Dataloop vs Atlan

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Dataloop employs 100-500 people, while Atlan has 200-500 employees.

Atlan has raised more in total funding at $78M, compared to Dataloop's $49M — a gap of $29M.

Atlan holds the higher Awaira Score at 68/100, compared to Dataloop's 55/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 13-point gap that reflects meaningful differences in scale or traction.

Dataloop was founded by Eran Shlomo in 2017. Atlan was founded by Prukalpa Sankar in 2018. Visit each company's profile on Awaira for a full founder biography.

Dataloop: Dataloop builds an AI data management and annotation platform that covers the full data pipeline for computer vision and NLP model development, combining data labelling tools, annotation workflow management, quality assurance automation, and model training integration in a single platform. The Tel Aviv company targets AI teams at enterprises and AI product companies that need to manage large-scale data labelling operations with quality controls and annotator workforce management.\n\nThe company raised approximately $49 million including a Series B round from investors including Viola Ventures and Samsung Next. Dataloop counts enterprise AI teams in automotive, retail, and agriculture verticals among its clients, with the platform used for large-scale computer vision dataset creation including bounding box annotation, semantic segmentation, and polygon annotation for object detection model training. The platform supports human-in-the-loop annotation workflows where model predictions are reviewed and corrected by human annotators to improve efficiency.\n\nDataloop competes in the data annotation and management market alongside Scale AI, Labelbox, Encord, and V7, as well as annotation services providers. The market has evolved from pure annotation tooling toward platforms that combine labelling with dataset management, model evaluation, and active learning capabilities. Dataloop positioning as a full data management platform rather than a labelling tool aims to capture more of the AI development workflow and reduce customer dependency on multiple point solutions across the data pipeline. Atlan: Atlan is an active metadata platform and data workspace that enables data teams to discover, understand, and govern data assets across cloud warehouses, lakes, BI tools, and pipelines through an AI-powered catalog interface. The platform automatically crawls data infrastructure to build lineage graphs, enables natural language search over data assets, and enforces data governance policies through automated tagging and access controls.\n\nThe company raised approximately $78M in Series C funding from investors including Insight Partners and Sequoia India, and has built a global customer base of data-forward technology companies and enterprises. Atlan has emerged as a recognized leader in the data catalog market, competing with Alation, Collibra, and DataHub in the modern data stack ecosystem.\n\nAs cloud data infrastructure has proliferated, data discoverability and governance have become critical operational problems for engineering teams managing hundreds of data sources. Atlan's modern metadata-first approach and deep integrations with the contemporary data stack — Snowflake, dbt, Fivetran, Looker — position it well in a market where legacy catalog vendors have struggled to keep pace with infrastructure evolution.

Dataloop was founded first in 2017, giving it 1 year of additional market experience. Atlan was founded later in 2018. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Dataloop has approximately 100-500 employees, while Atlan has approximately 200-500. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Dataloop and Atlan are direct competitors — both operate in the AI Data space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.