RakshaFor decades, Appen has been one of the biggest names in AI data services, offering text, speech, image, and video annotation at scale. Many global companies have used its workforce for projects like speech recognition, computer vision, and NLP training.
But in recent years, the AI landscape has changed. Clients are now demanding:
- Higher-quality datasets
- Stronger compliance with GDPR, CCPA, and other regulations
- Niche participant recruitment for voice and image data
- Ethical sourcing and fair treatment of workers
As a result, many businesses are exploring Appen alternatives that better meet the needs of today’s AI development.
Here’s a look at who’s leading the way in AI data services.
1. Twine AI
Twine AI delivers end-to-end data collection and annotation services for text, audio, video, and image. Twine curates its contributor network and manages projects directly with clients.
Why it’s a strong alternative:
- Curated global contributors instead of an open crowd
- Multilingual and multicultural datasets to train inclusive AI
- Video and voice data expertise (accents, dialects, diverse, emotional tone)
- Compliance-focused
- Dedicated project management for accuracy and scalability
Best for: Teams who need reliable, diverse, and ethically sourced training data without the risks of unmanaged crowdsourcing.
2. Scale AI
Scale AI blends automation and human review to provide high-quality data annotation, especially in computer vision and autonomous vehicles.
Strengths:
- Excellent for image and video annotation
- Automation speeds up large-scale projects
- Used by the automotive and robotics industries
Considerations:
- More enterprise-focused
- Not always the best fit for smaller projects or niche recruitment
3. Clickworker (LXT)
Clickworker connects businesses with over 4.5 million contributors worldwide, while its partner company, LXT, focuses on AI/ML data services.
Strengths:
- Multilingual coverage across 45+ languages
- Large pool of available workers
- Flexible project setups
Considerations:
- Quality varies depending on task design and QC processes
- Less specialised in managed services
4. Toloka
Toloka is a crowdsourcing platform with built-in quality control mechanisms, making it stronger than traditional open marketplaces.
Strengths:
- Wide contributor reach
- Supports text, audio, image, and video tasks
- More structured QC compared to gig-based competitors
Considerations:
- Still primarily crowdsourcing, so may lack the oversight of managed providers
5. CloudFactory
CloudFactory provides dedicated, managed annotation teams for AI data projects.
Strengths:
- Ongoing workforce for consistent results
- Strong focus on workflow management and accuracy
- Enterprise-level security
Considerations:
- Higher cost compared to crowdsourced platforms
- Better suited for long-term projects than one-off datasets
Why Look for Appen Alternatives?
While Appen has scale and global reach, a few reasons to consider:
- Quality inconsistencies due to reliance on crowd labor
- Worker conditions and ethical issues
- Delays in onboarding or project responsiveness
- High costs compared to more agile providers
As AI becomes more integrated into everyday products and services, the quality of training data directly impacts performance and trust.
Final Thoughts
Today’s AI projects need more than just access to a global workforce. They require tailored datasets, strong compliance, and trusted project management.
By exploring Appen alternatives, from managed providers like Twine AI and CloudFactory to hybrid platforms like Scale AI and Toloka, businesses can find a partner that better matches their data, budget, and compliance needs.
The right choice depends on your project’s goals, but one thing is clear: the future of AI data services is shifting toward quality, diversity, and ethical practices.
Twine
Twine AI