Sep 29

Top 5 alternatives to Appen for data sourcing and labeling

Machine learning (ML) use cases are complex and have unique requirements. As such, finding the right data vendor and AI development partner can be time consuming. Before defaulting to one of the better-known vendors, you should consider if there is a better fit for your needs. Each data vendor has a core focus where their strength lies. This means companies are differently equipped to help with your use case. Here are some of the top data sourcing alternatives for you to look into. 


Appen is a well-known data vendor in the AI industry. The company provides services within natural language processing (NLP) and computer vision, among others, and offers a number of different data products. Appen is a popular data labeling partner, but their solutions may not match what you are looking for in terms of language, support, price, or speed of delivery. There may be alternatives better suited to your AI use case. Check out our top picks below.

StageZero Technologies excels at speech and natural language processing

Your best choice for use cases within speech and text would be StageZero Technologies. Conversational AI and NLP are the company’s main focus. StageZero has a global user base of more than 10 million people, probably the largest on the market. 

StageZero was founded in 2016 and is based in Helsinki, Finland. The company provides an easy-to-use platform that heavily reduces the time needed to spend on data. Their services are human-centric rather than automated, and great attention is given to each use case to solve its particular problems. Every client gets assigned a contact they can reach out to when needed.

woman speaking with voice assistant speech data natural language processing

StageZero sources real and synthetic data in 40 languages, which is excellent if you plan to scale your business to other countries. 100% human-verified annotation services turn unstructured data into quality training data. The company can annotate your own data or data they sourced for you.

Each data point is verified by another human at least three times to reach an inter-annotator agreement. This allows StageZero to score over 10% higher accuracy in benchmarking tests with some of the market’s leading competitors. The company provides some of the most diverse data available today, reducing bias in AI models to a minimum.

Read more: Where to get ML training data: StageZero vs. crowdsourcing marketplaces

Scale AI for infrastructure and computer vision data

Scale AI is best for use cases within computer vision and AI-related infrastructure. Driven by a data-centric approach, the platform delivers high-quality data and provides end-to-end solutions to manage the entire ML cycle.

The company was founded in 2016 and is headquartered in San Francisco. Scale AI is specialized in retail, eCommerce and security systems. Typical use cases involve autonomous vehicles, robotics, AR/VR, document processing, content, and language. 

Scale AI offers different product packages, such as Scale Nucleus to qualify datasets, Scale Rapid for labeling, or Scale Studio to provide annotation tools for your own workforce. Scale 3D Sensor Fusion is an advanced annotation platform for 3D sensor data, while Scale Mapping is a flexible solution to develop and scale your custom maps.

Scale AI is one of the industry's most prominent players, making it a pricier option.

face recognition done by computer vision AI

Labelbox if you need tools for annotating

In certain situations, sharing data with third-party data vendors is not feasible. The best solution, in this case, is to have an in-house team do the work; however, it can be costly. For teams with such restrictions, we recommend Labelbox

Labelbox was founded in 2018 in San Francisco. The platform is built for annotating computer vision and NLP data. You pay a monthly fee for access to the tools instead of paying for each data point. You can then use internal or external experts to create and label data.

Labelbox supports several scenarios for labeling data: simultaneous labeling (done by both internal and external teams), labeling performed by an integrated managed data labeling workforce, and model-assisted labeling (using Labelbox Prediction API).

Labelbox is based on a flexible setup that can be easily customized to adapt to your workflows. Customers cite Labelbox as an intuitive and user-friendly platform.

Hive AI if you are looking for ready-made datasets in computer vision

For companies looking for ready-made algorithms for use cases such as brand recognition, crowd management, retail, and document processing, we would recommend Hive AI. The platform has a large selection of off-the-shelf algorithms you can license, and they support over 100 other cases, including self-driving and lidar.

Founded in 2013 in San Francisco, Hive AI provides cloud-based AI solutions and focuses on enterprise automation use cases. They work with a wide range of clients in automotive and transportation, financial services and insurance, media and entertainment, technology and communications, consumer goods and manufacturing, hospitality, travel, and leisure, among others.

Hive Data is a comprehensive, distributed data labeling platform with over 2 million registered global contributors. The company solves challenges for enterprises following a three-fold business model: Hive Data (a data labeling platform), Hive Predict (a set of proprietary deep learning models powering AI for corporate clients), and Hive Enterprise (packages applied industry solutions, integrating proprietary models with client datasets and systems).

object recognition done by computer vision in AI

SuperAnnotate for image annotation

While SuperAnnotate specializes in image and video annotation, they also work with computer vision and provide services such as SDK integration, text annotation, data curation, and quality management.

Founded in 2018 in San Mateo, California, the platform supports projects of all sizes across various industries, from autonomous vehicles and medical imaging to security and surveillance.

How to choose the best data vendor?

The listed alternatives may be a good start, but you want to have confidence in the provider before investing. To build a performant AI model, you need lots of quality data, and, you want your data partners to be experts in your use case area and be able to adapt to changing demands. So as you set out to research and look into various data vendors, here are some questions that you should be asking them:

  • Does the vendor have experience with similar use cases?
  • Do they have diverse enough annotators and do they match your end users?
  • How does the data vendor ensure data quality? What does their data validation/verification process look like? 
  • Can the vendor provide samples or metrics to verify data quality?
  • How much work will it be on your end before and after you get the data or models back?
  • How would they communicate with you throughout the process? Will there be a dedicated team or project manager?

And if you are ready to get to work, reach out to StageZero and tell us about your data needs.

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