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Mostafa Hanafy
Mostafa Hanafy

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Prevent Token Cost Spikes in LLM Apps with Token Budget Guard

#node #ai #webdev #javascript

When building LLM features, token usage directly affects three things:

  • cost
  • latency
  • reliability

But many applications treat token usage as an afterthought until prompts grow unexpectedly or API costs spike.

I recently released an open-source utility called Token Budget Guard to help solve this.

The idea is simple: enforce token limits before making expensive LLM API calls.

Instead of sending a request blindly to a provider, you can apply guardrails such as:

  • fail fast if the request exceeds a limit
  • automatically trim context
  • warn when the request goes over budget

Example:

import { withTokenBudget } from "token-budget-guard";

await withTokenBudget({
  maxTokens: 2000,
  prompt,
  context,
  expectedOutputTokens: 200,
  strategy: "trim_context",
  call: async ({ prompt, context }) => aiClient(prompt, context),
});
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This helps keep AI systems predictable as prompts and context grow over time.

The library also includes provider adapters for:

  • OpenAI
  • Anthropic
  • Gemini
  • AWS Bedrock
  • Azure OpenAI
  • Cohere

It’s intentionally small and focused so it can fit easily into existing AI pipelines.

GitHub
https://github.com/mostafasayed/token-budget-guard
npm
https://www.npmjs.com/package/token-budget-guard

If you're building production AI systems, I'm curious how you're managing token budgets today.

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