Software · head to head
BentoML vs KNIME
The short version
- Each has a real cost: BentoML core BentoML framework is Apache 2.0 and free, but the managed BentoCloud enterprise tier has no published pricing: the README instructs buyers to sign up for personal access or contact sales for enterprise use, with no rate card shown.; KNIME the free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub
- They diverge on capability: BentoML covers Model packaging, KNIME covers Visual workflows.
Where they differ
Only the attributes on which BentoML and KNIME actually diverge.
Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), platforms (Linux, Mac, Windows), 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 BentoML
- Model packaging
- REST API generation
- Adaptive batching
- Multi-framework support
- Container deployment
- PyTorch
- scikit-learn
- XGBoost
Only in KNIME
- Visual workflows
- Data preprocessing
- Machine learning
- Visualization
- Reporting
- Python
- R
- Spark
Both cover
- TensorFlow
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
BentoML
- Machine learningnot KNIME
- Data analysisnot KNIME
- Model trainingnot KNIME
- Predictive analyticsnot KNIME
KNIME
- Building data pipelines and analytics workflows visually rather than in codenot BentoML
- Connecting and blending data across many sources for analysisnot BentoML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
BentoML
- Core BentoML framework is Apache 2.0 and free, but the managed BentoCloud enterprise tier has no published pricing: the README instructs buyers to sign up for personal access or contact sales for enterprise use, with no rate card shown.
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
BentoML
Free- Open SourceFree
- Model packaging
- API creation
- Local serving
- BentoCloudFree
- Managed deployment
- Auto-scaling
- Monitoring
KNIME
Free- Analytics PlatformFree
- Visual workflows
- All nodes
- Community extensions
- ServerFree
- Team collaboration
- Workflow automation
- REST API
Which should you pick?
Choose BentoML if
- You need model packaging.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want rest api generation.
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 BentoML or KNIME better?
- Neither clearly leads. BentoML 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, BentoML or KNIME?
- BentoML starts at Free and KNIME at Free.
- Does BentoML or KNIME run on more platforms?
- Both run on Linux, Mac, Windows, so platform support will not decide this one for you.
- Can I use BentoML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is BentoML best used for?
- BentoML is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what KNIME is typically brought in for.
- What can BentoML do that KNIME cannot?
- BentoML covers Model packaging, REST API generation, Adaptive batching, Multi-framework support. KNIME covers Visual workflows, Data preprocessing, Machine learning, Visualization. Both handle TensorFlow, Linux support, Mac support, Windows support.