In the era where artificial intelligence is transforming industries across the board, data annotation remains one of the most critical components in building high-performing machine learning models. While Scale AI has long been a dominant player in the annotation services space, it’s not the only game in town. As we move into 2025, several alternative companies and platforms are emerging as serious contenders, offering innovative, reliable, and flexible data labeling solutions. These alternatives cater to diverse industries including autonomous vehicles, healthcare, e-commerce, robotics, and more.
Whether you’re a startup looking for budget-friendly options or a large enterprise seeking scalability and custom workflows, exploring other data annotation providers can uncover valuable capabilities that suit your needs. In this article, we explore the top alternatives to Scale AI that are making waves in 2025.
1. Labelbox: Comprehensive Platform for AI Training Data
Labelbox has carved out a leading position by offering an all-in-one solution for managing training data more efficiently. It combines labeling tools, data management, and a flexible API layer that allows teams to build and scale annotation pipelines with ease. One of Labelbox’s biggest strengths lies in its support for human-in-the-loop workflows and automation tools that speed up labeling without compromising quality.
- User-friendly interface with robust collaborative features
- Supports image, video, text, and geospatial data
- Custom workflows tailored to industry-specific models
Organizations in robotics and autonomous vehicles particularly favor Labelbox due to its hands-on quality assurance tools and diagnostic reporting dashboard.
2. Snorkel AI: Programmatic Labeling for Speed and Accuracy
For teams looking to move beyond manual labeling at scale, Snorkel AI is quickly becoming a go-to choice. Originally developed at Stanford, Snorkel focuses on programmatic data labeling, where users create labeling functions that act as heuristics to annotate large quantities of data automatically. This model is ideal for organizations facing tight deadlines or lacking massive human workforces for annotation.
- Fast deployment times due to automated labeling
- Strong in natural language processing (NLP) and tabular data
- Excellent for regulated industries like healthcare and finance
Snorkel also offers tools that help refine labeling functions over time, ensuring the training data improves alongside model performance. It’s a highly scalable option that works best for data-rich environments.
3. SuperAnnotate: Scalable Teams and Workflow Automation
A favorite among businesses combining human annotators and ML automation, SuperAnnotate provides an AI-first platform that makes it easy to annotate images, videos, LiDAR data, and text. What sets it apart in 2025 is its integrated ecosystem – combining annotation tools, team management features, and automated QA – all in one environment.
- Specialization in computer vision and 3D data annotation
- Customizable roles and detailed audit trails
- Seamless integration with popular ML pipelines
SuperAnnotate’s granular control over annotations, as well as support for integrating plug-ins and automations, makes it a top-tier solution for companies seeking efficiency and consistency in their AI development workflows.
4. Appen: Crowd-Sourced Precision with Global Reach
Appen remains a stalwart in the data annotation landscape due to its ability to mobilize a global workforce on demand. In 2025, Appen continues to invest in AI tools that enhance human labeling, while remaining flexible enough to handle voice, NLP, image, and video data. They’re especially strong in multilingual data and culturally nuanced annotation tasks.
- Global human-in-the-loop annotator network
- Excellent for localizing AI products for different markets
- Strong track record in ethical data usage and inclusivity
Appen’s enterprise tools also include project management dashboards and real-time analytics to monitor progress, making it a comfortable fit for mature ML organizations.
5. Datasaur: NLP-Centric Labeling with Smart Suggestions
If your projects primarily deal with natural language processing, Datasaur is a focused alternative that blends offline-human and real-time AI assistance. It is designed for labeling tasks like named entity recognition (NER), sentiment analysis, and text classification. Its smart suggestions and collaboration features allow annotators and reviewers to streamline their tasks with precision.
- Highly optimized UI for textual data
- Multi-language support and advanced review mechanisms
- Built-in automation for faster iteration and quality checks
It’s particularly loved by startups in conversational AI and chatbots looking for rapid development cycles and accurate intent classification.
6. Playment (Now Telus International AI Data Solutions): Full-Stack Enterprise Data Labeling
Acquired by Telus International, Playment has grown into one of the most trusted alternatives for enterprise data labeling as of 2025. Whether it’s annotating automotive LiDAR data or conducting pixel-level segmentation in biomedical imagery, Playment provides robust tooling and globally distributed annotation teams designed for scale.
- Handles high-volume, high-precision projects
- Highly configurable tasks and workflows
- Trusted by Fortune 500 and defense contractors
Thanks to their in-house quality control automation and experienced project management team, they’ve become a top choice for companies needing top-tier results.
7. Humans in the Loop: Ethical, Human-Centric Labeling
One unique provider that has garnered attention in 2025 is Humans in the Loop, a social enterprise that blends high quality annotation with a strong ethical mission. Focused on providing jobs to refugees, displaced communities, and people from conflict zones, they offer a different kind of value proposition: models that are fair, inclusive, and responsibly trained.
- Human-powered annotation focused on fairness and inclusion
- Ideal for nonprofits, social impact startups, and academia
- High quality assurance standards and transparent reporting
In today’s landscape where ethical AI is more important than ever, this platform proves that high-quality training data and social responsibility can go hand in hand.
Why Consider Alternatives to Scale AI?
While Scale AI has established itself as a leader, several reasons may prompt teams to explore alternatives in 2025:
- Cost scalability: Some businesses need more flexible pricing models and levels of service.
- Domain-specific needs: Platforms like Datasaur or SuperAnnotate cater better to niche use cases.
- Custom workflows: Alternatives often allow for greater control over workflows, automation, and QA.
- Ethical considerations: Vendors like Humans in the Loop bring a socially responsible aspect to data labeling.
Conclusion
Data annotation continues to play a pivotal role in bringing AI systems to life. As enterprises and innovators push the boundaries of what’s possible with machine learning, having the right annotation partner is non-negotiable. While Scale AI remains a strong option, the alternative platforms examined here—Labelbox, Snorkel AI, SuperAnnotate, Appen, Datasaur, Humans in the Loop, and Playment—offer compelling capabilities and benefits that may be a better fit depending on your project’s goals and complexity.
In 2025, the landscape is diverse, rich, and rapidly evolving. It’s a great time for teams to reassess their tools and partnerships in data annotation to ensure they are building AI that is not just powerful, but also ethical, inclusive, and future-ready.
