What is Kili used for?

Kili is used to annotate and label image, video, text, PDF, audio, and geospatial data for training and evaluating AI models.

ML engineers, data scientists, and annotation teams that need high-quality labeled datasets with review workflows and quality tracking.

Does Kili offer a free trial?

Yes. Kili offers a free trial, while paid pricing is not published and requires contacting the vendor.

Does Kili have an API?

Yes. Kili provides a Python SDK and REST API to connect labeling projects with data pipelines and model training workflows.

What are the best alternatives to Kili?

Top alternatives to Kili include Labelbox, Label Studio, SuperAnnotate, and V7 for data annotation and AI training data.

Kili Reviews, Features & Alternatives

Overview

What is Kili?

Kili is a data annotation and labeling platform from Kili Technology for building datasets that train, fine-tune, and evaluate AI and machine learning models. It handles image, video, text, PDF, audio, and geospatial data in one interface, with quality control, reviewer workflows, inter-annotator agreement tracking, audit trails, and project dashboards. A Python SDK and REST API connect it to data pipelines. ML engineers, data scientists, and annotation teams use Kili across healthcare, finance, defense, and retail.

Best for

ML EngineersData ScientistsAnnotation TeamsAI Product TeamsComputer Vision Teams

Product snapshot

Category: Data Extraction & Collection
Best for: AI training data labeling
Deployment: Cloud
Free trial: Yes
Integrations: Python SDK, REST API
AI features: Yes

Key features

Key features of Kili

Multimodal annotation

Label image, video, text, PDF, audio, and geospatial data together.

Quality control

Track inter-annotator agreement and review labels to catch errors.

Reviewer workflows

Route labeled assets through review steps before they are accepted.

Project dashboards

Monitor labeling progress, throughput, and quality across projects.

Python SDK and API

Connect labeling projects to data pipelines and model training code.

Managed workforce

Access domain-expert annotators when internal labeling capacity is limited.

Reviews

Kili reviews

1 review

Overall

5.0/5

Ease of use5/5
Value for money5/5
Customer support5/5

What users like

Supports many data types
Built-in quality control tools
Developer friendly Python SDK
Clear audit trails for labels

What users dislike

Pricing is not publicly listed
Setup suits technical teams
Learning curve for complex projects
Managed workforce costs extra

Barasa G.

Security Personnel

“Liked the valuable insight the platform provides for learning and considered it the best tool of its kind available. Noted no major drawbacks in the product.”

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Alternatives

Top Kili alternatives

Labelbox
Best for
Enterprise training data
Why choose it
Labeling, curation, and model evaluation tools
Label Studio
Best for
Open source annotation
Why choose it
Flexible self-hosted labeling for many formats
SuperAnnotate
Best for
Computer vision and LLM data
Why choose it
Annotation platform with managed workforce options
V7
Best for
Image and video labeling
Why choose it
Automated annotation for visual data

Integrations

Kili integrations

Kili connects to machine learning workflows mainly through its Python SDK and REST API, which let teams create projects, upload assets, import pre-annotations from their own models, and export labeled data programmatically. This makes it practical to plug Kili into existing training pipelines, MLOps tooling, and evaluation loops instead of moving files by hand. Teams typically store raw assets in cloud storage such as Amazon S3, Google Cloud Storage, or Azure Blob Storage and connect those buckets to labeling projects. Exports can be pulled in common annotation formats for use in frameworks like PyTorch and TensorFlow, and audit trails keep a record of every labeling change.

FAQ

Kili