Compare tools
Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
⇄ Comparison dimension — pick the market you're actually shopping in
✕
Prolific
✓ verifiedPaid
Research participant marketplace that gives AI teams and academics fast access to verified, screened human data and feedback.
21M visits/mo
✕
Appen
✓ verifiedPaid
Long-standing provider of human-labeled, expert-validated training data and model evaluation services for building frontier AI.
1.2M visits/mo
✕
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
Pricing
No public pricing
No public pricing
No public pricing
Starter: $99/user/mo (up to 12 users, hosted)
Free trial available
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
- ✦Frontier alignment data (RLHF, SFT, red teaming)
- ✦Speech and audio data
- ✦Multimodal / VLM annotation
- ✦Physical AI data (LiDAR, robotics, sensor fusion)
- ✦Model integrity, bias and hallucination audits
- ✦1M+ vetted contributors, 500+ locales
- ✦SOC2 and ISO 27001 certified
- ✦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
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
- →Source training data for AI models
- →Evaluate and benchmark models
- →Annotate multimodal and sensor data
- →Run safety and bias audits
- →Labeling training data across modalities
- →Human-in-the-loop AI evaluation
- →RLHF and fine-tuning data collection
- →RAG and LLM benchmarking
Visit