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Developer Builds AI Router to Cut LLM Costs by Matching Tasks to Right Models

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A developer created Maestro AI, a model-routing middleware layer designed to reduce costs when using coding agents like Cursor and Claude Code. The tool classifies each subtask — such as summarization, refactoring, or system design — and dispatches it to the most cost-appropriate model, from local Llama instances to premium Claude Sonnet. Routing decisions rely on deterministic rules based on keywords, context size, and task complexity, with automatic fallback if a chosen model tier is unavailable. Early development revealed false positives, such as simple UI tasks being misrouted to premium models due to broad keyword matching, which were resolved through tighter pattern definitions. The system also supports session-level budget caps and tier limits to prevent unexpected cost overruns during long agent sessions.

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