SilkRouter

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SilkRouter guide

LLM cost optimization without rebuilding your AI stack

SilkRouter helps teams optimize LLM spend by centralizing model routing, usage monitoring, prepaid credits, and API-key controls.

How it works

One AI infrastructure control platform, explicit controls, measurable usage

Cost optimization works best when teams measure traffic first, route routine work to suitable models, cap outputs, and review failures that waste requests.

A lower model price is not enough. The operating system around prompts, retries, fallbacks, and usage review determines whether costs actually improve.

Control layer
Keys, routing, balance, reports
Migration check
SDK, model IDs, streaming
First test
One workflow and one key
Benefits

Why teams evaluate this path

Route by workflow value and complexity

Estimate savings before switching defaults

Cap funded exposure with prepaid credits

Spot waste through usage visibility

Decision guide

What to check before production traffic

Compatibility

Confirm SDK behavior, base URL setup, model IDs, streaming, and error handling before moving production traffic.

Operating controls

Check how API keys, balance, request logs, low-balance behavior, and offboarding work after launch.

Reporting workflow

Make sure the usage view answers finance, support, agency, or customer questions without rebuilding data manually.

FAQ

Questions buyers ask before switching

Use these answers to decide what to test, what to keep, and what not to assume before moving production traffic.

What is the safest first optimization?

Measure the highest-volume routes, set output limits, and test cheaper models only where quality remains acceptable.

Can fallbacks increase cost?

Yes. Retry loops and expensive fallback models can raise spend, so cap retries and monitor fallback rate.