Loading...
Loading...
Facts checked against both vendors on September 28, 2026.
KNIME is the pick if you want a free, open source desktop tool with a transparent path to paid automation starting at $19 a month. Dataiku is the pick if you are buying a governed enterprise platform for a whole data organization and are prepared to negotiate a contract, because Dataiku does not publish prices.
Analyze Data, Upskill, Scale, No Coding Required. [Contact for Pricing]
KNIME and Dataiku both let analysts build data pipelines and machine learning models visually, with code where you want it. They are aimed at very different buyers. KNIME Analytics Platform is a free, open source desktop application, and KNIME charges only when you want to run, schedule and share workflows on its hosted Hub or a self-managed Business Hub. Dataiku is a commercial enterprise platform sold through sales, with a time-limited cloud trial and a downloadable Free Edition for local use. This comparison covers what each does, what each costs as of September 2026, and where each is the stronger choice.
KNIME Analytics Platform is a free, open source workbench built around node-based visual workflows. KNIME's site describes 300+ connectors to data sources, the ability to add Python, R and JavaScript scripting inside workflows, access to popular machine learning libraries including LLMs, and K-AI, a generative assistant that helps write scripts and build visualizations. Workflows are shared and deployed through KNIME Hub, which comes in a hosted Community and Business form, or through a self-managed Business Hub.
Dataiku is an enterprise platform for analytics, machine learning and AI agents. Its product pages describe visual and code-based recipes for joining, cleaning and transforming data with Python, R and SQL; AI assistants that generate preparation steps, SQL queries and Flow plans from natural language; visual ML and AutoML for building and comparing models; a model registry with approval workflows, lineage tracking and drift and bias monitoring; Visual Agents and Code Agents; and an LLM Mesh that centralizes routing, quotas, monitoring and safety controls across multiple LLM providers. It ships native connectors to Snowflake, Databricks, Redshift, S3 and other enterprise systems.
KNIME publishes its prices. The Analytics Platform is free and open source, and a free Personal account on KNIME Hub includes 20 K-AI interactions a month. The Pro plan starts at $19 a month, includes 120 credits of workflow runtime, 500 K-AI interactions a month and unlimited versioning, and charges $0.025 per vCore minute beyond the included credits. The Team plan starts at $99 a month for three team members, with additional members at $49 a month, and adds private spaces and team-level billing. Business Hub, the enterprise product with LDAP, OAuth and OIDC authentication, SCIM, staged deployment and custom extensions, is priced on request.
Dataiku does not publish prices. Its plans page offers only a trial and a demo request, and its community answers on cost point buyers to a sales representative. What you can get without talking to sales is a 14-day cloud trial with no credit card, limited to two people, 4 CPUs and 32 GiB of elastic compute and one API service, and with Govern and advanced LLM Mesh excluded. There is also a Free Edition you install locally on Mac or Linux, with an experimental Windows build. Anything beyond that is a negotiated contract.
KNIME wins on cost transparency and on the zero-cost starting point. A single analyst can download the full desktop platform today, build production-grade workflows, and never pay anything. When they do need scheduling, sharing or data apps, the $19 Pro tier and the $99 Team tier are cheap enough to expense rather than procure.
It also wins on openness. The platform is open source and the node-based model makes it easy to see exactly what a pipeline does step by step. Teams with a scripting culture get Python, R and JavaScript nodes rather than a proprietary abstraction. And KNIME is the natural fit for on-premise or air-gapped work: the desktop tool runs locally without any account, and Business Hub is available for self-managed deployment.
Dataiku wins on breadth of the managed platform. It bundles data preparation, AutoML, model deployment to APIs and batch scoring, monitoring, governance, agent building and an LLM Mesh into one product with a unified Flow. For a large organization that wants one governed environment where analysts, data scientists and ML engineers work in the same project, that integration is the point.
It also wins on governance and MLOps depth. The model registry, approval workflows, lineage and continuous drift and bias monitoring are built to satisfy risk and compliance teams, and the LLM Mesh gives central control over which LLM providers are used, with quotas and safety controls. KNIME can do much of this with a Business Hub setup, but Dataiku presents it as a single, opinionated system, and collaboration is stronger out of the box because everything lives in a shared project.
Pick KNIME if you are an individual analyst, a small team, a university group, or a department that must justify every dollar, and if you want to run locally, keep open source as an option forever, and grow into paid automation only when the value is obvious.
Pick Dataiku if you are buying for a data organization rather than a team, if governance, model monitoring and centralized LLM control are procurement requirements, and if you have the budget and patience for an enterprise sales cycle. If you need to know the total cost before a pilot, that alone is a reason to start with KNIME.
Built from each tool's listing. Vendors can update their own listing after claiming it.
KNIME Analytics Platform is free and open source. KNIME Hub plans are Pro from $19 a month (120 runtime credits, then $0.025 per vCore minute), Team from $99 a month for three members with extra members at $49 a month, and Business Hub priced on request.
Dataiku does not publish prices; plans are quoted by sales. A 14-day cloud trial for two people with 4 CPUs and 32 GiB of compute is free, and a Free Edition can be installed locally on Mac and Linux, with an experimental Windows build.