Launching today
Inferock Bench
An independent receipt for every LLM API call
560 followers
An independent receipt for every LLM API call
560 followers
Inferock-bench is a local proxy that sits between your app and OpenAI, Anthropic, Gemini, or OpenRouter shaped calls. It captures per-call token usage, failures, and retries, then generates an independent receipt showing what you were billed and how much you're actually overpaying for.
What I really want to know is how this handles historical data. Can I feed it a month of past logs and get a retroactive receipt or is it strictly forward-looking from install? I ask because the overspending I'm most curious about already happened and I'd love a way to audit it after the fact.
Inferock Bench
@kimberly_west really good question. Today it works by sitting in front of your live traffic, so the receipts start from the moment you point your SDK at it, it can't vouch for calls it never saw.
This is relevant to a problem I've had for months. I run agents that call out to multiple models depending on the task complexity and every so often the bill jumps in a way I can't explain from usage alone. If this can pinpoint whether that's failed calls, redundant retries or just legitimate scaling, I'd finally have an answer instead of a guess.
Inferock Bench
@yolanda_c_schneider Multi model agent setups are where this gets most interesting. Every call gets its own record whichever provider it went to, so a jump breaks down into failed calls, retries, etc. Would love to hear what you find.
Inferock Bench
Hey PH
We built inferock-bench because we kept paying for AI answers that died mid sentence, and nobody could tell us where the money went.
Providers give you totals. They don't give you the per call receipt you'd need to prove which answer broke, which retry ran, or which token count changed. The company that charges you also decides what counts as a failure and keeps the only detailed records.
inferock-bench runs locally as a proxy in front of OpenAI, Anthropic, Gemini, OpenRouter shaped calls. Point your existing SDK at it (change two settings: apiKey and baseURL), and it captures every call as an independent, per call record. Your provider key never touches our servers, it's used locally only, attached to provider requests.
What it catches:
- Answers cut off mid stream that still got billed
- Empty replies with billed tokens attached
- Token counts that don't match visible output
- Retries that may have silently doubled a charge
- Cache discounts you may be missing on your invoice
Every run reports a receipt: spend observed, bill-bounded money loss, time loss, and a separate "invoice-check exposure" line that never gets summed into money loss, because we don't want a louder headline at the cost of a weaker claim.
Run it in about a minute: npx inferock-bench
Its open source (FSL-1.1-Apache-2.0, converts to Apache-2.0 in 2 years).
Question for this community: has anyone here actually disputed an AI provider bill and gotten a credit? What worked?
@bharath_koneti @tehreem_fatima5 Appreciate you actually answering the question, that matches what we kept hearing from others too. Without per request records the conversation with a provider ends pretty fast. You walk in with evidence instead of a feeling.
Overpaying for failed calls has quietly cost me more than I'd like to admit. I've had timeouts that still consumed tokens on the provider's side and no clean way to catch that pattern until my bill arrived. If this surfaces failure-related overspend specifically, not just total usage, I'd trust the numbers more than any provider dashboard.
@jusiy_waller Yeah, It does track that separately btw, failed calls get their own cost line, so you can see what the failures actually cost you. Would honestly love to know what it finds on your traffic.
This solves a problem I didn't know I could solve, I always assumed API billing discrepancies were just the cost of doing business. Turns out I was wrong.
Thanks @itohan_blessing_eigbadon , honestly, this is exactly what we told ourselves for months, until we actually looked at the per call receipts and couldn't unsee it. Really glad this clicked for you. Pls run it on your own traffic, come back and tell us what your receipts show, genuinely curious!
I like that it works as a local proxy. Keeping billing and usage data on the developer's machine feels like a thoughtful design choice.
@awesome_america Thank you! That part was non negotiable for us, it's your spend and your keys, so the record should live on your machine, not on ours.
The independent receipt idea is genuinely useful. LLM costs are still surprisingly hard to audit at the individual request level.
@ashir_murtaza1 thank you! That's exactly the gap we kept running into, you can see the total, but never the audit at the individual request level. Glad it resonates.