Scale AI vs Dataiku

By valuation, Scale AI is the larger company at $29B versus $3.7B — a 7.8x difference. Size can also be measured by team: Scale AI employs 1,000 people while Dataiku has 1000+ employees.

Scale AI has raised more in total funding at $15.9B, compared to Dataiku's $1B — a gap of $14.9B. Combined, the two companies have completed 8 known funding rounds.

Dataiku leads with an Awaira Score of 90/100, while Scale AI sits at 84/100. That 6-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Scale AI was founded by Alexandr Wang in 2016. Dataiku was founded by Florian Douetteau in 2013. Visit each company's profile on Awaira for a full founder biography.

Scale AI: Scale AI is a data infrastructure company founded in 2016 that specializes in data labeling and annotation services for artificial intelligence model development. The company provides managed workforce solutions and software platforms that enable organizations to prepare high-quality training data at scale. Scale AI's core offerings include data labeling, model evaluation, data generation, and managed workforce services across computer vision, natural language processing, and other AI domains. The company serves enterprise customers across automotive, defense, technology, and other sectors requiring large-scale data annotation for machine learning applications. Notable use cases include autonomous vehicle development, where precise labeled data is critical for training perception systems. Scale AI operates a hybrid model combining software automation with human-in-the-loop annotation capabilities. With a Series F valuation of $13.8 billion and total funding of $1.6 billion, Scale AI represents one of the highest-valued data infrastructure companies in the AI stack. The company competes in the data labeling market alongside firms like Labelbox, Snorkel AI, and various in-house solutions deployed by large technology companies. Its growth trajectory reflects increasing enterprise demand for quality training data as AI model development accelerates. Scale AI's positioning emphasizes enterprise-grade reliability, security, and quality assurance in data preparation workflows. Scale AI has achieved unicorn valuation by solving the critical bottleneck of high-quality labeled data production at enterprise scale. Dataiku: Dataiku builds a collaborative data science and machine learning platform that enables data teams, business analysts, and AI engineers to build, deploy, and monitor machine learning models within a single unified environment. The Paris-founded company platform covers the full ML lifecycle from data preparation and feature engineering through model training, deployment, and governance, supporting both code-based and visual, low-code workflows to serve teams of varying technical sophistication.\n\nThe company raised over $1 billion in total funding including a $400 million Series E led by Tiger Global and ICONIQ Growth, reaching a valuation of $3.7 billion. Dataiku counts over 600 enterprise clients globally including Unilever, GE, Sanofi, and the US Army Corps of Engineers, and reports thousands of active practitioners using the platform daily. The company has offices in New York, London, Paris, Singapore, and Sydney, reflecting a global enterprise sales motion that has made it one of the largest pure-play ML platform vendors in the world.\n\nDataiku competes in the data science platform market against Databricks, SAS, KNIME, and H2O.ai. Its differentiation is the breadth of user personas it serves within a single platform, allowing collaboration between data scientists, ML engineers, and business domain experts without requiring separate tools. The company is considered one of Europe most successful enterprise AI software exports, with the majority of its revenue generated outside France and a valuation that places it among the top independent AI platform companies in the world.

Dataiku got there first, launching in 2013 — that's 3 years of extra runway. Scale AI didn't arrive until 2016. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Scale AI has about 1,000 employees; Dataiku has about 1000+. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Not head-to-head. Scale AI is in AI Infrastructure while Dataiku plays in ML Platform — different enough that they usually serve different buyers. But AI verticals are converging fast, and their roadmaps could overlap more over time.