Why llm-spend-guard?

The Problem

A single runaway loop, an uncapped user session, or one oversized prompt can burn through your entire LLM budget in minutes. There is no built-in way to set spending limits across OpenAI, Anthropic, or Gemini SDKs.

The Solution

llm-spend-guard wraps your existing LLM SDK calls and enforces token budgets before any request is sent to the API. If a request would exceed your budget, it gets blocked instantly — no money wasted.

Key Benefits

• Pre-request blocking — Stops overspending before the API call, not after
• Multi-provider — Single API for OpenAI, Anthropic Claude, and Google Gemini
• Multi-scope budgets — Global, per-user, per-session, and per-route limits
• Zero config — Works with 3 lines of code, no infrastructure needed
• Production-ready — Redis storage, Express/Next.js middleware, TypeScript-first
• Lightweight — 18.6KB bundle, zero runtime dependencies beyond tiktoken

How It Works

Your Code --> llm-spend-guard --> LLM API (OpenAI / Anthropic / Gemini)
                  |
                  |-- 1. Estimates tokens BEFORE the request
                  |-- 2. Checks all budget scopes (global, user, session, route)
                  |-- 3. If over budget --> BLOCKS the request
                  |-- 4. If auto-truncate enabled --> trims prompt to fit
                  |-- 5. Sends request to LLM API
                  |-- 6. Records actual token usage from response
                  |-- 7. Fires alert callbacks at 50%, 80%, 100% thresholds

Who Is This For?

• SaaS builders who need per-user token limits
• AI agent developers who want to cap runaway chains
• Backend teams protecting production LLM endpoints
• Solo developers who want to avoid surprise bills