--- title: KNIME Reviews meta_title: 'KNIME Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 102 reviews by the users' company size, role or industry to find out how KNIME works for a business like yours. aggregate_rating: rating_value: 4.5 review_count: 102 scale: '5' date_modified: '2026-07-17' parent_category: name: Analytics Tools & Software url: https://www.g2.com/categories/analytics-tools-software --- # KNIME Reviews **Vendor:** KNIME **Category:** [Analytics Platforms](https://www.g2.com/categories/analytics-platforms) **Average Rating:** 4.5/5.0 **Total Reviews:** 102 ## About KNIME KNIME helps everybody make sense of data. Its free and open source KNIME Analytics Platform enables anyone — whether they come from a business, technical or data background — to intuitively work with data, every day. KNIME Business Hub is the commercial complement to KNIME Analytics Platform and enables users to collaborate on data science and share insights across the organization. Together, the products support the complete data science lifecycle, allowing teams at all levels of analytics readiness to support the operationalization of data and to build a scalable data science practice. ## KNIME Pros & Cons **What users like:** - Users find **KNIME's ease of use** remarkable, enabling effortless workflow creation without coding expertise. (7 reviews) - Users value the **coding ease** of KNIME, making it accessible for beginners and non-technical users alike. (4 reviews) - Users find KNIME to be an **easy-to-learn platform** that enables quick delivery of data solutions without coding expertise. (4 reviews) - Users find KNIME to be **easy to learn** , enabling quick delivery of complex data analysis and AI solutions. (4 reviews) - Users praise the **intuitive data visualization capabilities** of KNIME, making complex data insights easily accessible. (3 reviews) - Easy Integrations (3 reviews) - Graphical Visualization (3 reviews) - Machine Learning (3 reviews) - AI Capabilities (2 reviews) - Automation (2 reviews) **What users dislike:** - Users find the **learning difficulty** of KNIME challenging, particularly with unfamiliar data science concepts and visual programming. (3 reviews) - Users often face **memory usage issues** with KNIME, leading to slow performance and difficulties handling large files. (3 reviews) - Users face **storage limitations** with KNIME, leading to memory issues and slow performance with large files. (3 reviews) - Users find **data management issues** with KNIME, particularly in improving file handling and accessing certain databases. (2 reviews) - Users find the **lack of learning resources** for KNIME frustrating, hindering their ability to effectively utilize the software. (2 reviews) - Users experience **limited storage** issues with KNIME Software, affecting performance and usability during data processing. (2 reviews) - Not User-Friendly (2 reviews) - Beginner Difficulty (1 reviews) - Data Analysis Difficulty (1 reviews) - Difficult Navigation (1 reviews) ## KNIME Reviews ### 1. KNIME’s Free No-Code Drag-and-Drop Analytics, from Descriptive to Agentic AI **Rating:** 5.0/5.0 stars **Reviewed by:** Guylaine B. | Analytics and Data Management Trainer, Professional Training & Coaching, Small-Business (50 or fewer emp.) **Reviewed Date:** June 22, 2026 **What do you like best about KNIME?** What I like best is KNIME's no-code, drag-and-drop approach, which makes it accessible to anyone interested in analytics, regardless of coding background. It also covers the entire analytics maturity spectrum — from basic descriptive analytics to advanced and agentic AI, all within a single tool. That means there's only one platform to learn, and it's both easy to pick up and COMPLETELY FREE, which lowers the barrier to entry enormously for individuals and organizations alike. **What do you dislike about KNIME?** Building visually polished data apps isn't very intuitive — it takes more effort than it should. **What problems is KNIME solving and how is that benefiting you?** KNIME solves the problem of making analytics accessible across skill levels and industries. Because no coding is required, I can use it to teach both technical and non-technical audiences, regardless of their background or sector. This benefits me directly as a trainer: I have one tool that adapts to any audience, which simplifies my curriculum design and makes analytics concepts approachable for people who might otherwise be intimidated by code-based tools. ### 2. KNIME’s Visual Workflows - One of the best tool for Auditing, Accounting & Finance Professionals **Rating:** 5.0/5.0 stars **Reviewed by:** Charm M. | Group Audit Manager, Oil & Energy, Mid-Market (51-1000 emp.) **Reviewed Date:** June 18, 2026 **What do you like best about KNIME?** As an experienced Internal Audit Manager, regional data analytics community contributor, and data analytics trainer, I confidently consider KNIME one of the best tools for Auditing, Accounting & Finance, Risk, and Compliance (AAFRC) professionals. The most helpful feature is its visual workflow approach, which provides a clear end-to-end view of the entire analytical process. This makes analyses transparent, easy to review, and highly auditable. Key upsides include the ability to efficiently analyze multiple subgroups using looping nodes and identify anomalies using outlier detection techniques. KNIME also requires little to no coding to get started, allowing professionals to focus on insights rather than programming. In my experience, KNIME has significantly improved the efficiency and effectiveness of my audit projects, enabling broader testing, better risk identification, and higher productivity. **What do you dislike about KNIME?** No significant downsides from my experience. KNIME provides strong value through its visual workflows, automation, flexibility, and ability to improve analytical efficiency. **What problems is KNIME solving and how is that benefiting you?** KNIME helps me solve key business problems in audit analytics and data analysis: Large data volume management – Analyze large datasets beyond Excel limitations efficiently. Workflow automation – Build repeatable workflows to reduce manual effort and improve consistency. Advanced analytics & Machine Learning – Apply ML techniques for deeper analysis, pattern discovery, and risk identification beyond traditional spreadsheet capabilities. Lower sampling risk – Enable broader or full-population analysis for stronger audit coverage. Multi-group analysis & segmentation – Handle complex segmentation across branches, business units, or risk groups, which is critical in audit analytics. Anomaly detection & risk assessment – Identify unusual patterns, potential risks, and support data-driven decisions. Overall, KNIME improves audit efficiency, coverage, and the quality of insights. ### 3. Workflows executed by business users with KNIME Business Hub’s Strong Functionality **Rating:** 5.0/5.0 stars **Reviewed by:** Adrian K. | CEE BI Systems & Tools Manager, Enterprise (> 1000 emp.) **Reviewed Date:** July 07, 2026 **What do you like best about KNIME?** I can't imagine better way to troubleshoot a workflow than what KNIME Business Hub provides. You literally see your workflow on KBH (in web browser) like in the desktop app where you've designed it - with errors and warnings generated during the execution. So easy! **What do you dislike about KNIME?** Variable Loop End has a kink that costed me some headache - order of flow variables is important apparently. But that's a minor thing only. **What problems is KNIME solving and how is that benefiting you?** I created a standard model for external data processing in my company that business users can self-serve. The model was implemented about 15 times by now. Our KAMs upload their customers’ sell-out data into our database on their own, without complaints. This was possible thanks to Business Hub functionalities. As a result, it frees up my time and the BI team’s time for new projects and deeper business analysis. ### 4. A Powerful, Intuitive, and Scalable Platform for Data Science and AI **Rating:** 5.0/5.0 stars **Reviewed by:** Arief R. | Founder, Non-Profit Organization Management, Small-Business (50 or fewer emp.) **Reviewed Date:** June 19, 2025 **What do you like best about KNIME?** KNIME stands out as a truly versatile open-source platform that empowers users to perform complex data analysis and develop AI solutions without writing code. Its visual workflow interface significantly lowers the barrier for non-programmers while still providing advanced extensibility for experts. KNIME integrates well with Python, R, and popular machine learning libraries, enabling seamless end-to-end development from data preparation to model deployment. The platform’s community, extensible ecosystem, and commitment to openness make it an invaluable tool for data professionals across industries **What do you dislike about KNIME?** While KNIME provides powerful functionality, the initial learning curve can be challenging for users unfamiliar with data science concepts or visual programming. Some integrations, especially with external APIs or cloud services, may require additional configuration or third-party extensions. Nonetheless, the documentation and community support largely mitigate these limitations. **What problems is KNIME solving and how is that benefiting you?** KNIME Software addresses the critical gap between data and actionable insights by providing a low-code/no-code environment that allows users of all skill levels to perform advanced data analytics, machine learning, and AI-driven workflows. It solves the challenge of accessibility in data science—empowering business analysts, educators, and researchers to analyze, visualize, and automate data processes without the need for complex programming. Personally, KNIME has enabled me to democratize data and AI in various domains, including education, government, and industry. I have used KNIME to build solutions for customer segmentation, churn prediction, marketing automation, and text/image analytics—helping organizations make data-driven decisions faster and more efficiently. Its intuitive interface, scalability, and strong integration with open-source tools have made it a cornerstone in my daily work as a data professional. ### 5. Strong No-code Analytics Tool that grows with you **Rating:** 4.5/5.0 stars **Reviewed by:** Karan S. | Enterprise (> 1000 emp.) **Reviewed Date:** July 07, 2026 **What do you like best about KNIME?** I use KNIME Software to build visual, low-code data workflows, which makes data cleaning, analysis, and machine learning tasks much easier. It's great for RPA-style automation because I can chain repetitive tasks and avoid manual work. I like that I can create reusable workflows with KNIME that keep everything consistent and help reduce manual mistakes. The visual workflow builder is fantastic because it's easy to see exactly what's happening at each step, and I don't have to rewrite an entire script if I need to update something—I just modify the workflow. It really simplifies understanding and troubleshooting my process, allowing me to quickly identify what needs to be changed without sifting through long scripts. **What do you dislike about KNIME?** The interface can feel overwhelming at first because there are so many nodes and configuration options. It takes some time to understand the best way to structure workflows, especially if you are new to the platform. The onboarding experience could be improved with more guided tutorials, sample end-to-end workflows, and more interactive walkthroughs. This would make it much easier to get comfortable with the platform without having to rely on external tutorials heavily. The main challenge was understanding how different nodes work and how to design efficient workflows. **What problems is KNIME solving and how is that benefiting you?** I use KNIME Software to build visual, low-code workflows for data cleaning, analysis, and automation, reducing manual work and errors by creating reusable workflows. It helps me avoid repeating tasks across projects, keeping everything consistent. ### 6. KNIME Automates Workflows for Major Time Savings and Accuracy **Rating:** 5.0/5.0 stars **Reviewed by:** Brandon H. | Data Quality Analyst II / Automation Developer, Enterprise (> 1000 emp.) **Reviewed Date:** April 21, 2026 **What do you like best about KNIME?** What I find most helpful about KNIME is that it lets me automate workflows and cut down on manual work. It’s a solid way to replace EUCs and reduce the chance of human error. Overall, the biggest upside for me is the time savings, along with better accuracy. It also makes my processes more efficient and, importantly, more reliable. **What do you dislike about KNIME?** The least helpful part of KNIME is that some workflows can become complex and, over time, harder to maintain. Another downside is the initial learning curve, especially when you’re just getting started. I’ve also noticed that performance can slow down when working with very large datasets or more complex processes. **What problems is KNIME solving and how is that benefiting you?** KNIME helps solve the problem of manual, error-prone processes and an overreliance on EUCs. It enables me to automate workflows and standardize recurring tasks. As a result, I see fewer errors, save time, and get processes that are more consistent and efficient overall. ### 7. A lifesaver for blending hardcore coding with visual, accessible workflows **Rating:** 5.0/5.0 stars **Reviewed by:** Lokesh S. | Senior Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** June 08, 2026 **What do you like best about KNIME?** I work as a Senior Data Scientist at a mid-sized company, and we use KNIME primarily as a bridge between our data science team and the business intelligence analysts. We use it to rapidly prototype machine learning models, automate messy data extraction pipelines, and blend data from our internal databases with third-party vendor files. It serves as our central hub for building data workflows that non-coders can actually understand and execute.The absolute biggest advantage of KNIME is how it perfectly balances visual drag-and-drop simplicity with deep technical flexibility. As a data scientist, I hate being locked into rigid GUI tools, but KNIME allows me to drop custom Python, R, or SQL snippets right into the middle of a visual pipeline whenever I need to do something highly specific. The platform is completely open-source, which made it incredibly easy to pilot and adopt within our medium-sized organization without fighting for massive budget approvals. I also genuinely appreciate the massive repository of pre-built nodes. Whether we are doing basic data joining, complex text mining, or connecting to AWS, there is almost always a node ready to handle the heavy lifting, which drastically speeds up our prototyping phase. **What do you dislike about KNIME?** Despite its underlying power, the user interface feels incredibly dated and clunky, reminding me of software from a decade ago. While it is a visual tool, the learning curve is surprisingly steep for new hires because the sheer volume of available nodes can be completely overwhelming to navigate. My biggest frustration, however, is how KNIME handles memory management on local machines. When processing datasets with millions of rows, the software acts as a massive memory hog. If you do not dive into the backend configuration files to manually adjust the RAM allocation, the platform frequently slows to an agonizing crawl or outright freezes during heavy transformations, which has led to lost work on more than one occasion. **What problems is KNIME solving and how is that benefiting you?** KNIME completely eliminated a major bottleneck we had with routine data preparation. Previously, my team was spending hours every week writing custom scripts just to pull CRM data, clean up messy marketing CSVs, and merge them for the reporting team. We built a KNIME workflow that automates this entire data blending process, saving us countless hours. Another huge win was deploying a customer churn prediction model. Instead of building a black-box Python script that our marketing managers were intimidated by, we built the scoring pipeline visually in KNIME. The marketing team can now look at the workflow, understand the logical steps, and run the churn scores themselves every week without ever needing to submit a support ticket to the data science team. ### 8. Flexible and Efficient Workflow Builder with Minor Performance Issues **Rating:** 4.0/5.0 stars **Reviewed by:** Erik D. | Ass.Prof., Research, Mid-Market (51-1000 emp.) **Reviewed Date:** July 09, 2026 **What do you like best about KNIME?** I really like how flexible KNIME Software is for building workflows. There's a lot of libraries and plugins available which makes it easy to compose complex behaviors without needing to code if you don't want to. It's adaptable to people with different levels of experience, allowing both beginners and more experienced users to contribute effectively. The software makes it easy to perform complex analysis tasks and have different variants of standard workflows, which is great for collaborative work and for building experience. **What do you dislike about KNIME?** Using it on Windows, sometimes it is a bit sluggish. **What problems is KNIME solving and how is that benefiting you?** KNIME Software lets me build reproducible and standard workflows for computational biology, ensuring consistent and reliable results. I can share these workflows with colleagues, fostering collaboration and maintaining high-quality outcomes. ### 9. Intuitive and Cost-Effective, But Resource-Heavy **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User | Mid-Market (51-1000 emp.) **Reviewed Date:** July 16, 2026 **What do you like best about KNIME?** I use KNIME Software as an open-source, low-code platform to build visual drag-and-drop data pipelines, allowing us to automate complex data preparation and modeling workflows without needing to write code, while still giving us the flexibility to script in Python or R for custom logic. I really like how it bridges the technical gap by letting business analysts visually construct complex data and machine learning pipelines without writing SQL or Python code. The node-based layout acts as a self-documenting flowchart, making it easy to audit, debug, and explain workflows to stakeholders. I love KNIME's massive open-source library of pre-built nodes, which makes blending different tools like Python, SQL, and Excel on a single canvas seamless. The intuitive traffic-light node status indicators make testing and debugging visual workflows easy and eliminate the guesswork by providing instant, node-by-node feedback on pipeline issues. Additionally, the library acts as an app store for data, allowing us to connect legacy files to modern AI models with just a drag-and-drop node instead of writing custom API integration code. The initial setup was easy and friction-free. **What do you dislike about KNIME?** I find KNIME Software to be an absolute memory hog, frequently crashing on large datasets due to its aggressive step-by-step caching. Configuring its clunky Python integrations is notoriously painful. Additionally, complex business logic quickly transforms workflows into a chaotic spaghetti of nodes that are highly disorienting to navigate and organize. **What problems is KNIME solving and how is that benefiting you?** I find that KNIME Software bridges the technical gap by allowing us to build data pipelines without code. Its self-documenting feature makes auditing and explaining workflows easy for us. We appreciate its open-source library and traffic-light node status which simplify our debugging and integration with tools like Python and SQL. ### 10. The Ultimate Time-Saver for Complex Data Pipelines **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** July 09, 2026 **What do you like best about KNIME?** What I love most about KNIME is its intuitive, drag-and-drop visual workflow interface. It allows me to build complex data cleaning pipelines and machine learning models without needing to write extensive code, which saves me hours of manual work every week. **What do you dislike about KNIME?** While the visual workflow is great, large and complex pipelines can quickly become messy and difficult to navigate. The user interface, while functional, feels a bit dated compared to modern web-based data tools, and it can sometimes take too many clicks to find specific configuration settings within a node. **What problems is KNIME solving and how is that benefiting you?** Problem Solved: We struggled with manual, repetitive data preparation tasks that involved stitching together multiple massive Excel spreadsheets every week. It was highly error-prone and consumed hours of engineering time. Benefit: KNIME allows us to completely automate these ETL pipelines. What used to take a full day of manual data cleansing now runs in minutes at the click of a button, drastically reducing human error and freeing up our team to focus on actual data analysis rather than data wrestling. ## KNIME Discussions - [Is Knime easy to use?](https://www.g2.com/discussions/is-knime-easy-to-use) - 1 comment - [View KNIME pricing details and edition comparison](https://www.g2.com/products/knime-analytics-platform/reviews/knime-review-4817489?section=pricing&secure%5Bexpires_at%5D=2026-07-17+04%3A05%3A56+-0500&secure%5Bsession_id%5D=d3c421b6-9244-4d27-88b7-51705f9800de&secure%5Btoken%5D=071ac4eb46df674a863f282a857eaab55a038871848695b6b38137ecee3ede42&format=llm_user) ## KNIME Integrations - [Amazon Athena](https://www.g2.com/products/amazon-athena/reviews) - [Amazon Redshift](https://www.g2.com/products/amazon-redshift/reviews) - [APACHE](https://www.g2.com/products/apache/reviews) - [Box](https://www.g2.com/products/box/reviews) - [Claude](https://www.g2.com/products/claude-2025-12-11/reviews) - 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Ease of Data Connectivity - API Connectivity **Administration** - Quality Control - Data Sampling - Collaboration **Management** - Reporting - Auditing **System** - Data Ingestion & Wrangling **Data Preparation** - Connectors - Data Governance **Responses** - Personalization - Route To Human - Natural Language Understanding (NLU) **Automation - AI Agents** - Sales Follow-Up - Customer Interaction Automation - Lead Generation - Document Processing - Feedback Collection **Customization - AI Agent Builders** - Natural Language Configuration - Tone Customization - Security Guardrails **Data Ingestion & Preparation - Low-Code Machine Learning Platforms** - Automatic Data Profiling & Quality Assessment - Multi‑Source Connector Support - Schema Drift / Change Detection **Data Connectivity and Prep - Agentic Analytics** - Data Source Connectivity - Automated Data Preparation **Statistical Tool** - Scripting - Data Mining - Algorithms **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training **Data Interaction** - Profiling and Classification - Metadata Management - Data Modeling - Data Joining - Data Blending - Data Quality and Cleansing - Data Sharing - Data Governance **Capabilities** - Data Visualization - Survival Analysis - Risk Data Attributes - Cost Analysis **Functionality** - Extraction - Transformation - Loading - Automation - Scalability **Model Development** - Feature Engineering **Data Modeling and Blending** - Data Querying - Data Filtering - Data Blending **Platform** - Conversation Editor - Integration - Human-In-The-Loop **Autonomy - AI Agents** - Independent Decision Making - Adaptive Responses - Task Execution - Problem Solving **Functionality - AI Agent Builders** - Omni-channel Support - Agent Branding - Proactive Response Capabilities - Seamless Human Escalation **Model Construction & Automation - Low-Code Machine Learning Platforms** - Guided Algorithm & Hyperparameter Recommendation - Code Extensibility - Automated Feature Engineering **Autonomous Insight Generation - Agentic Analytics** - Continuous Pattern Detection - Multi‑Step Reasoning - Predictive & Prescriptive Analytics **Data Analysis** - Analysis - Data Interaction **Machine/Deep Learning Services** - Computer Vision - Natural Language Processing - Natural Language Generation - Artificial Neural Networks **Data Exporting** - Breadth of Integrations - Ease of Integrations - Data Workflows **Methodology** - ANOVA Support - Regression - Time Series Analysis **Machine/Deep Learning Services** - Natural Language Understanding - Deep Learning **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization **Data and Analytics - AI Agent Builders** - Analytics & Reporting - Contextual Awareness - Data Privacy Compliance **Agentic AI - AI Agents** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Interaction and Workflow - Agentic Analytics** - Natural Language Query and Conversational Analytics - Action Triggering & Workflow Orchestration - Explainability & Audit Trails **Decision Making** - Modeling - Data Visualizations - Report Generation - Data Unification **Deployment** - Managed Service - Application - Scalability **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation **Integration - AI Agent Builders** - Workflow Automation - API Usage - Platform Interoperability - CRM Data Integration **Agentic AI - Analytics Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **AI Agent Management - Agentic Analytics** - Agent Configuration & Goals - Continuous Learning & Feedback **Self Service ** - Calculated Fields - Data Column Filtering - Data Discovery - Search - Collaboration / Workflow - Automodeling **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image **Deployment & Integration - Analytics Platforms** - No-code Dashboard Builder - Report Scheduling and Automation - Embedded Analytics and White-labeling - Data Source Connectivity **Advanced Analytics** - Predictive Analytics - Data Visualization - Big Data Services **Agentic AI - Data Science and Machine Learning Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Performance & Scalability - Analytics Platforms** - Large data handling and Query Speed - Concurrent User Support **Advanced Analytics & Modeling - Analytics Platforms** - Data Modeling and Governance - Notebook and Script Integration - Built-in Predictive and Statistical Models **Agentic AI Capabilities - Analytics Platforms** - Auto-generated Insights and Narratives - Natural Language Queries - Proactive KPI Monitoring and Alerts - AI Agents for Analytical Follow-ups **Personalized Intelligence - Analytics Platforms** - Behavioral Learning for Contextual Query Refinement - Role-based Insight Personalization - Conversational and Prompt-based Analytics **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs ## Top KNIME Alternatives - [Alteryx](https://www.g2.com/products/alteryx/reviews) - 4.6/5.0 (845 reviews) - [Dataiku](https://www.g2.com/products/dataiku/reviews) - 4.4/5.0 (210 reviews) - [Tableau](https://www.g2.com/products/tableau/reviews) - 4.4/5.0 (3,664 reviews)