OctoAI vs Dataiku

Dataiku has a disclosed valuation of $3.7B, while OctoAI'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 OctoAI's $132M — a gap of $868M. Combined, the two companies have completed 4 known funding rounds.

Dataiku leads with an Awaira Score of 90/100, while OctoAI sits at 65/100. That 25-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

OctoAI was founded by Luis Ceze in 2019. Dataiku was founded by Florian Douetteau in 2013. Visit each company's profile on Awaira for a full founder biography.

OctoAI: OctoAI delivers a machine learning acceleration and model serving platform that enables teams to deploy, scale, and optimize AI models in production with significantly lower inference latency and cost compared to standard cloud ML services. The platform includes automated model optimization, hardware-aware compilation, and a managed serving layer that handles traffic routing and autoscaling.\n\nThe company raised approximately 132 million USD and counts enterprise customers in sectors including e-commerce, media, and financial services that operate high-throughput model serving pipelines at scale. OctoAI emerged from foundational research at the University of Washington in ML systems optimization, giving the technical team a deep background in compiler-level model performance.\n\nAs inference costs become the dominant expense in production AI deployments, infrastructure that reduces cost-per-query by meaningful percentages translates directly to enterprise budget savings. OctoAI competes with Anyscale, BentoML, and cloud-native MLOps offerings, but its focus on hardware-level optimization and the depth of its model acceleration capability distinguishes it from platforms that primarily address deployment workflow rather than raw performance. 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. OctoAI didn't arrive until 2019. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

OctoAI 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 OctoAI and Dataiku compete in ML Platform, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.