KNIME Analytics Platform - 2026 Pricing, Features, Reviews & Alternatives

KNIME lets users analyze, upskill and scale data science without the need for coding. It's a complete platform for data science that lets users blend, transform, model & visualize data, deploy & monitor analytical models, and share insights organization-wide with data apps and services. Specifically, users can:

Access, blend, and transform data from any source

* Access data from a laptop, an application, or a data warehouse

* Easily blend data of any size and type

* Aggregate, sort, filter & join data on a device, in-database, or in distributed big data environments

Visualize & analyze data

* Explore data with interactive charts and visualizations

* Automate spreadsheets or other manual, repetitive data tasks

* Choose from a complete range of analytic techniques, with access to all popular ML libraries

Save, share, reuse, & collaborate on workflows

* Bundle segments of workflows as components for reuse

* Share & collaborate in teams

* Collaboratively & a-synchronously develop analytical solutions with version control

* Make Python functionality available to the wider organization

* Add custom functionality through coding

Upskill business & domain experts

* Create a knowledge base for teams, departments, or the entire organization with KNIME Business Hub

* Set standards for data access & routine operations, share best practices to enable self-onboarding for new users

* Browse, learn from & share solutions on KNIME Community Hub, which has 14,000+ data science solutions spanning use cases

* Ask questions & connect with peers on the KNIME Community Forum

Deploy enterprise-ready apps & services with KNIME Business Hub

* Deploy workflows as interactive data apps or REST APIs, bringing insights to anyone in the organization

* Choose on-prem or in-cloud installation, scale up with cloud-native architecture

* Minimize IT bottlenecks with centralized administration, but with deployment monitoring at the team level

* Build, administer & automate a custom ModelOps process to facilitate the continuous delivery of data science

KNIME Analytics Platform has 300+ connectors to data sources: big data environments (Hive, Spark, HDFS), file systems (Excel, CSV, JSON, image & audio files, networks, MDF files), integrations (Python, R, H2O, sas7bdat), databases (Oracle, Snowflake, PostgreSQL, MySQL, Microsoft Access), web services (REST, SPARQL endpoint, triples, Salesforce, RSS feeds), cloud authentication providers (Google, Microsoft, Amazon), cloud storage systems (Azure, Google Drive, Amazon S3, Sharepoint, Azure Blob), distributed file systems (HDFS, Databricks), cloud databases (Google BigQuery, Amazon Dynamo DB, Redshift, Athena), servers (KNIME, SSAP, SMB), cloud web services (Google Sheets, Google Analytics, various AWS AI/ML services). And many more.

KNIME Analytics Platform also offers integrations to all popular ML libraries such as R, Python, Jupyter Notebooks, Spark, Hive, Impala, H2O, Keras, Tensorflow, ONYX, Google Drive, D3, Plotly, Azure, Open Street Maps, & many more.

To support development goals, KNIME has implemented rigorous software engineering standards. A key part of these standards focuses on ensuring all aspects of software security. Our security policy (www.knime.com/security/policy) includes a structured approach for exchange & the communication of security related topics, & guides on how to report potential vulnerabilities.

The extensive capabilities & choices available with KNIME means that each organization will want to implement KNIME based on their business & security requirements. KNIME provides software capabilities & best practices from our customers so that an organization can implement & test their own security compliance regime. A detailed overview of our development framework & security approach can be found at knime.com/knime-software-framework-and-security.

A summary of all security advisories can be found at knime.com/security/advisories.