Deterministic cost control for probabilistic inference
Higher accuracy than the most expensive model.
At a fraction of the cost.
Physics-grounded adaptive fidelity routing for LLM APIs. No training data required. No heuristics. Deploys in your AWS account in minutes.
Empirical Results
MATH benchmark, mixed difficulty, exact-match scoring
| Configuration | Accuracy | Relative Cost | vs InfraNistic |
|---|---|---|---|
| Small model only | 60% | 1.0× | −6 pts accuracy |
| Large model only | 62% | 10.0× | Same cost, worse accuracy |
| Most expensive model | 58% | 50.0× | 5× the cost, worse accuracy |
| InfraNistic | 66–68% | 9.6× | Beats all. 81% cheaper than top model. |
How It Works
Query → [Surrogate Model] → n samples at temperature
↓
[Uncertainty Gate] ← physics-derived signal
↓
signal ≤ threshold → return surrogate answer (cheap path)
signal > threshold → escalate to refiner (expensive path)
No training data. No classifier. No domain-specific tuning.
Works on any model pair from day one.
↓
[Uncertainty Gate] ← physics-derived signal
↓
signal ≤ threshold → return surrogate answer (cheap path)
signal > threshold → escalate to refiner (expensive path)
No training data. No classifier. No domain-specific tuning.
Works on any model pair from day one.
First Principles
Training-Free
No historical data needed. Works on a new model the day it's released. No domain-specific tuning required.
Model-Agnostic
Any model pair, any provider. The uncertainty signal is computed from response agreement, not model internals.
Mathematically Grounded
Built on multi-level Monte Carlo, Galerkin residuals, and ensemble uncertainty quantification. Not heuristics.
Available on AWS Marketplace
Deploys as a Lambda in your account. You keep your data. We meter usage.
$0.50 per 1,000 queries. First 50,000 queries free every month.