Appen has been a leading provider of AI data since the 1990s. Their global workforce utilizes real people to collect and curate high quality datasets. They offer a variety of services that AI teams can take advantage of.
Here are some of the services Appen is known for.
- Data collection: Collect custom datasets using remotely or on site. Teams can even collect mobile device and location data.
- Data annotation: Use human or AI-powered data annotation services to enrich datasets and highlight patterns.
- Audio data services: Collect, annotate, transcribe and translate all sorts of audio data for creating multimodal and multilingual models.
- Large Language Model (LLM) fine tuning: Fine tune your AI models using their workforce and infrastructure.
- AI model evaluation and benchmarking: Evaluate and benchmark models using real human evaluation to identify coverage issues and needs for further fine tuning and training.
- Multilingual AI data: Appen’s workforce is truly global. This allows them to curate data using languages all over the world and to bypass English in many cases.
- Off-the-shelf datasets: Some teams simply need to get their datasets. Appen offers ready-to-use datasets available for purchase by teams all over the world.
How we compare Appen alternatives
Historically, Appen has provided end to end AI services through a single vendor. Given Appen’s breadth, we’ll compare providers based on how they overlap with Appen’s services as a whole. The following are the key services provided by Appen. They are direct things we look for when searching for Appen alternatives.
- Data collection
- Data annotation
- Multimodal data
- Model fine tuning
- Model evaluation
- Multilingual data
- Datasets
The more a provider overlaps with Appen’s use cases, the more aligned they are as a direct alternative to Appen.
Top Appen alternatives
Defined AI
Defined AI offers a large expanse of AI data services.
- Data collection: Like Appen, Defined AI offers crowdsourced data from over 1.6 million members and supporting over 500 languages.
- Data annotation: Annotate image, audio, video and multimodal data using a diverse network of contributors.
- Data and model evaluation: Use human-in-the-loop evaluation to identify coverage gaps and improve model performance.
- Machine translation: Translate text, audio, image and video data using a scalable human-powered system. Teams can take training data from one language and convert it to another with ease.
- Conversational AI: Power your next chatbot using rich conversational datasets with diverse, real-world speech and multilingual expertise across data modalities. They are also developing a project called Accelerat which aims to support future multilingual efforts with European Portuguese, Spanish, French, German, Italian and English.
Scale AI
Scale offers three main products: Scale Data Engine, Scale Donovan and Scale Evaluation. Their Data Engine allows teams to collect and curate all sorts of data. Scale Donovan provides teams with the required frameworks and infrastructure for custom AI agents. Scale Evaluation lets teams train and produce model iterations quickly for rapid model development.
- Scale Data Engine: Collect and curate data. Annotate text, image, video and 3D sensor data. Teams can use Reinforcement Learning with Human Feedback (RLHF) for training and fine tuning. This product can also be used to generate high quality synthetic data.
- Scale Donovan: Deploy AI agents using Scale’s Agent Factory. Teams can test and evaluate agent performance to identify weak points and make improvements in agent performance.
- Scale Evaluation: Use expert red teaming to identify issues in AI models to improve performance and output. Teams can define custom testing and parameters to sharpen their model output where needed.
Bright Data
Bright Data offers a variety of services that overlap with Appen. Instead of Appen’s global workforce, Bright Data uses a global network of web data infrastructure to provide the following services.
- Unblocking and remote browsing: Teams can use the Unlocker API, Browser API, proxies and a variety of other automated data solutions.
- Scraper API and custom scrapers: Define custom scrapers or use their JavaScript Integrated Development Environment (IDE) to build them yourself easily with AI assistance. Get fresh, curated data whenever you need it.
- Managed data acquisition: Tell Bright Data what you’re looking for and get ready-to-use data with custom dashboards, expert consulting and recommendations.
- Datasets: Purchase ready-made datasets from all over the web using their dataset marketplace. Teams can choose from popular sites such as LinkedIn, Amazon, Instagram and much more.
- Web archive data: Use Bright Data’s cached website collections for HTML data from sites all over the internet. They add over a petabyte each week.
- Video and media data: Extract video and media data using a central API for seamless integration into your system.
- Retail insights: Get expert advice for eCommerce and product intelligence.
- Data annotation: Use automated, hybrid or human-powered data annotation to enrich your datasets.
Hugging Face
Hugging Face is one of the leading platforms for open source AI development. They offer a vast expanse of AI models and AI datasets. Hugging Face also offers a Spaces platform for teams looking to deploy models and applications. It’s an indispensable resource for teams who need battle-tested datasets for free.
- Models: Teams can download all sorts of models across modalities such as text, audio, computer vision and even multimodal models. Some models are even available to test straight from Hugging Face’s website.
- Datasets: Teams can choose from 3D, audio, document, geospatial, image, tabular, text, time-series datasets and more. Dataset quality can vary. However, many of these datasets are used to train production grade models for free.
- Spaces: Not to be confused with social media spaces. Hugging Face Spaces give teams a place to deploy models and AI applications within the cloud.
LAION
Like Hugging Face, LAION is another place to find models, datasets and tooling for free. However, if Hugging Face is a vast expanse, LAION is small and highly curated. They provide datasets across various modalities such as text, image, 3D and audio data. They also provide a variety of multimodal models that teams can use for evaluation and benchmarking. The goal of LAION is to make AI models standardized and reproducible.
- Datasets: Train models using highly curated datasets with varying modalities. LAION supports text, 3D, image and audio datasets.
- Models: LAION’s models offer a solid choice for teams looking to benchmark or springboard their own development. All models are open and free to use. They offer a variety of image, audio, video, text and multimodal models. They even offer their own open implementation of DALL-E 2.
- Tools: LAION provides tools for a variety of AI needs. img2dataset allows teams to convert whole sets of images into AI-ready datasets. They offer a CLIP Retrieval tool that allows teams to process images and text embeddings at scale.
Anyverse
Anyverse primarily offers synthetic data for AI-powered systems that operate in the physical world. Their datasets are built for critical tasks in the real world for self driving and defense systems. These datasets allow teams to simulate rare and potentially dangerous scenarios — this helps models keep people safe. In these scenarios, teams get full, granular control over conditions that aren’t easily replicated in real-world data.
- Anyverse InCabin: Synthetic data for models that monitor the inside of the car. This is ideal for manufacturers who need to monitor driver states and ensure vehicle safety.
- Anyverse ADAS & AD: Data for autonomous driving. Teams can use synthetic data to test and evaluate models in just a fraction of the time it takes to collect these types of data within the real world.
- Anyverse Defence: Generate synthetic data to improve defense systems ensuring accuracy, reliability and compliance with safety standards.
Mostly AI
Mostly AI’s strengths come from synthetic data. They’re not an alternative for annotation or benchmarking. Mostly AI is an alternative data source. They provide different grades of synthetic data to meet various development needs while minimizing some of the pain points that come with real world data such as readiness, collection time and privacy concerns.
- Synthetic data SDK: Build high-fidelity synthetic data from your Python environment using a single central SDK. Teams can train their own data generators, create synthetic samples and connect to external data sources when generating their synthetic data.
- Mock data: Create realistic schema-compliant datasets with generalized synthetic data. This is a solid product for protecting data sources and handling privacy concerns when training models.
- Simulated data: Generate highly realistic synthetic data to simulate real world scenarios. Models are trained on extensive real world historical data for performant forecasting and prediction.
Key breakdown of Appen alternatives
| Provider | Primary role | Data source type | Core strengths | Best fit use cases |
|---|---|---|---|---|
| Appen | Managed human data services | Global human workforce | Large-scale data collection, annotation, multilingual coverage, human evaluation | Custom labeled datasets, multilingual AI, RLHF |
| Defined AI | Crowdsourced AI data services | Human contributors | Multilingual data, annotation, conversational AI datasets | Speech, translation, global AI training data |
| Scale AI | Model training and evaluation | Human-labeled + synthetic data | RLHF, red teaming, evaluation, agent workflows | Model evaluation, iteration, safety testing, enterprise AI |
| Bright Data | Web data acquisition infrastructure | Public web data | Scalable data collection, unblocking, datasets, automation | Web-scale data sourcing, continuous data pipelines, market intelligence |
| Hugging Face | Open AI ecosystem | Open-source datasets and models | Broad model and dataset access, community-driven development | Rapid prototyping, research, open-model training |
| LAION | Curated open datasets and tools | Open, standardized datasets | Reproducibility, benchmarking, research-grade datasets | Model benchmarking, academic and open research |
| Anyverse | Physical-world synthetic data | Simulated environments | Safety-critical simulation, rare scenario modeling | Autonomous driving, in-cabin monitoring, defense systems |
| Mostly AI | Privacy-first synthetic data | Generated tabular and structured data | Compliance, data sharing, fast dataset readiness | Regulated industries, analytics, model training with sensitive data |
Conclusion
If you’re looking for a single provider to replace all of Appen’s services simultaneously, it’s going to be difficult to find. Appen provides a large variety of AI data services designed to meet most general needs. However, other providers are more than capable of meeting your needs — albeit through a different strategy.
Defined AI and Scale can help meet your needs for human contribution, translation and model evaluation. Bright Data provides a solid central solution for all types of curated real world web data. LAION and Hugging Face are solid places for teams looking to find free models, data and tooling. Anyverse and Mostly AI are great hubs for highly specific synthetic data.
In 2026, we don’t need to be thinking about general ‘data providers’. We need to be thinking about data strategies.
Does anonymous synthetic data fit your privacy needs?
Do you need your data from a real world workforce?
Does it need to be collected on site?
Can curated web data meet your needs?
Do you need data for autonomous vehicles?