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Cleanlab
NewVerified
Detect and fix LLM hallucinations with confidence scores.
Overview
Cleanlab's Trustworthy Language Model (TLM) adds a confidence score to each LLM token, helping teams identify when models are likely hallucinating. It works with any LLM and provides real-time detection without retraining, making it useful for enterprises building reliable AI applications where accuracy matters.
Pros
- Works with any LLM without model fine-tuning or retraining
- Per-token confidence scores enable precise hallucination detection
- Reduces deployment risk in high-stakes applications
- API-first design integrates easily into existing workflows
- Free tier available for testing and prototyping
✕ Cons
- Requires additional API calls, adding latency to responses
- Pricing not clearly published on public-facing pages
- Limited to text-based detection, not multimodal hallucinations
Key Features
Per-token confidence scoring
Hallucination detection API
Multi-LLM support
Real-time detection
Integrated remediation guidance
Enterprise audit logging
Use Cases
Enterprise teams building customer-facing AI chatbots with accuracy requirementsFinancial and legal services using LLMs for document analysis and adviceHealthcare providers deploying LLMs for clinical decision supportContent creators using AI to generate high-stakes material
Best For
LLM Platform DevelopersEnterprise AI TeamsCompliance & Risk OfficersProduction ML EngineersQuality Assurance Leads
Frequently Asked Questions
What is Cleanlab's pricing model?▾
Cleanlab offers both open-source and commercial pricing tiers. Specific pricing details are available on their website based on deployment scale and usage volume.
How difficult is it to set up and integrate Cleanlab?▾
Cleanlab is designed to work with any LLM without model retraining, making integration relatively straightforward. Setup time depends on your existing infrastructure, but documentation and APIs support rapid deployment.
Can Cleanlab integrate with my existing LLM stack?▾
Yes, Cleanlab works model-agnostic and can be integrated into most LLM workflows via API. It validates outputs without requiring changes to your underlying models.
What are the main limitations of Cleanlab?▾
While effective for hallucination detection, Cleanlab works best as part of a broader quality assurance strategy and cannot eliminate all factual errors. Performance may vary depending on domain complexity and output types.
Who should use Cleanlab?▾
Organizations deploying LLMs in production environments where output reliability is critical—including customer-facing applications, research, healthcare, finance, and regulated industries—benefit most from hallucination detection and remediation.
Pricing Plans
Free
Custom
- Up to 10,000 data points
- Basic data quality scoring
- Community support
- Single project workspace
ProMost Popular
$99/monthly
- Up to 1 million data points
- Advanced label quality scoring
- Priority email support
- Multiple projects and team collaboration
Enterprise
Custom
- Unlimited data points
- Custom model training and deployment
- 24/7 dedicated support
- On-premise deployment options