Cut 20–50% of your LLM spend without changing your app code.
Modelux routes simple requests to cheaper models, keeps complex requests on premium ones, and shows you the savings before you switch. Start with the typical-team defaults below, then tune the sliders to match your stack.
Your current LLM bill across OpenAI, Anthropic, Google, and others.
Most teams land between 40–80%. Think summarization, classification, formatting, and many retrieval-heavy calls.
advanced — average savings on each downgraded request
Default: 80%. This assumes a meaningful gap between expensive frontier models and cheaper alternatives like GPT-4o-mini, Claude Haiku, or Gemini Flash. Adjust it if your provider mix is tighter.
A team spending $10,000/mo on LLMs usually has 40–60% of traffic that can move to cheaper models without hurting quality. That alone can create a clear monthly savings story.
Adjust only three inputs: what you spend now, how much traffic could downgrade, and how much cheaper those downgraded calls are. The result is the money you keep after Modelux cost.
methodology: Net savings = (monthly spend × downgrade % × per-request cost reduction) − modelux plan cost. Plan is auto-selected from your spend band. Real savings depend on prompt mix, model quality requirements, and provider pricing, so this is best used as a conservative estimate for an internal buying conversation. We do not store the inputs.
If the savings are real, the next step should be easy.
Keep your provider relationships, keep your existing app flow, and validate the routing policy on real traffic before you commit.
Start validating savings