Cleanlab

4.5 (13 reviews)

A data-centric AI platform that automates the detection and correction of label errors, outliers, and duplicates across text, image, and tabular datasets.

Excellent for ML teams needing to clean noisy training data at scale, weaker for teams requiring complex manual annotation workflows.

Machine Learning Data Analysis Datasets Python Mlops Analytics Computer Vision Nlp

Analysis based on product data, pricing structure, traffic signals, and public user sentiment.

Visit Website

Share this tool

Facebook X

LinkedIn Reddit

WhatsApp Telegram

Who Should Use Cleanlab?

Typical users

Data scientists and ML engineers managing large, noisy datasets or fine-tuning LLMs.

Maturity fit

scaling to advanced

Choose this if…

Your training data has a high rate of label noise or human error
You want to automate data cleaning without writing custom heuristic scripts
You are fine-tuning LLMs and need to prune low-quality instruction pairs
Your priority is improving model performance through data quality rather than architecture tuning

Skip this if…

You need a platform for manual, first-pass human labeling from scratch
Your datasets are small enough for manual inspection in Excel or basic notebooks
You require specialized support for video or 3D point cloud data in non-enterprise tiers

About Cleanlab

Cleanlab provides tools to improve dataset quality by identifying semantic errors that standard data validation tools miss. It was founded by the creators of Confident Learning at MIT and exists to solve the 'garbage in, garbage out' problem in machine learning. The platform offers both a programmatic Python library and a no-code web interface for data curation.

Official profiles

What it actually does

It analyzes the relationship between data features and their labels to find inconsistencies, such as an image of a 'cat' labeled as a 'dog.' It generates 'cleanliness' scores for every data point, flags outliers, and suggests corrected labels. For LLMs, it provides a 'Trustworthy Language Model' (TLM) that scores the reliability of generated responses to detect hallucinations.

What makes it different

Unlike traditional data quality tools that focus on schema errors or null values, Cleanlab uses 'Confident Learning' to find semantic errors in the labels themselves. It is model-agnostic, meaning it can work with any ML model (PyTorch, XGBoost, OpenAI) to evaluate the data that model was trained on.

Automated label error detection Outlier and anomaly detection Near-duplicate data identification LLM hallucination scoring (TLM) Multi-annotator consensus analysis Automated data cleaning (Auto-fix) Data drift and shift monitoring No-code data curation interface

Ratings across the web

4.5 (13 reviews)

G2 13 reviews

4.5/5

Ratings aggregated from independent review platforms.

Key Features

Confident Learning

The core algorithmic framework that mathematically identifies label noise.

Cleanlab Studio

A web-based UI for visual data inspection and one-click error correction.

Trustworthy Language Model (TLM)

Provides a 0-1 reliability score for LLM outputs to catch hallucinations.

Multi-annotator Analysis

Aggregates labels from multiple humans to identify who is underperforming.

Auto-labeling

Uses high-confidence predictions to label large unlabelled subsets automatically.

Python API

Allows for integration into existing MLOps pipelines and CI/CD workflows.

Cross-modal Support

Handles tabular, text, and image data within the same platform logic.

Cleanset Export

Generates a cleaned version of your dataset ready for immediate retraining.

Pricing

Open Source

Free

Python library (MIT License)
Basic label error detection
Outlier detection
Community support

Free (Studio)

Free

1,000 credits per month
No-code web interface
Basic AutoML
Standard data types

Popular

Personal

$20 month

1,000 credits included
Pay-as-you-go additional credits
Full web interface access
Python API access

Team

$100 month

5,000 credits included
Shared workspaces
Priority support
Advanced analytics

Enterprise

Custom

Unlimited credits/volume discounts
VPC/On-prem deployment
Support for Audio/Video
Custom ML task support (Object Detection)

Pricing checked 4 months ago

Pricing guidance

Best plan for most users: The Personal plan is the best entry point for individual data scientists to access the Studio UI without the limitations of the free tier's credit cap.

Free plan enough? Yes, if you are working on a single small project (under 1,000 rows) or just want to test the UI's capabilities.

Upgrade when:

When your dataset exceeds 1,000 rows per month
When you need to collaborate with other team members in a shared workspace
When you need to process unstructured data like video or audio

Watch out for:

Credits are consumed per row/task, and LLM tasks (TLM) consume credits much faster than tabular tasks
The open-source library lacks the 'Auto-fix' UI and AutoML features found in Studio

Premium positioning for the Studio platform, justified by the significant manual labor costs it offsets for enterprise ML teams.

Pros & Cons

Strengths

Theoretically grounded error detection
Based on peer-reviewed MIT research, the algorithms provide provable guarantees for finding label errors under certain conditions.
Significant reduction in manual QA
Users report reducing manual data review time by up to 80% by focusing only on the samples Cleanlab flags as low-confidence.
Model-agnostic flexibility
It works with any model that outputs predicted probabilities, allowing teams to keep their existing ML stack.
Effective LLM hallucination detection
The TLM feature provides a concrete metric for RAG reliability, which is often a 'black box' for developers.

Weaknesses

Credit-based pricing can be opaque
The Studio platform uses a credit system that can become expensive and difficult to budget for when processing millions of rows.

Affects: Teams with massive datasets
Computational overhead
Running advanced cleaning algorithms on very large datasets can be resource-intensive and slow in the open-source version.

Affects: Individual developers with limited compute
Limited support for complex ML tasks
While expanding, support for tasks like object detection or sequence-to-sequence is often restricted to Enterprise tiers.

Affects: Computer vision and advanced NLP teams

Real User Sentiment

Highly positive among technical users who value the algorithmic rigor, though some find the SaaS pricing steep for large-scale data.

Users tend to like

The 'one line of code' simplicity of the Python library
The ability to find errors in famous benchmark datasets (like ImageNet)
The no-code interface for non-technical stakeholders to review data
The effectiveness of the Trustworthy Language Model for RAG

Users commonly complain about

Studio credit costs can scale quickly
Occasional dependency conflicts in the Python package
The UI can be sluggish when handling very large files (>1GB)

Recurring tradeoffs

Users often choose between the free but 'manual' open-source library and the expensive but 'automated' Studio platform.

Happiest users

ML Engineers at mid-to-large enterprises who are tired of manually cleaning messy, human-labeled datasets.

Often frustrated

Budget-constrained startups with massive datasets that exceed the Personal/Team credit limits.

Use Cases

E-commerce

Cleaning a product catalog where thousands of items have been miscategorized by vendors.

LLM Fine-tuning

Identifying and removing low-quality or toxic instruction pairs from a training set.

Medical Imaging

Flagging potential misdiagnoses in labeled X-ray or MRI datasets for expert re-review.

Customer Support

Detecting hallucinations in a RAG-based chatbot before they reach the customer.

Financial Services

Improving the accuracy of fraud detection models by removing outliers from historical transaction data.

Frequently Asked Questions

Is there a free version of Cleanlab?

Yes, Cleanlab offers a fully free, open-source Python library (available via pip install cleanlab). Additionally, Cleanlab Studio has a free tier that provides 1,000 credits per month for the no-code web interface.

How does Cleanlab compare to Snorkel AI?

Snorkel AI focuses on 'weak supervision'—helping you create labels from scratch using programmatic rules. Cleanlab focuses on 'data-centric cleaning'—finding and fixing errors in labels you already have. They are often used together in the same pipeline.

What are the main limitations of Cleanlab?

The primary limitation is that it requires a model to generate predicted probabilities; if your model is very poor, the error detection will be less accurate. Also, advanced tasks like Object Detection and Video analysis are currently restricted to Enterprise plans.

Does Cleanlab work with Snowflake or Databricks?

Yes, Cleanlab Studio has native integrations and tutorials for both Snowflake and Databricks, allowing you to clean data directly from your warehouse and export the 'cleanset' back.

Can Cleanlab fix my data automatically?

Yes, the 'Auto-fix' feature in Cleanlab Studio can automatically update labels to the suggested correct value for high-confidence errors, though manual review is recommended for critical data.

What data types are supported?

Cleanlab supports tabular (CSV, Excel, SQL), text (JSON, TXT), and image data. Support for audio and video is available for Enterprise customers.

Why trust this page?

This evaluation combines product positioning, pricing analysis, traffic and market signals, and public user sentiment into a single decision-support page. Content is generated editorially — not copied from the vendor's website.

Funding & Company

Founded

2021

Stage

Acquired

Total Raised

$30M

Latest Round

Series A (Oct 2023)

Notable Investors

Menlo Ventures TQ Ventures Bain Capital Ventures Databricks Ventures

Cleanlab raised a total of $30M across two quick succession rounds in 2023, a $5M Seed and a $25M Series A, from notable investors including Menlo Ventures, Bain Capital Ventures, and Databricks Ventures. The company was subsequently acquired by Handshake in January 2026, shifting its stability and future development under the umbrella of its new parent company.

Full funding report high confidence

Market Signals & Traffic

Estimated visits, global rank, geography, traffic sources, monthly visit trends, and organic search keywords (Similarweb)—on a dedicated page built for depth and search.

Estimated visits: 27,176
Global rank: #1,031,199
Snapshot: Apr 2026
Traffic trend: Surging