KNIME is an open-source platform for data analytics, reporting, and integration. It enables users to visually create data workflows, perform advanced data analysis, and build machine learning models without extensive coding.
With a user-friendly interface, KNIME supports data blending, transformation, visualization, and predictive analytics, making it ideal for data scientists and analysts. Its modular design and extensive library of nodes streamline complex data processes across industries.
Visual Workflow Design: Users can create data workflows through a drag-and-drop interface. This intuitive approach simplifies complex data processes and requires minimal coding.
Data Integration and Blending: KNIME connects to various data sources, such as databases, cloud storage, and APIs. This capability ensures seamless data aggregation and preparation.
Data Transformation and Cleaning: The platform offers tools to preprocess and clean data. These tools handle missing values, normalize data, and transform datasets for analysis.
Advanced Analytics and Machine Learning: KNIME supports statistical analysis, predictive modeling, and machine learning algorithms. Users can build and deploy models without extensive programming.
Data Visualization: Built-in visualization tools create charts, graphs, and reports. These visuals help users interpret data insights effectively.
Automation and Scheduling: Workflows can be automated and scheduled for execution. This feature saves time and ensures consistent data processing.
Extensibility and Integrations: KNIME integrates with tools like Python, R, and Tableau, and it supports custom extensions. This flexibility enhances functionality for specific needs.
Collaboration and Sharing: Teams can share workflows, data apps, and results in private or public spaces. This fosters collaboration across organizations.
Scalability and Deployment: KNIME scales from local to enterprise-level deployments. It supports cloud, on-premises, or hybrid environments for large-scale data processing.
K-AI Assistant: An AI-powered tool provides workflow suggestions and automates repetitive tasks. This feature boosts productivity and simplifies complex analyses.
| Plan Name | Price | Key Features |
|---|---|---|
| Personal Plan | Free |
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| Team Plan | From $99/month | Includes Personal features, plus: – Run and automate workflows (starting at €0.10/min) – Deploy workflows as data apps – Private team collaboration (3 team members included; +$49/month per additional member) – Secure storage for secrets, optional disk space extension, limited email support, centralized billing |
| Business Hub – Basic | From $39,900/year | – Full enterprise deployment on private infrastructure – Team-level collaboration and workflow deployment – Enterprise-grade authentication (LDAP, OIDC) – Secret management – REST API support |
| Business Hub – Standard | From $71,250/year | Includes Basic features, plus: – Support for up to 3 teams – Unlimited REST API and Data App access for consumers – Advanced permissions and high availability – Additional staging/test environments (€7,500/yr) |
| Business Hub – Enterprise | Custom pricing | Includes Standard features, plus: – Unlimited teams and hub installations – Kubernetes support – GEN AI gateway, AI assistant management – Full-scale deployment tailored to enterprise needs |
Check pricing details: https://www.knime.com/knime-hub-pricing
| Competitor | Pros of KNIME |
|---|---|
| Alteryx | KNIME is free and open source, so anyone can start without cost. It works with many data types and scales well. It lets beginners and experts use visual tools plus code together and has a strong community that shares useful help and extensions. |
| Dataiku | KNIME is easier to get started with especially for individuals. It stays true to open-source roots and gives full access without heavy costs or vendor lock-in. |
| RapidMiner | KNIME offers more flexibility. It integrates with Java, Python, R, Weka, H2O and more. It handles really big datasets. |
| Apache NiFi | KNIME is stronger at analytics and modeling, not just moving data. It combines visual work with ML, code, and ETL in one space. |
| Microsoft Azure ML | KNIME works offline on your computer and doesn’t need cloud. It is open-source, and you c |
| Competitor | Cons of KNIME |
|---|---|
| Alteryx | KNIME’s interface can feel slow and less slick. KNIME needs more setup and effort at first. |
| Dataiku | KNIME has fewer built-in governance and MLOps features, and onboarding big teams can be slower. |
| RapidMiner | KNIME has a steeper learning curve for beginners and community support is smaller than RapidMiner’s. |
| Apache NiFi | KNIME is not as good as NiFi at realtime data flows, edge computing, clustering, or secure routing. |
| Microsoft Azure ML | KNIME lacks enterprise cloud service features like managed compute, automatic scale-up, and rich support. |
