AI for Multi-LLM Routing & Arbitration

Route requests across multiple LLMs to balance quality, cost, and latency with policy-based arbitration, evaluation, and guardrails.

No single LLM is best for all tasks. Multi-LLM routing picks the right model per request using policies, evals, and budgets, while arbitration blends or selects outputs with safety filters.

LLM-as-a-judge scoring with safety and quality criteria.

Hybrid answers (merge/summarize) with citations.

Per-tenant policies; secrets redaction and PII filters.

live demo · related simulation● LIVE

Eval datasets and live feedback from users.

Policy engine for model selection and fallbacks.

Pre/post processors (chunking, redaction, formatting).

Frequently asked questions

What are offline and online evaluations, and how do drift and regression alerts relate to them?

Offline/online evals; drift and regression alerts.

How can feedback loops be used to retrain routing and judges within the system?

Feedback loops to retrain routing and judges.

What is the process of defining policies and evaluation sets, and how should models be integrated with a unified API?

Define policies and eval sets; integrate models with unified API.

How can I add a routing layer with logging, and what is shadow A/B testing used for in comparison to the baseline?

Add routing layer with logging; run shadow A/B to baseline.

▶ Try it live

Everything above runs in your browser — open Gradient Descent Visualiser and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Gradient Descent Visualiser simulation

AI for Multi-LLM Routing & Arbitration | ML Knowledge Hub