Nota AI vs Dataiku

Dataiku has a disclosed valuation of $3.7B, while Nota AI'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 Nota AI's $10M — a gap of $990M. Combined, the two companies have completed 5 known funding rounds.

Dataiku leads with an Awaira Score of 90/100, while Nota AI sits at 38/100. That 52-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Nota AI was founded by Gwang-jin Cho in 2019. Dataiku was founded by Florian Douetteau in 2013. Visit each company's profile on Awaira for a full founder biography.

Nota AI: Nota AI develops neural network compression and optimisation technology for deploying AI models on edge devices and embedded systems with limited compute and power resources, providing tools that compress, quantise, and prune large neural network models into lightweight versions suitable for smartphones, surveillance cameras, automotive chips, and IoT devices. The Seoul company NetsPresso platform automates the model optimisation workflow, reducing the engineering effort required to adapt research models for production hardware deployment.\n\nThe company raised approximately $10 million in venture funding from Korean AI-focused investors. Nota AI targets computer vision and NLP model deployment scenarios where inference must run locally on resource-constrained hardware rather than in the cloud, a requirement driven by latency, privacy, and connectivity considerations. The company customers include semiconductor companies integrating AI features into SoC designs and device manufacturers seeking to add on-device AI capabilities without redesigning around more powerful processors.\n\nNota AI competes in the edge AI model optimisation market against Kneron, Hailo, and embedded AI optimisation tools from semiconductor companies including ARM, Qualcomm, and MediaTek. Its software-first approach, which optimises models for deployment across diverse existing hardware rather than requiring a specific custom chip, addresses the fragmented edge hardware ecosystem where AI model developers need to support many different target processors from different vendors. The Korean semiconductor industry concentration creates a proximate customer base among chipmakers and device manufacturers seeking edge AI 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. Nota AI didn't arrive until 2019. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Nota AI has about 1-50 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 Nota AI and Dataiku compete in ML Platform, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.