Where RedCrown sits next to your eval stack
RedCrown focuses on checking supported evidence against explicit requirements and making the result inspectable. The right fit depends on the input format, decision workflow, and operational tools your team needs. This page says what RedCrown does, what each of the tools it is most often compared with is positioned as, and how to decide.
The one-sentence version
RedCrown checks whether an AI feature still meets agreed requirements, and shows the evidence behind the decision: the cases, the scoring method, the rules with their pass, fail, or indeterminate results, the counts and the exclusions, and a shared report someone outside the team can open and inspect.
What each tool is positioned as
The two descriptions below are structural, taken from each vendor's own site as read in August 2026. They are not feature claims and they were not re-verified for this page; read the vendor's site before you decide.
| RedCrown | Braintrust | Confident AI (DeepEval) | |
|---|---|---|---|
| Positioned as | Regression checks and release evidence for AI features | An evaluation and observability platform for teams building AI applications | A hosted platform over DeepEval, an open-source LLM evaluation framework |
| Primary reader | The person who has to accept a delivery, review a change, or defend the choice later | The team building and iterating on the application | The team writing the evaluations |
| Open source | The MCP server. The CLI is published on PyPI. | SDK | DeepEval, the evaluation framework |
What RedCrown does, path by path
Each row is a path in the released product. The input you bring decides which evidence the record can carry.
| Path | You bring | You get | Limit |
|---|---|---|---|
| Score supplied outputs | Cases with references and the outputs your feature produced | A scored report per candidate with per-case receipts, and a private evidence file. Offline, no provider key, no model call. | RedCrown did not witness how the outputs were generated. Text in, text out. |
| Offline policy check | Two evidence files for the same feature plus a policy JSON | Pass, fail, or indeterminate per rule; exit 0, 2, or 1; the changed cases from the evidence file | Reads existing evidence only. Cost and latency rules stay indeterminate. |
| Compare harness runs | A journal directory per run with named checks per case | Per check: regressed, fixed, inconclusive, newly measured. Exit 1 on a regression, 2 on corpus drift. | Your runner executes the cases. An unmeasured check is never a pass or a fail. |
| Run an evaluation | A task, cases, references, and your provider keys | A ranked report with receipts, cost, and latency per candidate | Calls go to the providers on your keys. Provider charges are yours. |
| Accept a baseline | An eligible completed run in your workspace | A stored acceptance with history; later runs compared against it in the app | Not a deployment authorization. Refused for runs with errors, ungraded cases, or a blocking caveat. |
| Import a summary | Aggregate scores from another system | A run with summary scores, and only the receipts the payload carried | No evaluation identity, so no acceptance or comparison. Per-case receipts come from redcrown score, redcrown eval, or a harness export push. |
| Share a report | A completed run | A frozen public or unlisted page with the caveats it carried, revocable, with a signed attestation | Shares one run. The publish gate refuses blocking caveats; an override is recorded. |
Read the head-to-heads
RedCrown vs Braintrust →
An evaluation and observability platform beside a regression-check record. What each is for, and when to run both.
RedCrown vs Confident AI (DeepEval) →
An evaluation framework beside a regression-check record. Where a passing test ends and an inspectable record starts.
What "neutral" means here, and how it is structured
This is a statement about RedCrown only. It says nothing about any other vendor's terms, which you should read for yourself.
- RedCrown takes no payment, revenue share, or placement fee from any model provider.
- RedCrown takes no margin on the inference you run. Your keys, your accounts, your rates.
- The scoring methodology is pinned and versioned, and every report records the version that produced it.
- Every claim on a report carries per-case receipts, and optionally a signed attestation you can verify offline.
- A share link is refused while a blocking caveat stands. An owner can override that, and the override is written to the audit log.
Questions people ask before switching
What does RedCrown check, exactly?
Whether a candidate version of an AI feature meets the requirements you wrote down, on the cases you supplied, against a reference run. The requirements are a policy file: minimum cases, maximum error and ungraded rates, minimum quality, and maximum regression. Each rule reports pass, fail, or indeterminate, and the command exits with a code your pipeline can read. It does not tell you why a case changed, and nothing runs on a schedule.
Can I use RedCrown alongside Braintrust or DeepEval?
Yes. Keep the tool that produces your outputs or your test results. Bring the outputs to redcrown score for per-case receipts, or bring a test-harness journal to redcrown harness compare. An aggregate import from another system yields summary scores and only the receipts the payload carried; per-case receipts come from redcrown score, redcrown eval, or a harness export push.
Does RedCrown only work with LLMs?
No. RedCrown scores speech-to-text on word error rate, domain-term F1, and critical-term recall, and document extraction on field-level F1, on the same footing as language-model output. The two model-selection examples on the consultancies page are a clinical transcription comparison and a clinical note-generation comparison, each with its sample size stated.
What if I do not have labeled data?
Score the candidates against your current configuration's output instead of a labeled reference. That shows which candidates match what you ship today and what each would cost at your volume. It does not establish that the current output is correct.
Check one AI feature against something explicit.
Start with the synthetic example, offline, or bring your own cases and outputs. The app is free for one active AI feature. No sales call, no credit card.
Competitor descriptions on this page are structural, not feature-by-feature. They were last read on each vendor's own site in August 2026 and were not re-verified on 2026-09-17, when this page was last rewritten, so they are kept short and say only what each vendor positions itself as. Products change. Check theirs, then check ours.