Cleanlab

What is Cleanlab?

Cleanlab builds software for reliable AI, and its product line covers two jobs. The first is checking AI output: Cleanlab Detect and the Trustworthy Language Model give each response from an LLM, RAG system or agent a trustworthiness score, so a team can decide when an answer is safe to show and when to fall back to a canned reply or a human. The target failures are hallucinations, retrieval of the wrong context and gaps in the knowledge base. The second job is data quality: Cleanlab Studio finds label errors, outliers and ambiguous rows in text, image and tabular datasets, suggests fixes, trains models with AutoML and can deploy them through a web interface or REST API. Studio works through a no-code web app, a Python API and a command line interface, and its documentation mentions a free tier and an Enterprise plan with extra data types and tasks. Cleanlab sells through a request-a-demo process and does not publish prices, and its accuracy benchmarks are the company's own results.

Use cases

• Score LLM answers for trustworthiness
• Flag hallucinations in RAG responses
• Route low-confidence agent output to humans
• Find mislabeled rows in a dataset
• Train and deploy models with AutoML
• Auto-label large text or image sets

Who it's for

• ML engineers
• AI product teams
• Data scientists
• Enterprise AI teams

Why use Cleanlab

• A single score per answer gives you something concrete to threshold, instead of reading outputs by hand.
• Low scores can trigger fallback steps such as an abstention phrase or escalation to a person.
• The same vendor covers both answer checking and training-data cleanup.
• Studio's no-code app lets people without ML engineering skills fix dataset issues.
• The documentation covers agent frameworks, so scoring can be added to LangGraph or similar setups.

Cleanlab features

• Trustworthiness score for each AI response
• Detection of hallucinations, wrong context and knowledge gaps
• Real-time scoring for RAG systems and agents
• TrustworthyRAG evals for groundedness and context sufficiency
• Cleanlab Studio detection of label errors, outliers and ambiguous data
• AutoML training and one-click deployment of cleaned datasets
• Real-time and batch inference through a web interface or REST API
• Support for text, image and tabular data, with more types on Enterprise
• No-code web app, Python API and command line interface

Cleanlab integrations

• LangGraph
• OpenAI Agents SDK
• Slack

How to get started with Cleanlab

1. Send a prompt and response from your own LLM, RAG system or agent to Cleanlab for scoring, or call its model to generate and score together.
2. Read the trustworthiness score and any explanation for why it is low.
3. Set a threshold so low-scoring answers trigger an abstention, a retry with a changed prompt or a human review.
4. For datasets, upload your data to Cleanlab Studio, create a project and review the issues the AI finds.
5. Export the cleaned dataset, then optionally train and deploy a model on it.

Cleanlab limitations

• Pricing is not published, and access starts with a demo request
• Accuracy figures come from Cleanlab's own benchmarks
• Video, audio, regression and detection tasks in Studio need the Enterprise plan
• A trust score estimates the likelihood of an error and does not guarantee correctness

Cleanlab pricing

Cleanlab is sold on custom quotes, so its prices aren't published.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

Cleanlab alternatives

The main Cleanlab alternatives listed on Debuly are Galileo, Guardrails AI, Confident AI, TruLens, Label Studio and Cleanlab Trustworthy Language Model. The table compares pricing and platforms; order is A–Z.

1. 

   Cleanlab Trustworthy Language Model

   API that gives any LLM response a trustworthiness score in real time, so you can catch likely errors before users see them.

   Developer platformsAutomation and agents
   Visit Cleanlab Trustworthy Language Model (official site)
2. 

   Confident AI

   Evaluation and observability platform for LLM apps, built on the DeepEval framework, with tracing, datasets and regression tests.

   Developer platformsProductivity
   FreemiumVisit Confident AI (official site)
3. 

   Galileo

   AI observability and eval platform that tunes evals to your data and turns them into guardrails for LLM apps and agents.

   Developer platformsAutomation and agents
   FreemiumVisit Galileo (official site)
4. 

   Guardrails AI

   Open-source framework that wraps LLM apps with validators for PII, hallucination and policy checks, plus Snowglobe simulation testing.

   Developer platformsCodingAutomation and agents
   Open sourceVisit Guardrails AI (official site)
5. 

   Label Studio

   Open source data labeling platform for images, video, audio, text and time series, with paid Starter Cloud and Enterprise editions.

   Developer platformsAutomation and agents
   Open sourceVisit Label Studio (official site)
6. 

   TruLens

   Open-source, OpenTelemetry-native Python library for tracing AI agents and RAG apps and scoring each step with LLM judges.

   Developer platformsCodingAutomation and agents
   Open sourceVisit TruLens (official site)

Cleanlab FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from cleanlab.ai · fetched 23 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
• Cover: none collected yet, so a generated tile is shown.

Spot an error? Report an issue with this listing. We also remove a logo or image on the owner's request.