Kili
Data labeling software
Data science and machine learning platforms
Image recognition software
Machine learning software
MLOps platforms
Deep learning software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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What is Kili
Kili is a data labeling platform used to create and manage annotated datasets for machine learning, with a focus on computer vision and document-centric workflows. It supports teams that need to coordinate labeling projects, quality control, and reviewer feedback across internal labelers or external workforces. The product emphasizes configurable labeling interfaces, workflow management, and integration into ML data pipelines.
Configurable labeling workflows
Kili provides project setup features to define labeling instructions, roles, and review steps for consistent annotation. Teams can implement multi-stage workflows (e.g., label, review, rework) to improve dataset quality. This aligns with common requirements for production-grade training data programs where governance and traceability matter.
Computer vision and document support
Kili is used for image-based annotation and can also support document-oriented labeling use cases where structured outputs are needed. This makes it suitable for teams working on vision models as well as OCR/NLP-adjacent extraction tasks. The platform’s value is strongest when organizations need a single environment to manage multiple data modalities and labeling schemas.
Collaboration and quality controls
Kili includes collaboration features for managing labelers, tracking progress, and handling reviewer feedback loops. Quality-oriented capabilities (such as review processes and issue resolution) help teams reduce label noise and enforce guidelines. These controls are important when scaling beyond ad hoc labeling to repeatable dataset production.
Not a full ML platform
Kili primarily addresses dataset creation and labeling operations rather than end-to-end model development and deployment. Organizations still typically need separate tooling for experiment tracking, feature management, model registry, and production monitoring. Buyers evaluating it as an MLOps platform should validate integration points and division of responsibilities.
Workforce sourcing may vary
Compared with providers that bundle large managed labeling workforces, Kili’s approach may require customers to bring their own labelers or arrange third-party services depending on the engagement model. This can add procurement and operational overhead for teams without an existing labeling workforce. Fit depends on whether the buyer wants tooling-only, managed services, or a hybrid.
Advanced automation depends on setup
Capabilities such as model-assisted labeling, active learning-style iteration, or complex QA sampling strategies can require additional configuration and ML engineering effort. Teams may need to integrate their own models and pipeline tooling to realize these efficiencies. As a result, time-to-value can vary based on internal maturity and integration resources.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Free Trial | $0 / month (time-limited evaluation; no credit card required) | 2 team seats; 200 asset limit; Image, video, and PDF annotation; Basic annotation features; Kili advertises a free 2-week evaluation. |
| Grow | Custom Subscription (contact sales) | Up to 20 team seats; Up to 50,000 assets; API & Python SDK; Support level adapted to your needs; Accessible professional services; Add-ons available (storage, additional assets/seats, dedicated labeling services). |
| Enterprise | Custom Contract (contact sales) | All Grow features plus custom seat allocation, custom terms, SSO integration, advanced security features; on-premise deployment and compliance support available. |
Seller details
Kili Technology
Paris, France
2018
Private
https://kili-technology.com/
https://x.com/kilitechnology
https://www.linkedin.com/company/kili-technology/
