder.

Privacy boundary

Self-hosted is not enough for Developers.

Source code privacy depends on the complete execution path: application, data layer and inference endpoint. External inference is excluded when the requirement is that nothing leaves the controlled machine or network.

Application

Where does the interface or service run?

Data

Where are documents, embeddings and indexes stored?

Inference

Where does the model actually process the request?

Evidence

Which authoritative sources support those claims?

Verified baseline

Reference stacks for Developers

These are verified launch configurations already used by the deterministic Finder. They are reference profiles, not universal recommendations.

Hard constraints win: if a mandatory privacy or hardware requirement is incompatible, the stack is excluded rather than merely ranked lower.

Next decision

Move from research to a configuration.

Use the guides for the architecture questions, compare verified baselines side by side, or run the Finder with your exact privacy and hardware constraints.

Related guide

Private AI Assistants

Understand the decision boundary before choosing a specific stack.

Related guide

Local AI by Hardware

Understand the decision boundary before choosing a specific stack.

Compare

Compare verified Developers stacks

Open the relevant reference configurations side by side.

Personalize

Run the Stack Finder

Apply your exact task, privacy requirement and hardware instead of relying on a generic baseline.

FAQ

Questions about private AI for Developers

What makes a coding assistant private?

Privacy depends on where the editor or application runs, what repository data is transmitted or stored, and where the model performs inference. A local extension alone is not sufficient evidence.

Are local coding agents more demanding than chat assistants?

They can be because agentic workflows may need larger context, repeated inference and tool interaction. STACKELIER therefore evaluates workload, runtime and hardware together rather than using parameter count alone.

Can the same coding model fit differently across runtimes?

Yes. Quantization, runtime overhead, context and offload behavior can change memory use and practical performance, so hardware fit belongs to the complete configuration.