What engineers usually see
- •OpenAI returns 429 with Retry-After header
- •Client may or may not respect the delay
- •Unclear if automatic retries will be attempted
- •Cannot track cost of failed vs retried requests
Why this is hard to debug
Rate limit headers don't always get logged or even respected by clients. So you can't tell if your app actually waited the right amount of time, how many retries happened, or if you got charged for tokens. Pretty frustrating when you're trying to debug.
Minimal repro
curl https://aibadgr.com/v1/chat/completions \
-H "Authorization: Bearer YOUR_OPENAI_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "test"}]
}'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
- Retry-After value from provider
- Whether client respected the delay
- Number of retry attempts
- Total latency including retries
- Cost per attempt (even 429s can incur charges)
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.