KNIME
Data science and machine learning platforms
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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$19 per month
Free TrialFree version
Small
Medium
Large
- Professional services (engineering, legal, consulting, etc.)
- Agriculture, fishing, and forestry
- Retail and wholesale
What is KNIME
KNIME is a data science and machine learning platform centered on a visual, node-based workflow interface for data preparation, analytics, and model development. It is used by data analysts, data scientists, and business users to build repeatable pipelines that combine data access, transformation, modeling, and reporting. The platform includes a free desktop application and commercial server capabilities for collaboration, scheduling, and deployment. KNIME emphasizes extensibility through integrations (including Python/R and popular ML libraries) and a large ecosystem of community and vendor-supported extensions.
Visual, reproducible workflows
KNIME’s node-and-connection paradigm makes end-to-end analytics pipelines explicit and easier to review than ad hoc scripts. Workflows can be parameterized and reused, which supports repeatability across teams and projects. The visual approach can reduce time-to-prototype for common data preparation and modeling tasks. It also helps document lineage at the workflow level for operational handoffs.
Broad integration ecosystem
KNIME supports a wide range of connectors and extensions for databases, files, cloud services, and analytics tooling. It integrates with Python and R, allowing teams to combine visual steps with code-based components when needed. This hybrid approach can fit mixed-skill teams and heterogeneous toolchains. The extension model enables adding domain-specific capabilities without changing the core product.
Enterprise deployment options
Beyond the desktop application, KNIME offers server capabilities for sharing workflows, managing execution, and scheduling jobs. Centralized deployment supports collaboration and more controlled promotion of workflows into production-like environments. Teams can standardize common components and govern access to shared assets. This helps move from individual experimentation to managed operational use cases.
Scaling depends on architecture
Large datasets and complex workflows can stress local desktop execution, pushing teams toward server execution and external compute engines. Performance and scalability often depend on how data is accessed (in-memory vs. pushdown to databases) and which extensions are used. Organizations may need additional infrastructure planning to meet high-throughput or low-latency requirements. This can add operational complexity compared with platforms that abstract compute more fully.
Workflow complexity can grow
As projects expand, visual workflows can become difficult to navigate and maintain without strong conventions. Debugging can require stepping through multiple nodes and intermediate tables, which may be slower than code-centric debugging for some users. Teams often need governance around naming, modularization, and versioning to keep pipelines understandable. Without this discipline, workflows can become hard to audit and refactor.
Production MLOps is not turnkey
While KNIME supports deployment and scheduling, end-to-end MLOps capabilities (such as model registry, automated retraining pipelines, and advanced monitoring) may require additional tooling and integration work. Operationalizing models across environments can involve custom patterns and external services. Organizations with mature ML engineering practices may find gaps relative to platforms designed primarily for managed ML lifecycle operations. This can increase implementation effort for regulated or high-governance use cases.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| KNIME Analytics Platform | Free | Free, open-source desktop application; local execution (unlimited); connect to 300+ data sources; Personal/Community Hub access with limited K-AI (20 interactions/month). |
| Pro | Starts at $19 / month (or €19 / month) | For individual users: 120 execution credits included; deploy & automate workflows and data apps; 500 K-AI interactions/month included; additional runtime charged at $0.025 (or €0.025) per vCore minute; additional K-AI bundles charged (€/$0.025 per 5 requests). |
| Team | Starts at $99 / month (or €99 / month) | Small-team plan: all Pro features plus private team spaces (3 team members included; additional seats $49 / month or €49 / month); centralized billing; cloud execution; free 1-month trial (up to 3 users) with 1,000 execution credits (per KNIME’s Team trial offer). |
| Business Hub | Pricing available on request | Enterprise-grade features: staged deployment, LDAP/OAuth/OIDC, GenAI gateway, dedicated resources, enterprise support (US/EU business hours or more); contact sales for pricing. |
Seller details
KNIME AG
Zurich, Switzerland
2008
Private
https://www.knime.com/
https://x.com/KNIME
https://www.linkedin.com/company/knime/
