What engineers usually see
- •Need to know exact token usage per request
- •Billing shows total tokens but not per-request
- •Cannot identify token-heavy requests
- •Unable to optimize prompts for token efficiency
Why this is hard to debug
Provider dashboards show aggregate token usage. You can't drill down to individual requests. Receipts show per-request token usage with historical trends.
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"}]
)
print(f"Tokens: {response.usage}")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
- Input tokens
- Output tokens
- Total tokens
- Token efficiency vs similar requests
- Prompt optimization suggestions
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.
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.