Confident AI is the company behind DeepEval, the widely adopted open-source LLM evaluation framework, and the Confident AI cloud platform that layers observability, dataset management, regression testing, and red teaming on top of the local framework. DeepEval treats LLM evaluation as unit testing with research-backed metrics such as GEval, AnswerRelevancy, and Faithfulness, while DeepTeam provides an open-source red teaming framework. The hosted platform is SOC 2 Type II, HIPAA, and GDPR compliant with self-hosting available for regulated customers.
Confident AI is listed with 3 APIs on the APIs.io network. Tagged areas include LLM Evaluation, Open Source, Observability, Red Teaming, and Guardrails.
Confident AI’s developer surface includes documentation, engineering blog, pricing, and 18 more developer resources.
21.9/100emerging▬ flatAgent13/100agent awareFront door AI6/6
dominant
Heartbeat100/100
alive
open core · Apache-2.0
Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →
Regulatory Posture applies to this provider. Its tags matched the
Horizontal (data, software, accessibility, platform) regime, so
Regulatory Posture carries 15 points of the composite.
If this regime is wrong for your business, say so on your
provider repo — the
applicability map is public and we will correct it.
Open Source Surface applies to this provider. This product is open source and we
read its repository directly, so Open Source Surface carries
10 points of the composite. It is scored from what the repository actually
publishes — a security policy, a contribution guide, a release history, a code of conduct — read live from the
provider rather than inferred from our own catalog pointers.
This facet adds; nothing was taken away to make room for it. An open-source project is not excused from
the commercial facets, because exemption would strip it of the points it does earn.
If we have the wrong repository, or this product is not open source, say so on your
provider repo and we
will drop the facet rather than have you publish against it.
Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for
this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero:
never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and
several others — scorable at all.
The six quality facets above are damped to 75 points between them,
because both conditional facets apply and carry
25 points together. That is why each facet's contribution is shown against a damped
maximum: raising a quality facet moves the composite by 75% of its nominal
weight, not 100%. The full arithmetic is at apis.io/rating/.
Show this score on your site.
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Agent readiness — checks not met, largest first (exact, from this profile):
Machine-Readable Auth+10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey)
Idempotency+9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
Stable Error Semantics+8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes
A2A Agent Card+8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
10 more unmet checks in the Agent Readiness table above.
Kin Score — facets with the most composite points still available (weight × headroom):
Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.
DeepEval is an open-source Python framework for evaluating LLM applications as unit tests. It ships with research-backed metrics including GEval, AnswerRelevancyMetric, Faithful...
Confident AI is the hosted platform that complements DeepEval with observability, centralized reporting, regression testing, prompt versioning, dataset management, trace ingesti...
DeepTeam is Confident AI's open-source red teaming framework for stress-testing LLM applications against adversarial attacks including prompt injection, jailbreaks, PII leakage,...
Remote MCP server at mcp.confident-ai.com over HTTP.
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Confident AI API pricing
Not yet checked against Confident AI's own pricing page. Compiled by API Evangelist on 2026-05-22; confirm before you rely on a figure. A free plan or tier is listed.
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