RedCrown vs Confident AI (DeepEval)
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. DeepEval is an open-source framework for writing LLM evaluations as tests, and Confident AI is the hosted platform over it. RedCrown is a regression-check record: it checks a candidate against written requirements and a reference run, and keeps the evidence behind the result.
The short version
A test framework tells the team that wrote the tests whether an output passes the metrics they defined. RedCrown takes the outputs, or a test-harness journal, checks them against the requirements you wrote down, and produces a record someone who did not write the tests can open and inspect: the cases, the counts, the exclusions, the caveats, the signature.
What each is positioned as
Structural descriptions only, from each vendor's own site as read in August 2026, not re-verified for this page.
| RedCrown | Confident AI (DeepEval) | |
|---|---|---|
| Positioned as | Regression checks and release evidence for AI features | A hosted platform over DeepEval, an open-source LLM evaluation framework |
| Primary reader | The person who accepts the delivery, reviews the change, or defends the choice | The team writing the evaluations |
| Open source | The MCP server. The CLI is published on PyPI. | DeepEval, the evaluation framework |
| What you end up with | A record: the cases, the scoring method, the rules with pass, fail, or indeterminate, the counts and exclusions, the caveats, a share link, a signed attestation | Read their site for the current list |
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. |
Pick by the question you are asking
DeepEval / Confident AI fits when
- You want LLM evaluations written as tests, owned by engineers, as they position it.
- You want an open-source framework you can read and extend yourself.
- The consumer of the result is the team that wrote the tests.
RedCrown fits when
- The result has to be handed to a clinician, an auditor, a client, or a buyer, and inspected case by case.
- You have requirements to write down and a reference run to check a candidate against.
- You need a domain expert to judge outputs without knowing which candidate produced them.
- Some candidates are not LLMs at all.
- You need the record to stay checkable later, with a re-run on demand when a provider or a price changes.
On the circularity problem
Any tool that uses an LLM to grade an LLM has to answer the same objection. RedCrown's answer is to not depend on it. Score against your own labeled references where you have them. Where you do not, score candidates against your current configuration's output, which shows what matches today's behavior, not that today's behavior is correct. Where a judge is the right tool, it is pinned and versioned, it can run as a panel with the median taken, and every verdict links to the receipt it was based on. Fixed settings and recorded versions help make evaluations more comparable; judge scores can still vary between runs. When the call is a matter of expert judgment, send it to a person who votes A against B without seeing which candidate is which.
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
Is RedCrown open source like DeepEval?
Partly. The RedCrown MCP server is open source on GitHub, and the CLI is on PyPI, so you can score, check, and run an evaluation locally with your own keys. The hosted record, the shared report pages, the reviewer links, and the baseline acceptance are the paid product. DeepEval is an open-source evaluation framework with a hosted platform over it.
Does RedCrown use LLM judges?
It can, but it does not have to. Score against your own labeled references where you have labels. Score candidates against your current configuration's output where you do not. Use a pinned, versioned judge, optionally as a panel, where that is the right instrument. Or send the run to a blinded human reviewer who votes A against B without seeing model identities. Every verdict carries the receipt it was based on, and a review is recorded as a review, not an approval.
We handle PHI. Can we use this?
Read the data flow by path first. redcrown score and redcrown check send nothing anywhere. A native evaluation, local or hosted, sends requests to the providers you configure, on your keys. Uploading a run and creating a share link are separate actions, and a public share link shows inputs, outputs, and references. Stored eval inputs, provider keys, and proof-link tokens are encrypted at rest with a dedicated key; stored run reports sit in the database without that layer. RedCrown holds no formal certification, no SOC 2 and no ISO 27001, and does not claim any. The Security and Trust page states exactly what is true today.
Can RedCrown score speech-to-text or extraction?
Yes. 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 LLM output. The transcription example on the consultancies page compares four configurations on nine clinical clips, with that sample size stated on the card.
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.