- Data annotation tools side by side
- Eleven data annotation tools worth a look
- Which tool to try first
- Label quality is now a legal question too
- Self-hosted, cloud or managed service?
- Questions to ask before you choose
- Signs you need a dedicated labelling tool
- My plain advice on choosing a data annotation tool
- Frequently asked questions
The best data annotation tools let your team label images, video, text or audio quickly and check the labels are right before they train or test a model. Some are free and open source, some add AI-assisted labelling and managed workforces, and a few focus on evaluating AI output. Eleven options follow in no particular order.
A model is only as good as the examples it learns from. If your labels are rushed, inconsistent or wrong, no amount of clever training will fix the results.
The eleven tools below are listed alphabetically and in no particular order, and every description comes from the vendor's own website, read this week. If the jargon is new, my explainer on transformer models is a gentle starting point.
Data annotation tools side by side
I checked the free plan column on each tool's own website, as of Oct 2026. Yes means the site names a free plan, a free version, a free tier or an open source edition. Trial only means a time-limited trial. Not stated means the pages I read named neither, even where there's a button inviting you to try it, so check the vendor's page before you sign up.
| Tool | Type | Good for | Free plan |
|---|---|---|---|
| CVAT | Data annotation platform | Computer vision teams wanting self-hosting | Yes |
| Encord | AI data platform | Robotics and autonomous systems data | Not stated |
| Kili Technology | Data annotation and AI evaluation | Evaluating and post-training models | Trial only |
| Label Studio | Open source data labelling | Flexible labelling across data types | Yes |
| Labelbox | AI training data platform | Large frontier-model data projects | Not stated |
| Labellerr | Image annotation software | Speeding up image labelling | Not stated |
| Prodigy | Scriptable annotation tool | NLP developers labelling their own data | Not stated |
| Roboflow Annotate | Image annotation tool | Getting a vision model started fast | Yes |
| SuperAnnotate | Data annotation platform | Managed annotation workforces | Not stated |
| Supervisely | Computer vision platform | Curating, labelling and training in one | Not stated |
| V7 Darwin | AI data labelling platform | Medical and scientific imaging | Not stated |
Eleven data annotation tools worth a look
CVAT
Best for: Computer vision teams who want an open-source, self-hosted community edition for image and video labelling, with a managed cloud option too.
Encord
Best for: Builders of AI for navigation, inspection and monitoring who want one platform to manage, label and curate training data.
Kili Technology
Best for: Model builders who want data annotation alongside AI evaluation and post-training work, with a free trial to test it.
Label Studio
Best for: Developers who want an open source tool for labelling data and evaluating AI output, which they can run and adapt themselves.
Labelbox
Best for: Larger AI teams who want help producing training data for models that do real work, including expert human labelling.
Labellerr
Best for: Growing teams who want data labelling and image annotation software with automation to speed up repetitive work.
Prodigy
Best for: Python developers and NLP researchers who want a scriptable annotation tool they run themselves, from the makers of spaCy.
Roboflow Annotate
Best for: Engineers and technical founders who want to label images faster and can start on a free tier with open documentation.
SuperAnnotate
Best for: Companies that want an annotation platform plus access to a managed workforce for building training and evaluation datasets.
Supervisely
Best for: Vision teams who want to curate, label and build production models in one platform rather than juggling separate tools.
V7 Darwin
Best for: Research groups with complex image and video data who want an AI-assisted labelling platform and a proof of concept before buying.
Which tool to try first
- Free and open source: Label Studio or CVAT.
- Getting a vision model started fast: Roboflow Annotate.
- Curating, labelling and training in one place: Supervisely or Encord.
- NLP developers who write Python: Prodigy.
- AI-assisted labelling for complex images: V7 Darwin or Labellerr.
- A managed labelling workforce: SuperAnnotate or Labelbox.
- Evaluating model output: Kili Technology.
Label quality is now a legal question too
The European Union's AI Act, published on EUR-Lex as Regulation (EU) 2024/1689, says data sets for training, validation and testing, including the labels, should be relevant, sufficiently representative, and to the best extent possible free of errors and complete in view of the intended purpose.
Want AI doing the heavy lifting in your marketing?
I build the systems that handle the boring 80 percent, so you get your week back. Done properly, with the human kept in.
That's a useful test even if your system isn't high-risk. Pick a tool with review steps, agreement checks and an audit trail, because speed means little if nobody can show the labels were checked.
Self-hosted, cloud or managed service?
Self-hosting an open source tool keeps sensitive data on your own servers and needs no licence fees, but someone has to run it. It suits technical teams with privacy concerns.
Cloud platforms and managed services take the setup off your plate, and some supply trained labellers too. They're quicker to start, so ask where your data's stored before you upload anything private.
To trace where training data came from, this guide to data lineage tools sets out options built to record it.
Questions to ask before you choose
- Which data types do I need to label: images, video, text or audio?
- Can I run it on my own servers?
- Does it support review and agreement checks?
- Can AI pre-label items to speed things up?
- Can I export labels in the format my training code expects?
- Who owns the labelled data afterwards?
After the labelling is done, our list of MLOps tools compares platforms that train, track and serve the models.
Signs you need a dedicated labelling tool
- You label in spreadsheets. Move to a dedicated tool.
- Labellers keep disagreeing. Add review steps.
- Your data is sensitive. Consider self-hosting.
- Labelling takes weeks. Try AI pre-labelling.
Do you make data annotation or labelling tools? Tool makers can ask to be considered for a featured place on this list.
My plain advice on choosing a data annotation tool
Run a small pilot before you commit. Label the same two hundred items in two tools, compare the time taken and how often reviewers disagree, and you'll learn more than any demo can show you.
Write clear labelling guidelines first, with examples of the hard cases, because the tool can't fix vague instructions. If you're working with Google's cloud, my guide to what Vertex AI is explains where training fits.
Frequently asked questions
What is a data annotation tool?
It's software for adding labels to data, such as drawing boxes on images or tagging text, so a machine learning model can learn from the examples.
Are there free data annotation tools?
Yes. This list includes open source tools you can host yourself and at least one platform with a free tier.
Why does label quality matter?
Models learn from labels, so errors spread into results. The EU AI Act says training data, including labels, should be relevant, representative and as free of errors as possible.
Should I host the tool myself?
Self-hosting keeps data on your own servers, which suits sensitive data, but it needs someone technical to run it.
Can AI help with labelling?
Yes. Several tools pre-label items with AI so people only review and correct them.
How do I check labellers agree?
Use a tool with review steps and agreement checks, and label a sample twice to spot unclear guidelines.
