Labelbox Competitors: Top Alternatives for Data Labeling

How to Find the Best Alternative for Your Labeling Needs

When evaluating Labelbox competitors, think about the main challenge you need to solve and what your team values most. Broadly, options fall into three groups:

• Service-first providers like Label Your Data and Scale AI emphasize managed services, transparent pricing, and compliance. 
• Enterprise platforms such as Dataloop, SuperAnnotate, Encord, V7, and Kili combine advanced tooling with workforce management for teams running large-scale AI pipelines. 
• Open-source tools like CVAT and Label Studio are free and flexible but require technical setup and in-house QA.

The right choice of data annotation services for your project comes down to whether your priority is cost, compliance, scale, or flexibility.

Top 10 Enterprise-Ready Labelbox Competitors 

For teams evaluating Labelbox AI alongside other enterprise platforms, the main comparison points are workflow automation, model integration, and compliance. Below are the top Labelbox competitors for data annotation projects that ML teams most often evaluate, with their strengths, client feedback, and ideal use cases.

Label Your Data

As a service-first provider, Label Your Data delivers transparent per-object data annotation pricing with no hidden fees or platform lock-in. Alongside managed services, it offers a self-serve data annotation platform for computer vision projects, giving teams flexibility in how they run annotation. Clients value the free pilot option to test workflows risk-free, the 98%+ accuracy guarantee, and full compliance with ISO/IEC 27001, GDPR, and HIPAA. Reviews on G2 (4.9/5 from 15 reviews) and Clutch (5.0/5 from 26 reviews) consistently highlight predictable costs, reliable QA, and responsive support. These strengths position Label Your Data as the best Labelbox competitor for ML teams that want high-quality managed services without unnecessary complexity.

Check our Label Your Data company review for a deeper breakdown.

Dataloop

Dataloop positions itself as an enterprise platform for end-to-end AI development. It supports image, video, LiDAR, text, and audio annotation with advanced automation and active learning. Enterprise users note strong workflow management and scalability, though the learning curve can be steep. Best for teams running multimodal AI pipelines that require tight integration with cloud infrastructure and MLOps tools.

SuperAnnotate

SuperAnnotate combines an annotation platform with managed services. It offers advanced 2D/3D computer vision support, AI-assisted pre-labeling, and strong QA dashboards. With a 4.9/5 G2 rating from 160+ reviews, users often cite productivity gains and reliable quality. Some mention performance issues with very large datasets. Strong choice for enterprise teams needing both software flexibility and annotation services.

Read our full SuperAnnotate review for an in-depth evaluation.

Encord

Encord specializes in regulated industries, particularly healthcare and autonomous systems. It supports medical imaging formats (DICOM, NIfTI) and integrates models like SAM 2 and GPT-4o for assisted labeling. With a 4.8/5 G2 rating, users praise accuracy and compliance capabilities but note complexity in setup. Ideal for teams needing strict data governance and domain expertise.

V7 (Darwin)

V7, also known as Darwin, is built for fast, collaborative labeling. It supports auto-labeling, model training, and dataset management in one platform. Clients in life sciences and AV highlight its speed and dataset versioning. Reviewers often praise its user-friendly interface but flag high costs at scale. Best suited for research-heavy teams that want model-in-the-loop workflows.

Scale AI

Scale AI is a service-first competitor serving enterprises and government clients. It combines human labeling with AI validation, specializing in high-stakes applications like defense, AV, and LLM training. Customers note top accuracy but also higher costs. Large enterprises choose Scale AI when quality and security matter more than budget.

See our detailed Scale AI review for more insights.

Amazon SageMaker Ground Truth

Ground Truth is an AWS-native platform that integrates active learning and automation to reduce costs. Case studies show up to 27% cost savings on bounding box tasks through automated labeling. Works best for teams already embedded in AWS infrastructure. Less flexible for complex, non-standard workflows.

Kili Technology

Kili Technology targets enterprises with high-volume annotation needs and distributed workforces. It offers workforce analytics for teams of 1,000+ annotators and supports diverse data types, including satellite imagery and OCR. G2 reviews highlight stability and large-scale management capabilities but note limited video annotation features.

Find more insights in our full Kili Technology review.

I migrated from Labelbox to V7 because it offered better API flexibility and automated labeling that cut annotation time by 60%. It also integrated smoothly with our tech stack, and support was far more responsive — usually solving issues within hours instead of days.

Pavel Sher CEO, FuseBase

Open-Source & Budget-Friendly Alternatives

Open-source tools are popular Labelbox competitors for teams that prioritize flexibility and low cost over enterprise support. They’re especially relevant for startups, research labs, and technical ML teams with the expertise to manage setup, QA, and integrations themselves.

CVAT

CVAT (Computer Vision Annotation Tool), originally developed by Intel, is a free open-source platform designed for computer vision tasks. It supports bounding boxes, polygons, cuboids, and video annotation with interpolation. Users value its flexibility and no-cost access, but setup can be complex for self-hosted versions, and large machine learning datasets may strain performance. Best fit for teams with technical staff who can manage deployment and want to avoid vendor lock-in.

Explore our CVAT review for a closer look.

Label Studio

Label Studio supports a broader range of data types – images, text, audio, video, and time series. Its open-source edition is widely adopted (24K+ GitHub stars), with enterprise plans available for teams that need role-based access, advanced QA, or compliance features. Researchers praise its versatility and customization options, though enterprises often need the paid tier for security and scaling.

We recommended CVAT over Labelbox when a client needed more control at scale. It wasn’t as polished, but it gave us the flexibility to design custom workflows, add QA steps, and trigger active learning. That move reduced labeling costs by 30% in three months and eliminated vendor lock-in.

Niclas Schlopsna Managing Consultant and CEO, spectup

Comparing Labelbox Competitors Across Annotation Pricing and Features

Use this quick reference table as a guide you can save and share with your team when deciding which platform or service best fits your labeling needs.

One of the main reasons teams look for alternatives is Labelbox pricing, which often scales quickly with usage. Competitors like Label Your Data offer transparent per-object rates and free pilots, making costs easier to predict. Together with Scale AI, these service-first providers stand out for transparency and accuracy guarantees. 

Enterprise platforms such as Dataloop, SuperAnnotate, Encord, V7, and Kili bring advanced automation and domain expertise but often require custom contracts. 

Open-source tools CVAT and Label Studio remain the most flexible and budget-friendly, though they shift more QA and setup effort onto your team.

About Label Your Data

If you choose to delegate data labeling, run a free data pilot with Label Your Data. Our outsourcing strategy has helped many companies scale their ML projects. Here’s why:

No Commitment

Check our performance based on a free trial

Flexible Pricing

Pay per labeled object or per annotation hour

Tool-Agnostic

Working with every annotation tool, even your custom tools

Data Compliance

Work with a data-certified vendor: PCI DSS Level 1, ISO:2700, GDPR, CCPA

FAQ

What companies are competitors of Labelbox?

The main competitors include service-first providers like Label Your Data and Scale AI, enterprise platforms such as Dataloop, SuperAnnotate, Encord, V7 (Darwin), and Kili Technology, and open-source options CVAT and Label Studio.

Is Labelbox a good company?

Labelbox is widely adopted by enterprise ML teams. It’s known for advanced platform features and integrations but often criticized for cost. Top alternatives like Label Your Data are chosen by teams prioritizing transparency, lower pricing, or domain-specific expertise.

What is the difference between Labelbox and CVAT?

Labelbox is a commercial platform with usage-based pricing, automation features, and enterprise support. CVAT is an open-source tool, free to use but requiring technical setup and internal QA. Labelbox offers managed services and compliance, while CVAT is best for teams with strong in-house expertise.

Why do teams look for Labelbox alternatives?

The most common reasons are pricing, platform lock-in, and complexity. Many ML teams want transparent costs, free pilots, or simpler managed services without paying for features they don’t use.

Which Labelbox competitor is best for different types of teams?

Enterprises often pick SuperAnnotate, Encord, or Scale AI for complex, regulated projects and automation needs. ML engineers, AI business executives, or academic researchers lean toward Label Your Data for high-quality, transparent managed services, or CVAT/Label Studio when budgets are tight and technical expertise is available.