Labelbox vs Scale AI | Features, Pricing & Differences
Averroes
Oct 28, 2025
Comparing Labelbox vs Scale AI comes down to how your team handles data quality, scale, and control.
Both platforms promise smarter annotation workflows and better training data, but they’re built for different types of organizations and goals.
We’ll break down features, pricing, pros, and cons of each – plus introduce a third option for teams that want scalability without losing ownership.
Key Notes
Labelbox offers transparent LBU pricing and in-house control, while Scale AI provides a managed workforce.
Scale AI excels at extreme volume with 3D/LiDAR; Labelbox suits MAL automation workflows.
Labelbox provides per-labeler QA visibility; Scale AI shows strong outcomes with limited transparency.
Labelbox Overview
Labelbox is a comprehensive, user-friendly data labeling platform for teams that want to own their annotation workflows. It supports images, video, text, audio, geospatial, and multimodal projects.
The value proposition is simple. Labelbox acts like an AI data factory, combining annotation tooling, model-assisted labeling, collaboration, QA analytics, and dataset management so you can produce high-quality training data faster.
Core Features
Labeling interface and automation. Bounding boxes, polygons, masks, keypoints, text, and audio tasks, with AI assistance that pre-labels and lets humans correct.
Model Assisted Labeling (MAL). Plug in your model. Pre-label, then review. Teams report 50-70%time savings and better focus on edge cases.
Data management and versioning. Immutable snapshots, governed splits, class balance tools, slices for analysis, and audit trails.
QA and analytics. Consensus scoring, reviewer workflows, error analysis vs ground truth, inter-annotator agreement checks.
APIs and integrations. Python SDK, REST API, cloud storage connectors for S3, GCS, Azure, plus IAM and SSO.
Pros:
Intuitive UI: Reduces ramp time for annotators and reviewers
Strong Automation: MAL enables faster cycles and lower cost per label
Deep Collaboration: Role-based access with real-time presence
Transparent QA: Actionable metrics across projects and labelers
Cons:
Pricing Complexity: LBU consumption is transparent but modeling costs take effort; heavy review and rework can surprise budgets
Setup Learning Curve: Ontology, workflow, and QA configuration need discipline up front
Performance Planning: Very large video or dense annotations may require asset optimization and better hardware
Labelbox Pricing
Labelbox uses Labelbox Units (LBUs) across Catalog, Annotate, and Model. Starter rates are roughly per unit, with volume discounts at scale. You pay for storage, labeling events, and model operations.
The model is transparent, but you need to plan. Consensus, multi-stage review, and long retention drive LBUs.
For small to mid projects, costs often land in the low hundreds to low thousands per month. For very large volumes, costs scale with you.
Scale AI is a managed labeling and data operations provider built for very large, complex programs. Think enterprise and government workloads with strict SLAs.
Core idea: Scale supplies the trained workforce, process, QA layers, and automation so your team does not have to manage annotators. You send data, define rules, and receive labeled datasets with strong quality guarantees.
Core Features
Managed workforce and QA. Global vetted annotators, consensus validation, gold tasks, programmatic checks, and adjudication.
Automation and pipelines. Pre-labeling, rule validation, auto-raters for subtle error detection, and continuous screening of annotators.
Multi-modality depth. Images, video, LiDAR and 3D, NLP, audio, and synthetic data.
Enterprise integration. APIs, SDKs, and governance that fit data ops and MLOps at scale.
Pros:
High Throughput: Strong quality control at extreme volumes
Broad Modality Coverage: Includes 3D and LiDAR capabilities
Reduces Operational Overhead: Ideal for teams that prefer not to run annotation internally
Cons:
Cost: Premium service with enterprise contracts ranging from five to six figures depending on scope
Opacity: Less visibility into per-labeler metrics and day-to-day processes
Limited Control: Fewer levers for teams that need to tune workflows in detail
Scale AI Pricing
Pricing is quote-based. Variables include task complexity, modality, volume, workforce model, QA layers, and add-ons like synthetic data.
Self-serve paths exist, but most enterprise deals are custom. Budget forecasting improves with tight scoping and usage monitoring, yet variability can remain over long programs.
Unified visual data management and AI-assisted labeling platform
Deployment
Cloud plus on-prem paths
Primarily cloud, service-led
Cloud-first with 200+ integrations
QA visibility
Consensus, reviewer metrics, slices
Strong outcomes, limited per labeler visibility
Real-time metrics, inter-annotator agreement tracking, and automated QA workflows
Video labeling
Solid but may need optimization
Supported across large programs
Advanced image and video annotation with AI acceleration and real-time collaboration
Integrations
APIs, SDK, S3, GCS, Azure, SSO
APIs, SDKs, enterprise program support
200+ integrations including MES, QMS, SCADA, Power BI, Azure, and cloud storage connectors
Pricing
LBU usage model, transparent with planning
Quote-based, variable by scope
Starts free with tiered plans – scaling by users, storage, and labeling credits
Who Should Choose What
Choose Labelbox if you want an intuitive, API rich platform for in-house teams, need MAL to speed cycles, and value transparent QA analytics. It fits startups through many enterprise cases when you prefer control over outsourcing.
Choose Scale AI if you need to outsource at scale, want a vetted workforce with layered QA, and your priority is throughput and deadlines across complex modalities such as LiDAR. Expect higher cost and plan for governance.
The Third Option: Vision Repo
If you need scalability without the usual complexity, Vision Repo bridges both worlds.
It combines AI-assisted labeling, automated QA, and centralized visual data management in one secure cloud platform – built for teams that want speed, accuracy, and clarity from day one.
Unlike Labelbox and Scale AI, Vision Repo doesn’t hide behind opaque pricing or rigid enterprise tiers. It’s transparent, easy to start, and purpose-built to help teams get value from their visual data immediately – not after six months of setup.
Want Smarter Labeling Without Losing Control?
Manage, annotate, and analyze – all from one secure hub.
Frequently Asked Questions
Is Scale AI better than Labelbox for computer vision?
It depends on volume, modalities, and control. Scale AI excels with very large and complex computer vision programs including LiDAR, backed by a managed workforce and layered QA. Labelbox suits teams that want to run CV labeling in-house with strong automation and QA visibility.
Which platform offers better security and governance?
Both platforms meet enterprise-grade compliance standards. Scale AI operates as a managed, cloud-only service with limited data control. Labelbox offers hybrid and on-prem flexibility for highly regulated environments. Vision Repo, while cloud-based, ensures full data ownership, SOC 2 compliance, and granular access controls – making it a strong choice for teams that need security without infrastructure headaches.
How do Labelbox and Scale AI compare on pricing predictability?
Labelbox is transparent via LBUs, but you must model consensus and rework. Scale AI is quote-based with variability by scope. Long-term programs need tight scoping and usage monitoring to manage budgets on either platform.
Conclusion
Choosing between Labelbox vs Scale AI depends on what your team values most: control, scalability, or convenience.
Labelbox gives you visibility and hands-on flexibility with transparent pricing and strong automation. Scale AI delivers throughput and enterprise-grade QA at a higher cost through its managed workforce.
But for teams that want efficiency and autonomy together, Vision Repo brings the best of both. It unifies data management and annotation in one AI-assisted workspace, complete with workflow automation, real-time QA, and flexible deployment options.
Get started now with Vision Repo to centralize your visual data, label with speed and accuracy, and scale operations confidently – without compromising control or budget.
Comparing Labelbox vs Scale AI comes down to how your team handles data quality, scale, and control.
Both platforms promise smarter annotation workflows and better training data, but they’re built for different types of organizations and goals.
We’ll break down features, pricing, pros, and cons of each – plus introduce a third option for teams that want scalability without losing ownership.
Key Notes
Labelbox Overview
Labelbox is a comprehensive, user-friendly data labeling platform for teams that want to own their annotation workflows. It supports images, video, text, audio, geospatial, and multimodal projects.
The value proposition is simple. Labelbox acts like an AI data factory, combining annotation tooling, model-assisted labeling, collaboration, QA analytics, and dataset management so you can produce high-quality training data faster.
Core Features
Pros:
Cons:
Labelbox Pricing
Labelbox uses Labelbox Units (LBUs) across Catalog, Annotate, and Model. Starter rates are roughly per unit, with volume discounts at scale. You pay for storage, labeling events, and model operations.
The model is transparent, but you need to plan. Consensus, multi-stage review, and long retention drive LBUs.
For small to mid projects, costs often land in the low hundreds to low thousands per month. For very large volumes, costs scale with you.
How to Get Started with Labelbox
Visit Labelbox
Scale AI Overview
Scale AI is a managed labeling and data operations provider built for very large, complex programs. Think enterprise and government workloads with strict SLAs.
Core idea: Scale supplies the trained workforce, process, QA layers, and automation so your team does not have to manage annotators. You send data, define rules, and receive labeled datasets with strong quality guarantees.
Core Features
Pros:
Cons:
Scale AI Pricing
Pricing is quote-based. Variables include task complexity, modality, volume, workforce model, QA layers, and add-ons like synthetic data.
Self-serve paths exist, but most enterprise deals are custom. Budget forecasting improves with tight scoping and usage monitoring, yet variability can remain over long programs.
Visit Scale AI
Feature by Feature Comparison
Who Should Choose What
Choose Labelbox if you want an intuitive, API rich platform for in-house teams, need MAL to speed cycles, and value transparent QA analytics. It fits startups through many enterprise cases when you prefer control over outsourcing.
Choose Scale AI if you need to outsource at scale, want a vetted workforce with layered QA, and your priority is throughput and deadlines across complex modalities such as LiDAR. Expect higher cost and plan for governance.
The Third Option: Vision Repo
If you need scalability without the usual complexity, Vision Repo bridges both worlds.
It combines AI-assisted labeling, automated QA, and centralized visual data management in one secure cloud platform – built for teams that want speed, accuracy, and clarity from day one.
Unlike Labelbox and Scale AI, Vision Repo doesn’t hide behind opaque pricing or rigid enterprise tiers. It’s transparent, easy to start, and purpose-built to help teams get value from their visual data immediately – not after six months of setup.
Want Smarter Labeling Without Losing Control?
Manage, annotate, and analyze – all from one secure hub.
Frequently Asked Questions
Is Scale AI better than Labelbox for computer vision?
It depends on volume, modalities, and control. Scale AI excels with very large and complex computer vision programs including LiDAR, backed by a managed workforce and layered QA. Labelbox suits teams that want to run CV labeling in-house with strong automation and QA visibility.
Which platform offers better security and governance?
Both platforms meet enterprise-grade compliance standards. Scale AI operates as a managed, cloud-only service with limited data control. Labelbox offers hybrid and on-prem flexibility for highly regulated environments. Vision Repo, while cloud-based, ensures full data ownership, SOC 2 compliance, and granular access controls – making it a strong choice for teams that need security without infrastructure headaches.
How do Labelbox and Scale AI compare on pricing predictability?
Labelbox is transparent via LBUs, but you must model consensus and rework. Scale AI is quote-based with variability by scope. Long-term programs need tight scoping and usage monitoring to manage budgets on either platform.
Conclusion
Choosing between Labelbox vs Scale AI depends on what your team values most: control, scalability, or convenience.
Labelbox gives you visibility and hands-on flexibility with transparent pricing and strong automation. Scale AI delivers throughput and enterprise-grade QA at a higher cost through its managed workforce.
But for teams that want efficiency and autonomy together, Vision Repo brings the best of both. It unifies data management and annotation in one AI-assisted workspace, complete with workflow automation, real-time QA, and flexible deployment options.
Get started now with Vision Repo to centralize your visual data, label with speed and accuracy, and scale operations confidently – without compromising control or budget.