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How to fall back to a free local model when budget runs low with Fastllm Proxy

Use budget rules so users past 80 percent of budget get the local model instead of an error.

Short answer: Set budgets per user or team in the UI; then add a rule with min_budget_used_percent 80 targeting the local model; Label batch work with the x-fastllm-tier header to route it cheaply. All steps.

Who it is forA team lead giving developers LLM access under a monthly budget.

The problemHard budget cutoffs stop work with payment errors.

The outcomeGraceful downgrade to a local model once spend crosses a threshold.

What you need first

  • Docker and Docker Compose
  • At least one inference backend or provider key

Steps

  1. Set budgets per user or team in the UI
  2. Add a rule with min_budget_used_percent 80 targeting the local model
  3. Label batch work with the x-fastllm-tier header to route it cheaply
  4. Watch budget usage in the UI

Prompt for your agent

In fastllm-proxy, add a rule routing requests to <local model> once min_budget_used_percent reaches 80, then simulate a user over that threshold. Confirm their requests hit the local model instead of returning 402.

Caveat. Budget and RBAC checks run against an in-memory snapshot, so changes apply once the snapshot refreshes.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for azrtydxb/Fastllm-proxy and "Fall back to a free local model when budget runs low"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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