Top 3 Appen Alternatives in 2025 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.

Detailed analysis of Appen for workers

In this section, we analyzed worker reviews from leading review platforms.

Clickworker

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

• Sign-up/Onboarding: Sign up for free through your computer or mobile phone. Users found the signup process easy.
• Compensation: Payments are promised to be processed on a weekly basis. Payments are made via Payoneer or PayPal. Some state that the payment process took around 30 days.

Amazon Mechanical Turk

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

• Sign-up/Onboarding: Workers faced sign-up calculations without reasoning. MTurk support staff did not provide any support regarding the workers’ sign-up process. Most reviews regarding the sign-up process were negative.
• Compensation: While some workers found the pay rates decent, most found them low and stated that other platforms offer better pay. Most reviews regarding compensation were negative.

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 stated that the sign-up process requires unnecessary personal information and involves multiple tests during onboarding.
• Compensation: Workers found the payment system misleading. The time allocated to finish jobs is less, which reduces the hourly pay. Several workers faced payment delays.

Appen alternatives for customers

* The data is based on B2B review platforms such as G2, Trustradius, and Capterra.

• Companies are ranked based on the number of reviews.
• The table is created by using publicly accessible and verifiable data.
• All vendors offer data collection and data annotation services

Appen review

Appen offers data collection and management services for each stage of the AI project lifecycle through a crowdsourcing model. Their data is mainly for developing, implementing, and improving AI-powered solutions such as computer vision (CV), facial image recognition, voice recognition solutions, etc.

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.² 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 revenues come from its top 5 biggest customers. This suggests that smaller customers may not receive priority over larger ones.

A screenshot from the report:

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 an hour, which is much lower than 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.

For a more detailed review of Appen’s customer and user reviews, check out this article.

Detailed analysis of Appen customers

This section highlights alternatives to Appen and how they address its gaps. We evaluated competitors based on reviews from platforms such as G2, Trustpilot, and Gartner, as well as annual reports for customer and financial insights. We also compared the size of each company’s data collector network.

Size comparison of each alternative’s crowd 

The crowd refers to the human workers within the company’s network who are signed up for micro-projects.

Figure 1. Crowd size comparison of all the alternatives and Appen

Clickworker

Best for enterprises with its large crowd & reliability

Visit Website

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 Clickworker team will act as project managers.

Pros and cons

1. Comments regarding Clickworker’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
• Clickworker 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.

One of the downsides we identified with Clickworker is that their crowd covers fewer languages compared to Appen (45).

Choose Clickworker to obtain diverse and custom datasets for your AI projects.

Amazon Mechanical Turk

Best for small projects

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 million³ , studies show that there are around only 100,000⁴ workers available on the platform.
• It was also identified⁵ that a large number of MTurk’s workers use Large language models for text production tasks.

For a more detailed evaluation of Amazon Mechanical Turk, check out this article.

You may want to try alternatives to Amazon Mechanical Turk due to some of these drawbacks identified in customer reviews.

TELUS International

Best for multiple language requirements

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 of Appen solutions

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

External Links

• 1. Appen, which helps Amazon and Google train AI, is reeling. CNBC
• 2. Before you continue.
• 3. Welcome to the Amazon Mechanical Turk Requester User Interface Guide - Amazon Mechanical Turk.
• 4. Demographics and Dynamics of Mechanical Turk Workers | Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining.
• 5. [2306.07899] Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks.