Task-to-Model Optimization for Enterprise LLM Coding Assistants: A Data-Driven Framework for Cost-Optimal Routing
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Computer Science > Machine Learning
arXiv:2608.08528 (cs)
[Submitted on 9 Aug 2026]
Title:Task-to-Model Optimization for Enterprise LLM Coding Assistants: A Data-Driven Framework for Cost-Optimal Routing
Authors:Srinivasan Manoharan, Junhua Zhao, Fangbo Tu, Haifeng Wu, Jian Wan, Maliah Rajan M, Ashwin Hegde, Mithun Sasidharan, Kalyan Chakravarthi Podamekala
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Abstract:Enterprise AI coding assistants incur substantial inference spend, and naive token-cost minimization often fails to reduce end-to-end cost once retries, escalations, and developer wait time are included. We present Task-to-Model Optimization (T2MO), a data-driven methodology for optimizing model selection in production coding workflows. We treat each developer session as a task that can be discovered, classified, graded for difficulty, benchmarked in a production-like harness, and routed to the cheapest model able to complete it within quality and latency constraints. The framework is a nine-stage pipeline spanning telemetry instrumentation, taxonomy discovery, difficulty grading, benchmark construction, candidate evaluation, optimal mix derivation, forecasting and version planning, staged routing deployment, and continuous governance. Unlike token-centric routing rules, our objective is cost per completed task, with failure escalation priced in explicitly. We show that this expected-completion-cost objective weakly dominates token-cost minimization under escalation, and we derive the routing boundary, the minimum pass rate a cheaper model must reach on a given cell to be worth deploying. Decisions are organized as a two-level hierarchy of task category difficulty tier, and per-cell displacement opportunities are aggregated into a traffic-weighted savings waterfall that ranks replacement candidates by realized dollar impact. The framework supports developer guidance, spend forecasting, and a staged transition from static policies to shadow-mode classifiers, verified cascades, and ultimately an intelligent router. We describe the methodology, optimization objective, evaluation protocol, and governance loop in a form suitable for production deployment and future empirical study.
| Comments: | 11 pages, 1 figure, 2 tables |
| Subjects: | Machine Learning (cs.LG) |
| ACM classes: | C.4 |
| Cite as: | arXiv:2608.08528 [cs.LG] |
| (or arXiv:2608.08528v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.08528
arXiv-issued DOI via DataCite (pending registration)
|
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View a PDF of the paper titled Task-to-Model Optimization for Enterprise LLM Coding Assistants: A Data-Driven Framework for Cost-Optimal Routing, by Srinivasan Manoharan and 8 other authors
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