Dataloop vs Databricks

Databricks has a disclosed valuation of $134B, while Dataloop's valuation is not publicly available, making a direct size comparison difficult. Databricks employs 6,000 people.

Databricks has raised more in total funding at $20.2B, compared to Dataloop's $49M — a gap of $20.2B. Combined, the two companies have completed 8 known funding rounds.

Databricks leads with an Awaira Score of 93/100, while Dataloop sits at 55/100. That 38-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Dataloop was founded by Eran Shlomo in 2017. Databricks was founded by Ali Ghodsi in 2013. 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. Databricks: Databricks is an AI and data platform founded in 2013 that provides a unified analytics workspace for data engineering, data science, and machine learning. The company developed Databricks Lakehouse, which combines data lake and data warehouse capabilities, built on Apache Spark technology. Its platform enables organizations to process large-scale data, build machine learning models, and deploy AI applications through a single interface. The company offers several core products: Databricks SQL for analytics, Databricks Machine Learning for model development, and Databricks Jobs for workflow automation. The platform supports multi-cloud deployment across AWS, Azure, and Google Cloud. Databricks serves enterprises across various industries, with customers including organizations in financial services, technology, and healthcare sectors. As of its latest funding round, Databricks has raised $11.2 billion in total funding and maintains a valuation of $134 billion, positioning it among the highest-valued private AI and data companies. The company achieved Series J funding status, indicating significant capital accumulation and investor confidence. Databricks competes with platforms like Snowflake, Teradata, and cloud-native data solutions from major hyperscalers. The company's growth trajectory reflects strong market demand for integrated data and AI infrastructure, driven by increasing enterprise adoption of machine learning and data-driven decision-making. Databricks unified the traditionally separate data warehouse and data lake approaches through its Lakehouse architecture, creating a single platform for analytics and AI workflows.

Databricks got there first, launching in 2013 — that's 4 years of extra runway. Dataloop didn't arrive until 2017. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Dataloop has about 100-500 employees; Databricks has about 6,000. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both Dataloop and Databricks compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.