Dataiku is an enterprise AI command center — it lets business analysts and data teams build predictive models, automate pipelines, and now orchestrate AI agents, all without writing production code. Its security credentials are genuinely impressive, and its integrations with Snowflake, Databricks, and major cloud providers are deep and documented. Tableau, backed by Salesforce, is the gold standard for data visualization with a rapidly maturing AI layer — Tableau Pulse and Tableau Agent are real, shipping features, not roadmap promises. The meaningful difference is purpose: Dataiku is built to create AI outputs (models, predictions, automated decisions), while Tableau is built to communicate data (dashboards, trends, stories). Dataiku's learning curve is lower than you'd expect for ML tooling, but it still assumes your team wants to build something. Tableau assumes your team wants to see something — and that distinction matters enormously at budget and adoption time. Choose Dataiku if your business goal is building predictive capability at scale with a small technical team. Choose Tableau if your priority is company-wide data literacy, executive reporting, or you're already invested in Salesforce infrastructure. Most mature data organizations eventually need both — but if forced to pick one first, Tableau reaches more people faster.