Kili Technology vs Label Studio — Comparison

Kili Technology

Build high-quality and trustworthy datasets to train, fine-tune, and evaluate your AI models. Complete with a robust annotation tool built for collabo

Kili Technology began as an idea in 2018. Edouard d’Archimbaud, our co-founder and CTO, was working at BNP Paribas, where he built one of the most advanced AI Labs in Europe from scratch. François-Xavier Leduc, our co-founder and CEO, knew how to take a powerful insight and build a company around it. While all the AI hype was on the models, they focused on helping people understand what was truly important: the data. Together, they founded Kili Technology to ensure data was no longer a barrier to good AI. By July 2020, the Kili Technology platform was live and by the end of the year, the first customers had renewed their contract, and the pipeline was full. In 2021, Kili Technology raised over $30M from Serena, Headline and Balderton. Today Kili Technology continues its journey to enable businesses around the world to build trustworthy AI with high-quality data. Without them, are we even human? We are one team; we help each other with candor, and we stand together to achieve common goals. We own and master our topics, we learn fast to improve individually and collectively. We are curious, we have a growth mindset, we learn to be one step ahead. Leadership begins with ownership. Ownership breeds accountability. We take ownership to make a difference. We are critical-thinkers, we take our decisions based on facts, metrics, data analysis, instead of intuition. We achieve a lot, with little. We love keeping things simple and focusing on what matters. We aim to have 10X impact for our customers and users. We obsessively seek for what would change our customers our users life, and work backwards to create the right solution. Kili Technology is the complete platform to build trustworthy, high-quality datasets for training, fine-tuning, and evaluating AI/ML models.

Label Studio

A flexible data labeling tool for all data types. Prepare training data for computer vision, natural language processing, speech, voice, and video mod

The most flexible data labeling platform to fine-tune LLMs, prepare training data, or evaluate AI systems. Label data for supervised fine-tuning or refine models using RLHF Response moderation, grading, and side-by-side comparison Use Ragas scores and human feedback Detect objects on image, boxes, polygons, circular, and keypoints supported Partition image into multiple segments. Use ML models to pre-label and optimize the process Partition an input audio stream into homogeneous segments according to the speaker identity Tag and identify emotion from the audio Write down verbal communication in text Classify document into one or multiple categories. Use taxonomies of up to 10000 classes Extract and put relevant bits of information into pre-defined categories Answer questions based on context Determine whether a document is positive, negative or neutral Put time series into categories Identify regions relevant to the activity type you're building your ML algorithm for Label single events on plots of time series data Call center recording can be simultaneously transcribed and processed as text Put an image and text right next to each other Use video or audio streams to easier segment time series data Label and track multiple objects frame-by-frame Add keyframes and automatically interpolate bounding boxes between keyframes Configurable layouts and templates adapt to your dataset and workflow. Webhooks, Python SDK and API allow you to authenticate, create projects, import tasks, manage model predictions, and more. Save time by using predictions to assist your labeling process with ML backend integration. Connect to cloud object storage and label data there directly with S3 and GCP. Prepare and manage your dataset in our Data Manager using advanced filters. Support multiple projects, use cases and data types in one platform. Vector annotation, an interactive task source viewer, and workflow improvements across the Data Manager and Template Builder. A practical workflow for onboarding and evaluating annotators with clear instructions, calibration, quality gates, reviewer feedback, and dashboards that keep labeling consistent as volume grows. How do you build the right labeling interface in Label Studio? This video walks through a practical progression: start with templates, customize with XML tags, extend with React Code, and standardize workflows with plugins.

Kili Technology

Label Studio

Kili Technology

Label Studio

Kili Technology

Label Studio