LLM Routing by Task, Cost, Latency and Data Risk

One model should not handle every job. Coding may need strong reasoning. Support may need speed. Extraction needs a strict format. Private data may need an approved provider and region.

Good routing starts with the task, not the model name.

What an LLM router does

A router reads facts about a request. It sends the request to an approved path. It can block a request, remove private data, try a safe backup, or ask a person to review the result.

Axiom Studio's LLM Gateway offers one endpoint across 18+ providers, with routing, failover, policy controls, and observability.

The safe flow

Name the task.
Find the data class.
Set quality, time, and cost needs.
Pick an approved route.
Verify the result.
Record the choice.

Do not let cost rules override data rules.

Start with four task groups

Coding covers code, tests, and bug checks. Support covers replies and summaries. Extraction turns documents into set fields. Sensitive work has personal, health, payment, or secret data.

Policy example

task: code_review data_class: internal quality_floor: high latency_budget_ms: 12000 max_cost_usd: 0.08 allowed_routes: - primary_reasoning_model fallback: - approved_backup_model on_policy_failure: human_review

This is a design example. Map stable route names to real providers in one place.

Use a scorecard

Check	Question	Pass rule
Quality	Does it finish with proof?	Meets the task test
Cost	What is the full cost?	Under the limit
Speed	Does it finish in time?	Under the limit
Data	Is the route allowed?	Matches policy

Count output tokens, cache work, retries, tool calls, and review time.

Test before launch

Build a small set of real tasks with private details removed. Give each task an answer key. For code, require tests. For extraction, compare every field. For support, require the right source.

Run the same set on each approved route. Do not claim a winner from one prompt.

Fallback flow

Primary times out → check data policy → try approved backup → verify output → ask a person if the check fails.

Limit retries. Never send data to a new provider just because the first one failed.

Open-weight models

Open-weight models can give more control over where data runs. Hosting adds capacity, updates, security, monitoring, and on-call work. Compare full cost, including idle compute and failures.

Log the task group, data class, route, policy version, tokens, latency, retries, cost, and check result. Do not store raw prompts with secrets.

The NIST Generative AI Profile says teams should document controls and monitor third-party AI resources.