Confident AI
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.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles Confident AI the way a machine reads it — 33 machine-readable artifacts across 3 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Confident AI scores 26.4/100 (emerging), with a separate agent-readiness read of 7/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Confident AI
Each block below is one kind of artifact we hold for Confident AI. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 3
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
DeepEval
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 Platform
Confident AI is the hosted platform that complements DeepEval with observability, centralized reporting, regression testing, prompt versioning, dataset management, trace ingesti...
DeepTeam
DeepTeam is Confident AI's open-source red teaming framework for stress-testing LLM applications against adversarial attacks including prompt injection, jailbreaks, PII leakage,...
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Confident Ai Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Confident Ai Finops
FINOPSFeatures 10
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
DeepEval Framework
Open-source Python framework for evaluating LLM apps as unit tests with research-backed metrics.
GEval Metric
LLM-as-a-judge metric for custom evaluation criteria configurable by natural language rubric.
LLM Tracing
Component-level tracing of LLM calls, retrieval steps, and tool usage for agents.
Observability
Hosted dashboards for traces, latencies, costs, and metric scores across production runs.
Regression Testing
Detect quality regressions against historical baselines as part of CI.
Prompt Versioning
Centralized prompt registry with version history and rollout.
Dataset Management
Manage evaluation datasets, synthetic data generation, and human annotations.
Red Teaming
DeepTeam framework for adversarial testing against LLM applications.
Self-Hosting
Self-hosted deployment available for regulated customers.
Compliance
SOC 2 Type II, HIPAA, and GDPR compliant cloud platform.
Scroll within the panel for all 10 ·
Security Posture 1
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Use Cases 5
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Unit Testing LLM Apps
Treat LLM evaluations as pytest-style unit tests inside developer workflows and CI.
RAG Evaluation
Score retrieval, faithfulness, and answer quality in RAG pipelines.
Agent Evaluation
Trace and evaluate multi-step agents with component-level metrics.
Production Observability
Stream production traces to Confident AI for monitoring and alerting.
Red Teaming
Run adversarial test suites with DeepTeam to find security and safety failures.
Integrations 11
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
OpenAI
Evaluate OpenAI Chat Completions and Assistants outputs.
Anthropic
Evaluate Anthropic Claude outputs.
LangChain
Native integration for evaluating LangChain chains and agents.
LangGraph
Trace and evaluate LangGraph stateful agents.
LlamaIndex
Evaluate LlamaIndex RAG pipelines.
CrewAI
Trace and evaluate CrewAI multi-agent crews.
Pydantic AI
Integrate evaluators with Pydantic AI agents.
OpenTelemetry
Ingest OTel traces for evaluation and observability.
Ollama
Use local Ollama models as evaluators or as systems under test.
Azure OpenAI
Evaluate Azure-hosted OpenAI deployments.
Gemini
Evaluate Google Gemini model outputs.
Scroll within the panel for all 11 ·
Resources
Every other property we hold for Confident AI — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 1
Portal, sign-up, and the first successful call
Documentation 3
Reference material describing how the API behaves
Build 3
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
Commercial 1
Pricing, plans, and the legal terms of use
Company 3
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/confident-ai · machine-readable index on apis.io