The data behind breakthroughs
Users generally appreciate Labelbox for its robust features in facilitating data labeling and annotation tasks, highlighting its user-friendly interface and efficient workflow management as major strengths. However, key complaints often revolve around occasional software glitches and a desire for improved customer support. Pricing sentiment appears mixed, with some users feeling the cost is justified by its capabilities, while others view it as somewhat expensive for the value offered. Overall, Labelbox maintains a positive reputation among users for enhancing productivity in AI data management, despite some areas needing improvement.
Mentions (30d)
0
Reviews
0
Platforms
2
Sentiment
0%
0 positive
Users generally appreciate Labelbox for its robust features in facilitating data labeling and annotation tasks, highlighting its user-friendly interface and efficient workflow management as major strengths. However, key complaints often revolve around occasional software glitches and a desire for improved customer support. Pricing sentiment appears mixed, with some users feeling the cost is justified by its capabilities, while others view it as somewhat expensive for the value offered. Overall, Labelbox maintains a positive reputation among users for enhancing productivity in AI data management, despite some areas needing improvement.
Features
Use Cases
Industry
information technology & services
Employees
460
Funding Stage
Series D
Total Funding
$188.9M
Comparing data annotation platforms [D]
Scale AI Highest quality in the industry. But no public pricing and every project requires a sales call. Onboarding takes weeks not days. In June 2025 Meta bought a 49% stake and hired Scale’s CEO as Meta’s Chief AI Officer. Several major customers quietly reduced engagements over data exposure concerns. Worth thinking about if you’re building anything competitive with Meta. Best for: well-funded teams with enterprise security requirements and long timelines. Appen Over 1 million contractors across 170 countries. Sounds impressive until you realize it was built for massive long-term projects. Small teams consistently report it being slow and inflexible for novel tasks. Low contractor pay rates also raise real questions about annotation quality. Best for: high volume, low complexity, multilingual tasks. CloudFactory Trained dedicated teams and ethical sourcing. More consistent than the giants. Still not self-serve though and onboarding takes time. Project management quality varies depending on which team you get. Best for: structured projects with clear requirements and no time pressure. LabelBox Best annotation software on the market. The catch is it’s a platform not a workforce. You still need to find and manage your own annotators. Powerful if you have an internal team. Not useful if you don’t. Best for: teams building long-term internal annotation infrastructure. The problem!! Every major platform is optimized for enterprise scale. None of them are built for teams that need 500-2000 examples labeled fast, with domain expertise, and full transparency into who’s doing the work. What are you currently using for annotation work? submitted by /u/Neil-Sharma [link] [comments]
View originalYes, Labelbox offers a free tier. The pricing model is subscription + freemium + tiered.
Key features include: Data for reinforcement learning, Evals, Robotics, Alignerr expert network, Latest work from Labelbox Research, Discover how top models perform with Labelbox Leaderboards, Fueling cutting-edge research.
Labelbox is commonly used for: Image annotation for autonomous vehicles, Text classification for sentiment analysis, Video labeling for surveillance systems, 3D point cloud annotation for robotics, Medical image segmentation for diagnostics, Natural language processing for chatbots.
Labelbox integrates with: AWS S3, Google Cloud Storage, Azure Blob Storage, Kubernetes, TensorFlow, PyTorch, Jupyter Notebooks, Slack, Zapier, GitHub.
