plan-hosted-run

What problem does it solve?

Use when a developer wants to run a hosted job — a fine-tune, an RL (reinforcement-learning) run, batch inference, or large-scale trajectory generation — and asks where, how long, and how much. "What would it cost to fine-tune this", "which provider should I use for this RL run", "is this cheaper locally or in the cloud". Estimates and routes; never spends.

Core Features & Use Cases

• Plan hosting strategy for ML workloads across local and cloud environments with transparent cost and time estimates.
• Route to the provider whose strengths fit the job type, scale, and data-residency needs, with citations to live pricing and quotas.
• Produce a concise handoff payload including inputs, results, and recommended next actions (training, serving, or data handling).

Quick Start

Describe your hosted job (type, data size, and duration) and I will estimate wall-clock time, cloud spend, and the recommended provider.