Compare Databricks and KNIME on pricing, features, platforms, and ratings — both Business Intelligence tools, Usage-based (pay-as-you-go per Databricks Unit)…
At a Glance
Databricks
KNIME
Primary category
Business Intelligence
Business Intelligence
Rating
Not documented
Not documented
Pricing model
Usage-based (pay-as-you-go per Databricks Unit)
Open Source / Freemium
Starting price
Free Community Edition available; paid usage billed per Databricks Unit (DBU) plus underlying cloud compute costs
Free (KNIME Analytics Platform); Business Hub is custom priced
Free plan
Yes
Yes
Free trial
Yes
Not documented
Platforms
Web
Mac, Windows
Team collaboration
Not documented
Not documented
AI features
Yes
Yes
Public API
Yes
Yes
Pricing Compared
Starting price reflects the lowest paid tier, not the full cost for every team size or usage level.
Databricks
Premium — Pay-as-you-go, from ~$0.07/DBU Usage-based
Enterprise — Custom (negotiated with sales) Usage-based
KNIME
KNIME Analytics Platform — Free N/A
KNIME Community Hub Pro — Reportedly ~$19/month Monthly
KNIME Community Hub Team — Reportedly ~$99/month Monthly, for small teams (e.g. 3 users)
KNIME Business Hub — Custom pricing Contact sales
Pros & Cons
Databricks
Pros
Unified platform reduces the need for separate data warehouse, data lake, and ML tooling
Built on widely adopted open-source projects (Spark, Delta Lake, MLflow), lowering vendor lock-in risk
Scales efficiently for very large data engineering and machine learning workloads
Strong native support for generative AI and custom model development via Mosaic AI
Cons
Consumption-based DBU pricing can be complex to forecast and may escalate at scale
Requires meaningful technical expertise in Spark, SQL, and cloud infrastructure
No flat, predictable starting price for smaller teams or individual users
Cost governance and cluster configuration require dedicated platform engineering resources
KNIME
Pros
Full-featured desktop Analytics Platform is free and open source with no feature gating
No-code visual builder lowers the barrier to entry for non-programmers
Native support for blending Python, R, SQL and other code within visual workflows
Covers the full data science lifecycle from ETL through machine learning to deployment
Large, established user community of 300,000+ across more than 60 countries
Cons
Enterprise Business Hub pricing is not publicly disclosed and requires contacting sales
Java/Eclipse-based desktop interface can feel dated compared to modern cloud-native tools
Steeper learning curve for very large or complex workflows compared to code-first tools
Production deployment and governance features require a separate paid Hub tier
Less native cloud-first collaboration experience than competitors like Dataiku
Use Cases
Databricks
Enterprise Data Engineering and ETL — Building and orchestrating large-scale batch and streaming data pipelines using Delta Lake and Databricks Workflows.
Machine Learning and Generative AI Development — Training, tracking, and deploying ML models and building custom LLM applications with Mosaic AI and MLflow.
Enterprise Analytics at Scale — Running warehouse-style SQL analytics and BI reporting directly on governed lakehouse data via Databricks SQL.
KNIME
Data Blending and ETL Automation — Analysts combine and clean data from multiple databases and file sources without writing custom ETL code.
Machine Learning Model Development — Data scientists build and test classification, regression and clustering models visually, blending in custom Python or R code as needed.
Production Data Pipeline Deployment — Organizations deploy analyst-built workflows as scheduled jobs or REST API services using KNIME Business Hub.