Labelbox charges $0.10 per "Labelbox Unit" (LBU), a simplified currency that equates to one labeled asset, five model predictions, or 60 stored catalog items. For a team labeling 5,000 images a month, you are looking at roughly $500/month just for the software license—before you pay a single human to draw a bounding box. It’s a steep entry price compared to open-source alternatives like Label Studio, but you aren't paying for just a canvas; you're paying for the orchestration layer between your data, your models, and your workforce.
The platform has successfully pivoted from a pure computer vision tool to a "Data Factory" for GenAI. Its strength lies in "Foundry," where you can use foundation models (Gemini, Claude, etc.) to pre-label data, drastically reducing human effort. Instead of drawing 1,000 boxes, your team reviews 1,000 model suggestions. The interface for RLHF and chat evaluation is currently industry-leading, handling multi-turn conversations and complex ranking tasks that older labeling tools struggle to visualize.
Technically, the architecture is robust but opinionated. The API is GraphQL-first, which provides immense flexibility in querying complex relationships (e.g., "show me all 'car' labels created by 'User A' with low consensus scores"), but it imposes a steeper learning curve than standard REST endpoints. The Python SDK (now on v7.5+) creates a nice abstraction layer, but you will eventually need to understand the underlying graph schema to build advanced automation pipelines.
The primary downside is the cost structure. The LBU model penalizes volume. Storing 1 million images in their Catalog without even labeling them costs ~$1,600/month (1M / 60 rows per LBU * $0.10). If you are a solo developer or a startup with a tight budget, this math breaks quickly.
Skip Labelbox if you just need to draw boxes on images; use CVAT or Label Studio. Use Labelbox if you are an enterprise AI team building a permanent pipeline where data requires strict access control, multi-stage review workflows, and HIPAA/SOC2 compliance.
Pricing
The free tier offers 500 LBUs/month. Since 1 labeled image = 1 LBU, you can label 500 assets monthly for free. However, storage also consumes LBUs (1 LBU = 60 stored assets), so a stagnant catalog of 30,000 images would consume your entire free tier without any labeling. The $0.10/LBU starter rate is high; labeling a 10k dataset costs $1,000 purely for software utility. There is no "seat-based" pricing in the self-serve tier, meaning costs scale linearly with data volume, not team size.
Technical Verdict
High-quality engineering. The Python SDK (v7.5.0) is modern, utilizing Pydantic v2 for strong typing and validation. Documentation is excellent, particularly for the GraphQL API, which is powerful but complex. Expect a learning curve if you aren't familiar with graph queries. Integration with cloud buckets (AWS/GCP/Azure) is seamless via delegated access.
Quick Start
import labelbox as lb
# Auth and Client setup
client = lb.Client(api_key="YOUR_API_KEY")
# Create a dataset and add a data row
dataset = client.create_dataset(name="Quickstart Dataset")
task = dataset.create_data_rows([{"row_data": "https://picsum.photos/200"}])
task.wait_till_done()
print(f"Dataset created: {dataset.uid}")Watch Out
- Storage costs money: Storing data in their Catalog consumes credits (LBUs) even if you don't label it.
- GraphQL curve: The API is powerful but requires learning their schema; simple REST calls aren't the primary method.
- No easy self-host: It is a SaaS-first platform; on-premise options are gated behind heavy enterprise contracts.
- LBU complexity: 1 LBU ≠ 1 Task. It varies by product (Annotate vs Model vs Catalog), making bill forecasting difficult.