Find the model your workflow can actually run.
Turn vague AI interest into a practical starting point: model class, cloud or self-hosted path, memory estimate and the next step.
The selector.
The best model depends on task type, data sensitivity, request volume, context length and available compute. A smaller local model can beat a premium API when the work is narrow, private and repetitive.
The selector is deliberately conservative. A model that fits in memory still needs workflow design, guardrails, data cleanup and testing before it touches real customer operations.
Model name
The model class we would start testing for your use case.
Run path
Cloud, local, or hybrid based on privacy, volume, and uptime.
Memory needed
Estimated memory for model weights, context, and runtime overhead.
Plain-English recommendation
Whether the plan is smooth, usable, tight, or unrealistic on the selected compute.
Related paths
Send the workflow, data source and hardware. We will scope the agent, model, hosting path and approval rules.