Software · head to head
Dataiku vs KNIME
The short version
- Each has a real cost: Dataiku no pricing is published at any tier, and the plans page carries no figures at all; KNIME the free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub
- They diverge on capability: Dataiku covers Visual data prep, KNIME covers Visual workflows.
Where they differ
Only the attributes on which Dataiku and KNIME actually diverge.
Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), user rating (Not yet rated), category (Unknown).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Dataiku
- Visual data prep
- AutoML
- MLOps
- Collaboration
- Governence
- Snowflake
- Databricks
- Web support
Only in KNIME
- Visual workflows
- Data preprocessing
- Machine learning
- Visualization
- Reporting
- H2O
- TensorFlow
Both cover
- Python
- R
- Spark
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
Dataiku
- Building and deploying data science and machine learning pipelinesnot KNIME
- Giving analysts and data scientists a shared visual and code environmentnot KNIME
KNIME
- Building data pipelines and analytics workflows visually rather than in codenot Dataiku
- Connecting and blending data across many sources for analysisnot Dataiku
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Dataiku
- No pricing is published at any tier, and the plans page carries no figures at all
- User, row and compute limits are not stated, so nothing about scale can be assessed before contacting sales
- Access begins with a demo request or a trial rather than a self serve signup
KNIME
- The free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub
- The free AI assistant is limited to 20 interactions a month
- Paid workflow runtime is metered in credits, with 120 included on Pro and overage at $0.025 per vCore minute
- The Team plan at $99 a month includes 3 members, with additional seats at $49 a month each
- Business Hub pricing is on request, and its tiers are capped at 4, 8 and 16 vCores with 5, 5 and 20 users
Pricing, plan by plan
Dataiku
Free- Free EditionFree
- Single user
- Core features
- EnterpriseFree
- Full platform
- Collaboration
- MLOps
KNIME
Free- Analytics PlatformFree
- Visual workflows
- All nodes
- Community extensions
- ServerFree
- Team collaboration
- Workflow automation
- REST API
Which should you pick?
Choose Dataiku if
- You need visual data prep.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want automl.
Choose KNIME if
- You need visual workflows.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want data preprocessing.
Questions people ask
- Is Dataiku or KNIME better?
- Neither clearly leads. Dataiku starts at Free and KNIME at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Dataiku or KNIME?
- Dataiku starts at Free and KNIME at Free.
- Does Dataiku or KNIME run on more platforms?
- Dataiku runs on Linux, Mac, Windows, Web. KNIME runs on Linux, Mac, Windows.
- Can I use Dataiku for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Dataiku best used for?
- Dataiku is most often used for building and deploying data science and machine learning pipelines, giving analysts and data scientists a shared visual and code environment. Of those, building and deploying data science and machine learning pipelines and giving analysts and data scientists a shared visual and code environment are not what KNIME is typically brought in for.
- What can Dataiku do that KNIME cannot?
- Dataiku covers Visual data prep, AutoML, MLOps, Collaboration. KNIME covers Visual workflows, Data preprocessing, Machine learning, Visualization. Both handle Python, R, Spark, Linux support.