Is Cleanlab open source or paid?

Both, confusingly. The original `cleanlab` Python library for label-error detection is open source. The Trustworthy Language Model (TLM) API and Cleanlab Studio are commercial and priced through sales — no public self-serve tiers.

How hard is it to integrate?

Roughly a few lines of code: install the client, pass your prompt, response, and retrieved context, get back a score plus an explanation. It also drops into existing observability stacks like Langfuse, Arize, Langtrace, and MLflow instead of becoming another dashboard.

How is the trustworthiness score computed?

It's a composite: LLM self-reflection on its own answer, consistency checks across multiple sampled responses, token-level log-probability analysis, plus Cleanlab's proprietary aleatoric and epistemic uncertainty estimators — collapsed into one calibrated score rather than a pile of raw metrics.

Does it work with any LLM, or am I locked in?

It's model-agnostic. You can point it at your own model/provider and tune the 15+ supported evaluator models and 5 quality settings, trading latency against detection accuracy. You're not forced onto a single backbone.

What's the latency and cost overhead per call?

Budget extra LLM calls — consistency sampling and self-reflection aren't free. Response times can go as low as ~300 ms with fast evaluators, but accuracy degrades the further you push latency down. Cost scales with evaluator choice and quality preset, so benchmark against your own traffic.

Directory/Analytics/AI Evaluation/Cleanlab

Cleanlab

Cleanlab provides an AI reliability platform that detects hallucinations, wrong-context answers, and knowledge gaps in LLM and RAG outputs. It combines proprietary uncertainty methods with self-reflection, consistency checks, and probabilistic signals into a single trustworthiness score with explanations. Teams use it to add real-time guardrails, escalate low-confidence responses to humans, and benchmark model performance. It integrates with observability stacks like Arize, Langfuse, and MLflow. Built for AI engineers, data scientists, and platform teams that need production-grade evaluation without relying solely on LLM-as-a-judge.

Analytics

Visit website

Added on

Nov 4, 2026

Hallucination detectionTrust scoresReal-time guardrailsRAG supportAgent workflows300ms latencyLangfuse integrationMLflow integration

Product information

Everything worth knowing about Cleanlab

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Hallucination Detection

Flags responses your AI fabricates regardless of whether retrieval actually found the right context, so made-up answers never reach users unflagged.

Wrong Context Detection

Catches cases where your knowledge base holds the correct answer but the agent retrieves the wrong passage and responds incorrectly.

Knowledge Gap Flagging

Identifies 'I don't know' answers caused by missing context in your knowledge base, separating real gaps from retrieval failures.

Trustworthiness Scores & Escalation

Returns a clear, actionable trust score per response (e.g. 0.53 on a bad answer) so your app can decide when to answer confidently and when to hand off to a human or fallback flow.

Real-time Guardrails

Runs inline in production to score live LLM output, not just offline evals — with explanations alongside each score.

Technical capabilities

Composite Uncertainty Scoring

Combines self-reflection, multi-response consistency checks, token-level probabilistic measures, and proprietary aleatoric/epistemic uncertainty methods into one metric rather than stacking separate judges.

Configurable Models & Quality Levels

15+ supported evaluation models and 5 quality settings let you trade latency against accuracy, with response times as low as 300 ms.

Must watch videos

Who uses it

Real use cases, no hype

RAG Hallucination Guardrails

Score every retrieved-context answer and block the ones that invent facts.

Agent Escalation Logic

Route low-confidence responses to humans before the bot burns a customer.

Support Bot QA

Catch wrong-context and knowledge-gap answers in production chat flows.

LLM Eval Pipelines

Replace weak LLM-as-a-judge scoring with a calibrated, explained metric.

Training Data Cleaning

Use Cleanlab Studio's confident learning to find mislabeled data at scale.

Noisy-Label Model Training

Train classifiers on dirty labels without hand-auditing every example.

AI Compliance & Audit

Keep a per-response reliability record for regulated deployments.

What's great

Best-in-class hallucination detection (0.91 AUROC)
Drop-in integration in a few lines of code
One clear trustworthiness score per response
Works on RAG, agents, and plain LLM apps

Technical strengths

Blends self-reflection, consistency sampling, and token log-probs into one calibrated metric
15+ evaluator models and 5 quality/latency presets
Native hooks for Langfuse, Arize, Langtrace, MLflow, NVIDIA

Where it falls short

Core TLM is closed-source; OSS lib only does data cleaning
Acquired by Handshake AI — roadmap and pricing are in flux
No transparent self-serve pricing; sales call required
Thresholds need per-app tuning before you trust the score

Technical limitations

Every evaluation means extra LLM calls — real latency and cost
Cloud API dependency; no true air-gapped on-prem deploy
300ms floor only holds on fast evaluators, at an accuracy hit

For technical folks

The deep-dive specs

Hallucination Detection AUROC

0.91 (vs LLM-as-a-judge 0.78, RAGAS Faithful 0.70, G-Eval Correctness 0.56)

Benchmarks Used

CovidQA, DROP, FinanceBench, PubmedQA

LLM Reliability vs Alternatives

34% better precision/recall than other methods

RAG Reliability vs Alternatives

3× better precision/recall than popular real-time evaluation models across six RAG applications

Agent Coverage

Validated on 5 different AI agent architectures

Supported Evaluation Models

15+

Quality Settings

Minimum Latency

300 ms

Integrations

Arize, Langfuse, Langtrace, MLflow, NVIDIA

Detection Methods

Self-reflection, consistency sampling, probabilistic token measures, proprietary aleatoric and epistemic uncertainty methods

FAQ

Includes technical Q&A

It scores every AI response with a 0–1 trustworthiness score and flags the failure mode: hallucination, wrong context (answer exists in your KB but wasn't retrieved), or knowledge gap. You then decide to answer, fallback, or escalate to a human.

Traffic Insights

Monthly Visits

—

November 2026

Growth Rate

vs last month

Dominance

in Analytics

Top Country

—

Top Countries Breakdown

—

Growth Trend

Stable

Traffic has remained stable.

Pricing overview

Freemium Pricing

Cleanlab Pricing

Cleanlab uses a freemium pricing structure. Real-time rates, custom enterprise quotas, and plan details are managed directly on their official platform.

Check Pricing on Cleanlab

Reviews