Unexpected OpenAI bill

What engineers usually see

  • •Monthly bill is much higher than expected
  • •Cannot identify which requests drove the cost
  • •No per-request cost breakdown
  • •Unable to attribute spend to specific features or users

Why this is hard to debug

OpenAI billing is aggregated and doesn't map to individual requests. You can't trace high costs to specific API calls or user actions. Receipts provide per-request cost attribution.

Minimal repro

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_OPENAI_KEY",
    base_url="https://aibadgr.com/v1"
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "test"}]
)

# Receipt includes estimated cost

This request routes through AI Badgr and returns a stable request ID that links to an execution record.

Note: AI Badgr is OpenAI-compatible and works as a drop-in proxy. No SDK changes required — only the base_url changes.

What a per-request execution record makes visible

  • Cost per request
  • Token usage breakdown (input vs output)
  • Model pricing applied
  • Cumulative spend over time
  • Cost attribution by API key or user

Run 1 request → get receipt

Change your base URL to https://aibadgr.com/v1 and run your request.

The response includes an X-Badgr-Request-Id header that links to a receipt showing latency, retries, tokens, cost, and failure stage for that specific execution.

Get API Key

Not the engineer?
Share this page with your dev and ask them to run one request through AI Badgr. That's all that's needed to get the receipt.

This kind of thing only makes sense when you can actually see what happened to a single request from start to finish, instead of trying to piece it together from scattered logs.