Machine Learning & Data Science · head to head
scikit-learn vs Weaviate
scikit-learn
Machine Learning & Data Science
Machine learning in Python
- From
- Free
- Rated
- -
The short version
- Each has a real cost: scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays; Weaviate the free tier caps at 100,000 objects, 1 GB of memory and a single collection
- They diverge on capability: scikit-learn covers Classification algorithms, Weaviate covers Vector and keyword search.
Where they differ
Only the attributes on which scikit-learn and Weaviate actually diverge.
| Attribute | scikit-learn | Weaviate |
|---|---|---|
| Pricing model | Unknown | freemium |
| Platforms | Python, Linux, macOS, Windows | Linux, Mac, Windows, Web |
| Founded | 2007 | 2019 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).
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 scikit-learn
- Classification algorithms
- Regression models
- Clustering methods
- Dimensionality reduction
- Model selection
- NumPy
- SciPy
- Pandas
Only in Weaviate
- Vector and keyword search
- Built-in vectorizers
- GraphQL API
- Multi-tenancy
- Hybrid search
- OpenAI
- Hugging Face
- Cohere
Both cover
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
scikit-learn
- Machine learningnot Weaviate
- Data analysisnot Weaviate
- Model trainingnot Weaviate
- Predictive analyticsnot Weaviate
Weaviate
- Running a vector database for semantic and hybrid searchnot scikit-learn
- Generating and storing embeddings alongside the objects they describenot scikit-learn
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
scikit-learn
- No GPU acceleration by default; limited optional GPU support requires external arrays
- Single-machine only; no built-in distributed computing across clusters
- All datasets must fit entirely in RAM; no out-of-core learning
- No production-grade deep learning; neural network support limited to basic multilayer perceptron
- No reinforcement learning algorithms
Weaviate
- The free tier caps at 100,000 objects, 1 GB of memory and a single collection
- Billing is per million vector dimensions rather than per record, so wider embeddings cost proportionally more for the same object count
- Premium is a prepaid contract starting at $400 a month rather than pay as you go
- Storage rates do not fall consistently with tier, and Premium Dedicated is $0.1505 per GiB against $0.12 on the cheaper Flex plan
- The Query Agent is metered separately, free to 1,000 requests a month and $30 a month plus overage beyond
Pricing, plan by plan
scikit-learn
FreeNo published plan breakdown. See the scikit-learn review.
Weaviate
Free- Open SourceFree
- Full features
- Self-hosted
- ServerlessFree
- Managed service
- Auto-scaling
Which should you pick?
Choose scikit-learn if
- You need classification algorithms.
- You want to start without paying.
- You work on Python, Linux, macOS, Windows.
- You also want regression models.
Choose Weaviate if
- You need vector and keyword search.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want built-in vectorizers.
Questions people ask
- Is scikit-learn or Weaviate better?
- Neither clearly leads. scikit-learn starts at Free and Weaviate at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, scikit-learn or Weaviate?
- scikit-learn starts at Free and Weaviate at Free.
- Does scikit-learn or Weaviate run on more platforms?
- scikit-learn runs on Python, Linux, macOS, Windows. Weaviate runs on Linux, Mac, Windows, Web.
- Can I use scikit-learn for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is scikit-learn best used for?
- scikit-learn is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Weaviate is typically brought in for.
- What can scikit-learn do that Weaviate cannot?
- scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction. Weaviate covers Vector and keyword search, Built-in vectorizers, GraphQL API, Multi-tenancy. Both handle Linux support, Mac support, Windows support.
Answered from the vendors’ own pages
scikit-learn: Does scikit-learn support GPU acceleration?
Scikit-learn has no native GPU support by design to keep installation simple and cross-platform. Since 2023, a limited number of estimators can run on GPUs if input data is provided as PyTorch or CuPy arrays, but this requires additional setup.
Sourcescikit-learn: Can scikit-learn handle datasets larger than RAM?
No. Scikit-learn is built on NumPy which requires all data to fit in memory, and NumPy operates on single-machine CPUs only. For very large datasets, consider Spark MLlib or distributed alternatives.
Sourcescikit-learn: Is scikit-learn free to use commercially?
Yes. Scikit-learn is open source under the BSD license, which allows free commercial use, modification, and distribution.
Sourcescikit-learn: What neural network capabilities does scikit-learn have?
Scikit-learn includes only a basic multilayer perceptron (MLPClassifier and MLPRegressor) for simple feedforward networks. For serious deep learning, use PyTorch, TensorFlow, or Keras instead.
Sourcescikit-learn: Does scikit-learn include natural language processing?
Scikit-learn has minimal NLP support limited to basic text feature extraction and vectorization. For comprehensive text processing, use spaCy or NLTK instead.
Sourcescikit-learn: When was scikit-learn first released?
Scikit-learn's first public release was February 1, 2010, following its start as a Google Summer of Code project in 2007.
SourceRelated pages
More on scikit-learn
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