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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
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Prolific
✓ verifiedPaid
Research participant marketplace that gives AI teams and academics fast access to verified, screened human data and feedback.
21M visits/mo
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Label Studio
✓ verifiedFreemium
Open-source data-labeling and AI-evaluation platform for image, text, audio, video and LLM workflows, with a paid enterprise tier.
261K visits/mo
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Labelbox
✓ verifiedFreemium
Data-labeling and RL data platform supplying training data, environments and evaluation for frontier AI labs and enterprises.
1.1M visits/mo
Pricing
No public pricing
No public pricing
Starter: $99/user/mo (up to 12 users, hosted)
Free trial available
No public pricing
Core features
- ✦CSV to API conversion
- ✦Data parsing (CSV to JSON)
- ✦Filtering capabilities
- ✦300,000+ verified, screened participants
- ✦300+ audience targeting filters
- ✦Representative and quota-based sampling
- ✦API and no-code survey tool integrations
- ✦AI-powered participant quality monitoring (Protocol)
- ✦Managed services with dedicated project teams
- ✦Access to vetted domain experts
- ✦Open-source multi-type labeling
- ✦Programmable, customizable interfaces
- ✦API, SDK and webhooks
- ✦ML backend for pre-labeling and active learning
- ✦LLM evaluation and RLHF workflows
- ✦Enterprise QA, SSO and analytics
- ✦Data labeling across modalities
- ✦RL environments and reward signals
- ✦Custom model evaluations and benchmarks
- ✦Human preference/annotation from an expert network
- ✦Recursion RL platform for enterprise agents
- ✦Robotics data (video, trajectories)
Use cases
- →Sharing CSV data with a team via an API
- →Creating a public API from CSV data
- →Filtering and accessing specific data within a CSV file programmatically
- →Collecting human preference data for RLHF or model evaluation
- →Running academic behavioral or market research studies
- →Sourcing domain-expert data for specialized AI benchmarks
- →Labeling training data across modalities
- →Human-in-the-loop AI evaluation
- →RLHF and fine-tuning data collection
- →RAG and LLM benchmarking
- →Building training and evaluation datasets
- →Post-training and RLHF for models
- →Benchmarking model capability
- →Training enterprise specialist agents
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