Scribble Data vs Dataiku

Dataiku has a disclosed valuation of $3.7B, while Scribble Data's valuation is not publicly available, making a direct size comparison difficult. Dataiku employs 1000+ people.

Dataiku has raised more in total funding at $1B, compared to Scribble Data's $3.5M — a gap of $996.5M. Combined, the two companies have completed 4 known funding rounds.

Dataiku leads with an Awaira Score of 90/100, while Scribble Data sits at 35/100. That 55-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Scribble Data was founded by Arun Iyengar in 2019. Dataiku was founded by Florian Douetteau in 2013. Visit each company's profile on Awaira for a full founder biography.

Scribble Data: Scribble Data builds a feature store and ML data pipeline platform that enables data science teams to define, compute, store, and serve machine learning features in a unified platform, addressing the significant engineering overhead of building and maintaining feature infrastructure for production ML systems. The platform reduces the time data teams spend on data wrangling and feature engineering by providing reusable feature definitions and automated pipeline orchestration.\n\nThe company is in early Seed stage with limited disclosed funding and targets data science teams at financial services companies, digital consumer businesses, and enterprise analytics organizations that run large-scale ML operations. Scribble Data positions its platform as infrastructure that shortens the path from model ideation to production deployment.\n\nFeature stores have emerged as a recognized production ML infrastructure category, with vendors including Tecton, Feast, and Hopsworks competing for enterprise adoption. Scribble Data's India-based team and pricing model target a segment of the market that cannot justify the cost of US-headquartered enterprise ML platform vendors, while still requiring production-grade feature management capabilities. 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 6 years of extra runway. Scribble Data didn't arrive until 2019. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Scribble Data has about 10-50 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.

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