Best Appen Alternatives in 2026 for Workers & Customers

Appen, an AI data service provider, faces challenges that may explain its declining popularity.¹

We compared the top alternatives to Appen in the AI training data space. The alternatives to Appen depend on your goals. Explore alternatives for Appen’s:

• Workers or contributors.
• Customers or buyers who rely on data from Appen.

Appen alternatives for workers

* Data is from Trustpilot, as it primarily consists of worker reviews.

** Data gathered from worker reviews. In some cases, workers reported payment delays.

Summarize This Research

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Detailed analysis of alternatives for workers

Clickworker

Clickworker offers a data-collection and generation platform based on a crowdsourcing model. Here are some aspects for workers to consider:

• Sign-up/Onboarding:
  • Free signup via computer or mobile
  • Users report “easy” registration process
  • Qualification assessments for specific job types
• Compensation:
  • Promised weekly payments
  • Payment via Payoneer or PayPal
  • Some workers report a 30-day actual payment time
  • Average: $8-15/hour

Amazon Mechanical Turk

MTurk offers a marketplace for AI data services and other AI development tasks. Workers interested in signing up should consider the platform’s key features.

• Sign-up/Onboarding:
  • Difficult signup process with unexplained rejections
  • Workers report no support during signup
  • “Calculations without reasoning” required
• Compensation:
  • Weekly payment
  • Client sets worker pay per task
  • Average: $2-8/hour (below minimum wage)
  • 20% Amazon fee + additional 20% for tasks with 10+ assignments
  • Extra 5% charge for “Master” qualified workers

Telus International

Telus International claims to operate with a network of over 1 million contributors in its crowdsourcing platform. Here are some aspects for workers to consider:

• Sign-up/Onboarding:
  • Workers report “unnecessary personal information” required
  • Multiple tests during onboarding
  • Complex qualification process
• Compensation:
  • Monthly payment
  • The payment system is described as misleading.
  • Allocated time per job is “less than needed,” reducingthe effective hourly rate
  • Average: $10-18/hour when paid on time

Sama

Social enterprise founded to alleviate poverty through digital work. Part of the Sama Group nonprofit.

Sign-up/Onboarding:

• Basic computer skills training provided
• Tests for logical reasoning, image/video processing
• Focus on hiring from low-income communities

Compensation:

• Monthly payment
• “Living wages” for local markets
• Medical insurance, dental coverage, maternity benefits
• Average varies significantly by country

Lionbridge AI

One of the oldest players in the AI training data space, founded in 1996. Employs 6,000+ people across 26 countries.

Sign-up/Onboarding:

• Extensive qualification tests (10+ hours unpaid)
• Language and skill certifications required
• W8-BEN form needed for non-US workers
• Workers report the process as lengthy but straightforward.

Compensation:

• Monthly payment via PayPal or Payoneer
• Rates vary by project: $11-15/hour
• 20-hour weekly cap on many projects

Appen alternatives for customers

* The data is based on B2B review platforms.

Companies are ranked by the number of reviews, and the table is created from publicly accessible, verifiable data.

Appen review

Appen offers data collection and management services across the AI project lifecycle through a crowdsourcing model. Their data is primarily used to develop, implement, and improve AI-powered solutions, including computer vision (CV), facial recognition, and voice recognition.

Appen’s services/offerings

• The data collection service is based on either crowdsourcing or a managed service model.
• Data collection of all data types (image, video, audio, text)
• Data annotation (audio, video, text, image annotation) and model evaluation services

Appen evaluation

We divided the pros and cons into ‘client’s perspective’ and ‘workers’ perspective’.

Client’s perspective of Appen

1. Weak financial situation

Appen has experienced significant financial losses over the past few years. This may impact their performance. Appen had a revenue decrease. These losses also impacted their customers.

Appen stock price decline:

2. Dependent on large customers

We also identified that over 80% of Appen’s revenue comes from its top 5 customers. This suggests that smaller customers may not receive priority over larger ones.

3. Lack of transparency

• The company also does not provide any details about the crowd’s demographics, qualifications, or diversity.

Worker’s perspective

1. Difficult-to-use UI 

Some sources describe the platform’s user interface as complicated to use. Workers also found invoicing difficult, and issues regarding the unavailability of jobs:

2. Low compensation

Workers from Appen’s network also find the compensation rates low. According to some comments, the rates were as low as $2 per hour, which is well below the US minimum wage. This can be a problem for clients, as they may not want to work with a partner who engages in unfair compensation practices.

Detailed analysis of the top 3 alternatives for customers

LXT, the parent company of Clickworker, is a data collection service provider that works with a crowdsourcing model. The company offers the following services:

• Training datasets for training machine learning models in multiple languages and target markets.
• Data processing services (Turning raw data into relevant and accurate data) and image annotation
• Sentiment analysis data for open-source tools
• SEO content & text creation services
• Data categorization and tagging
• Conducting surveys & web research

Pricing

Prices depend on:

• The services that the crowd will perform.
• Whether the client will manage the crowd or leverage a managed service model, where the LXT team will act as project managers.

Pros and cons

1. Comments regarding LXT’s performance:

• Clients find the crowd “reliable to work with” and the platform “easy to use”.

2. Comments regarding the crowd:

• Largest network of workers among the competitors
• LXT provides the following information regarding its crowd on its website:
  • Demographic distribution: How many of the contributors are located in which parts of the world
  • Gender distribution: How many people from the crowd are male/female/other genders?
  • Details regarding their education: How many people from the crowd are high-school graduates, university graduates, or even PhDs?
  • Language: The weightage of the spoken languages in the crowd.

Amazon Mechanical Turk, also known as MTurk, is another platform that offers a crowdsourced marketplace for companies seeking an online workforce to outsource small tasks, also referred to as microtasks.

MTurk’s core offering is a platform or marketplace where clients can specify the service they require from the crowd and post it as an online task or job on the site. The crowd then performs these online tasks to earn extra money. These “human intelligence tasks (HITs)” or micro jobs can include:

• Data collection for developing and improving AI/ML models
• Data annotation/labeling/tagging 
• Conducting online surveys
• Developing machine learning models

Pricing

• The client decides workers’ pay for each assignment.
• Amazon Mechanical Turk charges 20% on the worker’s pay and an additional 20% on microtasks with 10 or more assignments.

Pros and cons

1. Scalable service:

• Clients can scale the workforce up or down based on demand, catering to fluctuating workloads and large-scale experiments.

2. Concerns regarding extra charges from customers:

• MTurk charges an extra 5% off the pay for workers with a Master’s qualification. However, some clients complained that the quality of the workers’ output is similar and not worth the extra charge.

3. Concerns regarding user-friendliness:

• Some survey service users claim that MTurk is not as user-friendly as its competitors:

4. Concerns regarding communication:

• Data collection customers stated that it is very difficult to follow up with the workers who complete the micro-jobs.

5. Quality issues:

• Studies identified quality issues in the research data gathered by MTurkers.
• Others hypothesized that quality issues could be due to the limited language abilities of the MTurk crowd and that most workers were not proficient in English:

6. Smaller crowd size than claimed

• While the company claims to have a crowd of around half a million ² , studies show that there are around only 100,000³ workers available on the platform.
• It was also identified⁴ that a large number of MTurk’s workers use Large language models for text production tasks.

You may want to consider alternatives to Amazon Mechanical Turk due to the drawbacks identified in customer reviews.

Telus International is a Canadian company that offers data collection services through a crowdsourcing model. Its offerings include:

• Data collection for training machine learning models
• Data entry, analysis, and enrichment
• Content summarization, formatting, and processing
• Dataset creation across multiple languages

Pros and cons

• Similar Crowd Size: Offers a similar crowd size to Appen.
• More Language Coverage: Covers 500+ languages and dialects.
• Higher Cost: Reviews suggest Telus International is more expensive than competitors, though pricing information is unavailable.

You can also check our data-driven list of data collection companies to find the best option for your business needs.

FAQs

Reference Links

1.

Appen, which helps Amazon and Google train AI, is reeling

CNBC

2.

Welcome to the Amazon Mechanical Turk Requester User Interface Guide - Amazon Mechanical Turk

3.

Demographics and Dynamics of Mechanical Turk Workers | Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

4.

[2306.07899] Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks

Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Researched by

Özge Aykaç

Industry Analyst

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Özge is an industry analyst at AIMultiple focused on data loss prevention, device control and data classification.

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