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ML Compass: Navigating Capability, Cost, and Compliance Trade-offs in AI Model Deployment

Vassilis Digalakis, Ramayya Krishnan, Gonzalo Martin Fernandez, Agni Orfanoudaki

TL;DR

ML Compass reframes AI model deployment as constrained optimization over a technology frontier that binds capability profiles to deployment costs. By estimating an empirical frontier and learning context dependent deployment utility, it generates deployment aware recommendations that can diverge from capability led leaderboards. Theoretical results identify a three regime structure where some capability dimensions bind to compliance minimas, some saturate, and the rest balance via frontier curvature. A scalable pipeline extracts low dimensional measures, estimates the frontier, learns utility from interaction data, and outputs targeted capability cost profiles and model recommendations. Case studies on PRISM and HealthBench demonstrate deployment aware leaderboards and recommendations that reflect realistic trade offs between capability, cost, and safety.

Abstract

We study how organizations should select among competing AI models when user utility, deployment costs, and compliance requirements jointly matter. Widely used capability leaderboards do not translate directly into deployment decisions, creating a capability -- deployment gap; to bridge it, we take a systems-level view in which model choice is tied to application outcomes, operating constraints, and a capability-cost frontier. We develop ML Compass, a framework that treats model selection as constrained optimization over this frontier. On the theory side, we characterize optimal model configurations under a parametric frontier and show a three-regime structure in optimal internal measures: some dimensions are pinned at compliance minima, some saturate at maximum levels, and the remainder take interior values governed by frontier curvature. We derive comparative statics that quantify how budget changes, regulatory tightening, and technological progress propagate across capability dimensions and costs. On the implementation side, we propose a pipeline that (i) extracts low-dimensional internal measures from heterogeneous model descriptors, (ii) estimates an empirical frontier from capability and cost data, (iii) learns a user- or task-specific utility function from interaction outcome data, and (iv) uses these components to target capability-cost profiles and recommend models. We validate ML Compass with two case studies: a general-purpose conversational setting using the PRISM Alignment dataset and a healthcare setting using a custom dataset we build using HealthBench. In both environments, our framework produces recommendations -- and deployment-aware leaderboards based on predicted deployment value under constraints -- that can differ materially from capability-only rankings, and clarifies how trade-offs between capability, cost, and safety shape optimal model choice.

ML Compass: Navigating Capability, Cost, and Compliance Trade-offs in AI Model Deployment

TL;DR

ML Compass reframes AI model deployment as constrained optimization over a technology frontier that binds capability profiles to deployment costs. By estimating an empirical frontier and learning context dependent deployment utility, it generates deployment aware recommendations that can diverge from capability led leaderboards. Theoretical results identify a three regime structure where some capability dimensions bind to compliance minimas, some saturate, and the rest balance via frontier curvature. A scalable pipeline extracts low dimensional measures, estimates the frontier, learns utility from interaction data, and outputs targeted capability cost profiles and model recommendations. Case studies on PRISM and HealthBench demonstrate deployment aware leaderboards and recommendations that reflect realistic trade offs between capability, cost, and safety.

Abstract

We study how organizations should select among competing AI models when user utility, deployment costs, and compliance requirements jointly matter. Widely used capability leaderboards do not translate directly into deployment decisions, creating a capability -- deployment gap; to bridge it, we take a systems-level view in which model choice is tied to application outcomes, operating constraints, and a capability-cost frontier. We develop ML Compass, a framework that treats model selection as constrained optimization over this frontier. On the theory side, we characterize optimal model configurations under a parametric frontier and show a three-regime structure in optimal internal measures: some dimensions are pinned at compliance minima, some saturate at maximum levels, and the remainder take interior values governed by frontier curvature. We derive comparative statics that quantify how budget changes, regulatory tightening, and technological progress propagate across capability dimensions and costs. On the implementation side, we propose a pipeline that (i) extracts low-dimensional internal measures from heterogeneous model descriptors, (ii) estimates an empirical frontier from capability and cost data, (iii) learns a user- or task-specific utility function from interaction outcome data, and (iv) uses these components to target capability-cost profiles and recommend models. We validate ML Compass with two case studies: a general-purpose conversational setting using the PRISM Alignment dataset and a healthcare setting using a custom dataset we build using HealthBench. In both environments, our framework produces recommendations -- and deployment-aware leaderboards based on predicted deployment value under constraints -- that can differ materially from capability-only rankings, and clarifies how trade-offs between capability, cost, and safety shape optimal model choice.
Paper Structure (86 sections, 7 theorems, 78 equations, 11 figures, 26 tables)

This paper contains 86 sections, 7 theorems, 78 equations, 11 figures, 26 tables.

Table of Contents

  1. Introduction
  2. Contributions & Outline
  3. Related Literature
  4. Technology selection and adoption.
  5. AI evaluation.
  6. Multi-criteria decision making.
  7. Problem and Model Formulation
  8. Stakeholders and their Evaluation Functions
  9. The Technological Frontier
  10. Optimization Formulation
  11. Structural Analysis
  12. Assumptions and Stylized Model
  13. Optimal Solution Characterization
  14. Comparative Statics
  15. Managerial Takeaways
  16. ...and 71 more sections

Key Result

Theorem 1

Under Assumptions assump:utility--assump:nondeg, any optimal solution $(x^*, c^*)$ to Problem eq:stylized-problem satisfies, for each $i\in[I]$, where $\mu_0 > 0$ is the Lagrange multiplier associated with the frontier constraint. If in addition $b>1$, then the optimal solution $(x^*,c^*)$ is unique.

Figures (11)

  • Figure 1: MLC pipeline.
  • Figure 2: Measure extraction: factor loadings for capability data.
  • Figure 3: Frontier estimation.
  • Figure 4: Optimal targets and recommended models in internal-measure space.
  • Figure EC.1: Correlation heatmap of model-internal metrics provided in the PRISM dataset. This set of variables was augmented a posteriori with two additional benchmark variables, MMLU and MTEB, obtained from Hugging Face. The high correlations between the PRISM-derived model performance measures motivates the Measure Extraction step of our pipeline.
  • ...and 6 more figures

Theorems & Definitions (14)

  • Theorem 1: Optimal Solution Characterization
  • Proposition 1: Budget Sensitivity Analysis
  • Proposition 2: Regulatory stringency
  • Proposition 3: Technological Progress
  • Lemma EC.1: Convexity
  • proof
  • Lemma EC.2: Slater’s condition
  • proof
  • Lemma EC.3: Frontier Constraint is Binding
  • proof
  • ...and 4 more