Azmaval vs Argilla
Argilla is an open-source tool for building and reviewing datasets, strong for NLP and language model feedback. Azmaval adds a vetted reviewer network, quality gating and image, audio and video tasks.
The short version
Argilla is open source, integrates with Hugging Face and focuses on text and language model data, such as rating and ranking model answers. You host it, or run it on a Hugging Face Space, and the annotators are your own team.
Azmaval covers the same kind of text work and goes further on media: boxes, polygons, keypoints and time segments on images, audio and video. It also supplies the people and the quality system, which a self-hosted tool does not.
Side by side
Competitor details are from their public documentation, checked on 22 October 2026. Products change, so check their pages before you decide.
| Argilla | Azmaval | |
|---|---|---|
| Setup | Self-host, or run on a Hugging Face Space | Hosted |
| Focus | NLP and language model feedback | Text, image, audio and video tasks |
| Hugging Face integration | Native | Export to JSONL, CSV and Parquet, which load into datasets |
| Image, audio and video marking | Text-first; check its documentation for media | Boxes, polygons, keypoints, time segments |
| Reviewers | Your own team | Invitation-only reviewers with probation and tiers, or private mode |
| Quality system | Metrics for your own team of annotators | Gold checks, consensus, agreement, reviewer accuracy |
| Paying reviewers | Not part of the product | Built-in payouts (currently in sandbox) |
| Right-to-left languages | Depends on your setup | Reviewer screens in Pashto, Persian, Urdu and Arabic |
Where Argilla is stronger
- Deep integration with the Hugging Face ecosystem.
- Open source, so you can inspect and change it.
- Built around the loop of model output, human feedback and retraining for language models.
Where Azmaval is stronger
- Reviewers and quality control are part of the service.
- A screen built from your file, with no code or config.
- Media tasks beyond text.
- Focus on Pashto, Dari, Urdu and Arabic reviewers.
How to choose
- Choose Argilla when your team is the annotator pool, your work is text and language model feedback, and you want to self-host.
- Choose Azmaval when you need outside reviewers, quality checks or media annotation.
Sources
Frequently asked questions
Can I use both?
Yes. Export an Azmaval campaign as JSONL or Parquet and load it into a Hugging Face dataset, or the reverse.
Does Azmaval support preference data for model tuning?
Yes. The pairwise preference question collects which of two replies is better, with an optional comment for the reason.