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Agent Infrastructure

Evaluating

Inferock Bench

Inferock Bench is a local proxy that sits between your applications and OpenAI, Anthropic, Gemini or OpenRouter and writes a receipt for every call: tokens, cost, timing, retries, empty responses you were billed for, and cache discounts at risk. The source is published under a license that converts to Apache 2.0 after two years.

The AIE Angle

Why Inferock Bench made the cut

Your model provider counts the tokens, sets the price, decides what counts as a failure and keeps the only detailed log. Inferock Bench is a small tool that gives you an independent record. Run it with one npx command on a laptop or server, point your application at the local address instead of the provider, and every call is logged with provider-reported usage, pricing evidence, status, latency and retry history. A set of detectors then flags the things that quietly cost money: responses that came back empty but were billed, refusals and truncations that cost as much as an answer, token recounts that do not match, duplicate request IDs, and prompt-cache discounts that may not have applied. The dashboard rolls that into four labeled numbers, what you spent, money loss it can tie to the bill, time lost waiting, and an invoice-check exposure, and it is careful not to add unlike things together. Your API keys stay on your machine; the proxy attaches them only to the provider request. The license is the Functional Source License: you can use and modify it freely but not sell a competing service, and each release converts to Apache 2.0 two years after it ships, so it is source-available today rather than open source in the strict sense. The maintainers sell a hosted version, Inferock Watch, Serve and Dedicated, and the benchmark grades receipts against a standard they wrote, which they disclose up front. For spend caps and routing rather than audit, see Helicone; for tracing what an agent did, Langfuse; for the cloud bill as a whole, Vantage. Inferock Bench is the one to run when you suspect the AI line on the invoice is wrong and want evidence.

Independently tested. No pay-to-play.

The AI Toolbox is curated by practitioners who use these tools in real business workflows. We don't accept payment for placement or favorable reviews.

5 Ways To Use It

Inferock Bench for business

  1. 1

    Check whether your monthly OpenAI or Anthropic bill matches what your application actually received.

  2. 2

    Find calls that were billed but returned nothing, or were cut off before the answer finished.

  3. 3

    Measure real latency, retries and token counts per call before committing an agent workload to a provider.

  4. 4

    Confirm that prompt-cache discounts you planned for are showing up in the usage data.

Common Questions

Inferock Bench FAQ

The questions business professionals most often ask about Inferock Bench.

What is Inferock Bench?+

Inferock Bench is a local proxy for metered model API traffic. Your application sends requests to it instead of the provider, and it records tokens, cost, timing, retries and failure evidence for every call, then renders receipts in a local dashboard.

Which providers does it measure?+

OpenAI, Anthropic, the Gemini Developer API, and a pinned set of OpenRouter endpoints covering models from Meta, DeepSeek, Mistral, Moonshot, Z.ai and Qwen. Other providers can be added but are not measured out of the box.

Is Inferock Bench open source?+

The proxy and CLI are published under the Functional Source License, which allows free use and modification but not a competing hosted service, and converts each release to Apache 2.0 two years after it ships. The @inferock/measure grading library is Apache 2.0 and the Inferock Standard spec is CC BY 4.0.

Do my API keys go to Inferock?+

No. Keys are saved locally with owner-only file permissions and are attached only to the requests the proxy forwards to your provider. Receipts stay on your machine unless you choose to share them. The maintainers do sell a hosted service, Inferock Watch, Serve and Dedicated, which is separate from the local benchmark.

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