Labelbox — The data factory for AI teams building reinforcement learning and evals. Best for AI labs training reinforcement learning models with custom reward signals, Teams building private AGI benchmarks for frontier capability assessment, Organizations needing expert human evaluation for multimodal and reasoning tasks. Free to use.
AI labs training reinforcement learning models with custom reward signalsTeams building private AGI benchmarks for frontier capability assessmentOrganizations needing expert human evaluation for multimodal and reasoning tasksRobotics companies requiring real-world multimodal training data with trajectory annotations
Not ideal for
Simple image classification or bounding box labeling tasksSmall teams or indie developers without budget for enterprise pricingProjects requiring fully automated or no-human-in-the-loop labelingUsers needing tight integration with specific ML frameworks like TensorFlow or PyTorch (not listed)
Labelbox is essential for AI labs needing high-quality data for post-training and evals. Its deep expertise in RL and human evaluation sets it apart from generic labeling platforms, but it may be overkill for basic image tagging tasks.
Pick Labelbox if you're training or evaluating frontier models—especially for RL, custom benchmarks, or multimodal annotation. Its Alignerr network (1.5M+ knowledge workers) and research-backed methods give it an edge for high-stakes data. Pass if you only need simple classification tasks; smaller tools like Scale AI or dedicated labeling services might be cheaper. A key caveat: pricing is opaque (contact required), making it less accessible for indie developers. For RL data, Labelbox's tuned environments and rubric-based scoring are unmatched, but for basic bounding boxes, consider alternatives. Their leaderboards and research publications show real-world rigor, but integration details are sparse on the page—check API docs separately.
Skip Labelbox if Skip Labelbox if you need a low-cost self-serve labeling tool for basic ML projects—the free tier is limited, and paid tiers require sales contact.
Latest from Labelbox
Updated yesterday
Across the latest 7 updates: 1 launch, 2 changelog entries and 4 news mentions.
How likely is Labelbox to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
funding runway
60
website health
90
github activity
50
category mortality
70
wrapper dependency
100
hyperscaler overlap
80
About Labelbox
Labelbox is a data factory for AI teams, powering over 80% of leading AI labs in the US. It provides end-to-end data solutions for reinforcement learning, custom evaluations, robotics data, and access to an elite human annotator network. Key features include expert-crafted rubrics for scoring, tuned environments for optimal reward gradients, private AGI benchmarks, and multimodal annotation capabilities. Unlike generic data labeling tools, Labelbox focuses on advanced AI data needs like RLHF and frontier model evaluation, serving startups to Fortune 500s.
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Key Features
Reinforcement learning data generation with reward signals and preference pairs
Expert-crafted scoring rubrics for coding, science, finance, and more
Tuned environments for optimal reward gradients and maximal learning
Custom private AGI benchmarks before public release
Arena evals for head-to-head model comparisons with human preferences
Rubric-based multimodal scoring across text, vision, and reasoning tasks
Full-stack robotics data including video, trajectories, and multimodal annotations
Purpose-built hardware for custom collection infrastructure
AI-powered diversity for broad task and environment coverage
Access to 1.5M+ knowledge workers across 40+ countries and 200+ domains
Real-world workflow fit
Concrete scenarios for the personas Labelbox actually fits — and what changes day-one when you adopt it.
AI research scientist at a frontier lab
You need to collect preference pairs for RLHF fine-tuning of a new LLM. You upload 10K conversational prompts via Catalog, design a rubric-based ontology, and launch a labeling project using the Alignerr network. Foundry integrates GPT-5.2 and Claude Opus 4.6 to generate model responses, which human annotators rank. Within a week, you export the preference data and train your model.
Outcome: High-quality preference pairs delivered with Labelbox's quality guarantee, accelerating model alignment.
ML engineer at a robotics startup
You have 5K hours of video and sensor data from robotic manipulation tasks. You import the data into Labelbox, use the video editor to annotate trajectories, and apply AI critics (added May 2026) to flag low-quality labels. You review and export the dataset for training your policy model.
You need to red-team a new model for safety vulnerabilities. You define red teaming rubrics in Labelbox, deploy Alignerr experts to generate adversarial prompts, and evaluate model responses using both automatic and human scoring. The platform tracks performance over time via Leaderboards.
Outcome: Systematic safety evaluation with detailed reports, identifying blind spots that automated benchmarks miss.
Use Cases
Collect preference pairs and reward signals to fine-tune LLMs with RLHF using expert annotators
Evaluate multimodal model performance with custom rubrics and head-to-head arena comparisons
GPT-5.2 ProClaude Opus 4.6Gemini 1.5Grok Voice AgentAmazon Nova Sonic 2OpenAI GPT-5OpenAI o3Grok 4Foundry (integrated frontier models)proprietary (Labelbox AI)
Limitations
Free tier caps users at 30 and projects at 50, and lacks premium features like SSO, custom embeddings, and the Alignerr network. Subscription tiers require contacting sales for pricing, making upfront cost unclear. On-demand labeling services add significant cost and are gated behind contract negotiation. The platform is cloud-dependent; no air-gapped deployment option is mentioned. Some advanced features like Foundry models and AI critics are only available in the subscription tier.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Labelbox tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Solo ML engineers or small teams evaluating Labelbox's platform with up to 30 users and 50 projects.
What this tier adds
Free entry point with core catalog, annotate, and model features but limited to 30 users, 50 projects, 25 ontologies, and community support.
Subscription Tier (contact for price)
Contact sales
Ideal for
Enterprise AI teams needing unlimited users, projects, and advanced features like SSO, Foundry models, and AI critics.
What this tier adds
Unlocks unlimited users, projects, ontologies, Labelbox Monitor, SSO, custom embeddings, Foundry models, auto-labeling, AI critics, and premium support.
Standard Services
Contact sales
Ideal for
Teams needing cost-effective, on-demand labelers for standard CV, NLP, and multilingual projects.
Hidden costs & gotchas
What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.
•Subscription tier pricing is contact-only, with unknown per-seat or overage costs
•Additional workspaces and LBUs available for purchase
•Security & HIPAA add-ons available for purchase
Where the pricing makes sense
The company stage and team size where Labelbox's pricing actually pencils out — and where peers do it cheaper.
Labelbox's free tier is generous for evaluation, but scaling requires a sales-intermediated subscription. For startups and small teams, the free tier (up to 30 users, 50 projects) is workable initially. However, compared to Scale AI's self-serve or SuperAnnotate's per-seat pricing, Labelbox's enterprise focus means higher minimum commitments. The Alignerr network adds premium cost but delivers high-quality annotation for complex tasks. Volume discounts are available at scale, but not published.
Setup time & first value
How long it actually takes to get something useful out of Labelbox — broken out by persona, not the marketing-page minute.
For a single user: The platform is cloud-based; sign up free instantly. Basic annotation projects can be set up in under an hour using pre-built editors. For a team using the Alignerr network, expect a few days to define rubrics, onboard experts, and launch the first labeling job. For complex evaluations (arena evals, private benchmarks), setup may take 1-2 weeks.
Switching to or from Labelbox
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
→From SuperAnnotate: Export your annotations in COCO or JSON format, then import via Labelbox SDK or Catalog UI.
→From Scale AI: Use Labelbox's API to import project configurations and annotation data programmatically.
→From custom spreadsheets/CSV: Import data rows and labels using CSV upload with schema mapping.
→From in-house tools: Use Labelbox SDK (Python) to batch-create projects, ontologies, and label rows.
Migrating out
↗To SuperAnnotate: Export annotations via Labelbox SDK in COCO or JSON format, then import into SuperAnnotate.
↗To Scale AI: Use Labelbox export API to retrieve labels in your preferred format and upload to Scale.
↗To Custom pipeline: Export data rows and labels via Labelbox SDK or CSV, and transform as needed.
↗To CVAT: Export in COCO format and import into CVAT.
↗To Hugging Face Datasets: Use Labelbox SDK to export and convert to HF Dataset format.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
•2026-05-04: Added AI critics to audio/video editors, Ctrl+F search in document editor, scoreless multi-labeling, read-only predictions, and Foundry model updates.
•2026-04-06: Added custom roles compatible filters, audio transcription in editors, and removed public demo workspace.
•2026-02-10: Acquired Upcraft to integrate AI agent technology into Alignerr for scaling expert training.
•2026-01-20: Introduced program to help founders convert dormant codebases into value via secure one-time transactions.
