Iguazio vs Dataiku

Dataiku has a disclosed valuation of $3.7B, while Iguazio'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 Iguazio's $72M — a gap of $928M. Combined, the two companies have completed 8 known funding rounds.

Dataiku leads with an Awaira Score of 90/100, while Iguazio sits at 53/100. That 37-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Iguazio was founded by Yaron Haviv in 2014. Dataiku was founded by Florian Douetteau in 2013. Visit each company's profile on Awaira for a full founder biography.

Iguazio: Iguazio built an MLOps and real-time AI platform that provided a unified data and model serving infrastructure for machine learning applications requiring low-latency prediction serving and real-time feature computation, enabling data science teams to deploy models into production with integrated feature stores, model serving, and monitoring pipelines. The Tel Aviv company open-source Nuclio serverless framework became widely adopted for event-driven AI inference workloads.\n\nThe company raised approximately $72 million in venture funding before being acquired by McKinsey in 2023, with the acquisition integrating Iguazio MLOps platform into McKinsey QuantumBlack AI consulting and technology practice. Prior to the acquisition, Iguazio had clients including Deutsche Telekom, Moody Analytics, and financial services firms using its platform for real-time fraud detection, recommendation systems, and predictive maintenance applications.\n\nIguazio competed in the MLOps platform market against Databricks, MLflow, Kubeflow, and Weights and Biases, as well as managed MLOps offerings from AWS SageMaker, Google Vertex AI, and Azure ML. The acquisition by McKinsey represented a strategic move to acquire proprietary AI infrastructure that differentiates McKinsey technology consulting from pure advisory competitors. The Iguazio platform provides McKinsey QuantumBlack with an accelerated deployment capability for AI use cases it implements for clients, reducing time-to-production for ML models in regulated enterprise environments. 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 1 year of extra runway. Iguazio didn't arrive until 2014. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Iguazio has about 100-500 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 Iguazio and Dataiku compete in ML Platform, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.