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QuestDB vs scikit-learn

QuestDB logo

QuestDB

Databases

Fast open source time-series database for high throughput ingestion

From
Free
Rated
-
Full reviewWebsite
scikit-learn logo

scikit-learn

Machine Learning

Machine learning in Python

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: QuestDB open-source edition lacks high-availability, distributed architecture, and enterprise security features; scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays
  • They diverge on capability: QuestDB covers High Throughput Ingestion, scikit-learn covers Classification algorithms.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which QuestDB and scikit-learn actually diverge.

Attributes where QuestDB and scikit-learn differ
AttributeQuestDBscikit-learn
Pricing modelopen-sourceUnknown
PlatformsDocker, Kubernetes, Cloud (AWS, Azure, GCP)Python, Linux, macOS, Windows
CategoryDatabasesMachine Learning
Founded20142007

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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 QuestDB

  • High Throughput Ingestion
  • SQL Support
  • Time-series Optimization
  • SIMD Vectorization
  • Column-oriented Storage
  • Built-in Web Console
  • InfluxDB Line Protocol
  • PostgreSQL

Only in scikit-learn

  • Classification algorithms
  • Regression models
  • Clustering methods
  • Dimensionality reduction
  • Model selection
  • NumPy
  • SciPy
  • Matplotlib

Both cover

  • Pandas
  • Linux support
  • Windows support
  • Mac support

What people use each for

The jobs each tool is most often brought in to do.

QuestDB

  • Time-series analytics ingesting up to 20M rows/second from IoT sensors or financial data feedsnot scikit-learn
  • Real-time dashboarding with 32ms time-to-first-row latency for minute-level analyticsnot scikit-learn
  • Applications requiring multi-tier storage (hot ingest, real-time SQL, cold Parquet archive)not scikit-learn

scikit-learn

  • Machine learningnot QuestDB
  • Data analysisnot QuestDB
  • Model trainingnot QuestDB
  • Predictive analyticsnot QuestDB

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

QuestDB

  • Open-source edition lacks high-availability, distributed architecture, and enterprise security features
  • Enterprise edition pricing not published; requires contacting sales for custom quote
  • Ingestion limit of 20M rows/sec platform-dependent; may not scale to extreme throughput requirements

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

Pricing, plan by plan

QuestDB

Free

No published plan breakdown. See the QuestDB review.

scikit-learn

Free

No published plan breakdown. See the scikit-learn review.

Which should you pick?

Choose QuestDB if

  • You need high throughput ingestion.
  • You want to start without paying.
  • You work on Docker, Kubernetes, Cloud (AWS, Azure, GCP).
  • You also want sql support.
QuestDB review

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.
scikit-learn review

Questions people ask

Is QuestDB or scikit-learn better?
Neither clearly leads. QuestDB starts at Free and scikit-learn at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, QuestDB or scikit-learn?
QuestDB starts at Free and scikit-learn at Free.
Does QuestDB or scikit-learn run on more platforms?
QuestDB runs on Docker, Kubernetes, Cloud (AWS, Azure, GCP). scikit-learn runs on Python, Linux, macOS, Windows.
Can I use QuestDB for free?
Both have a free tier, so you can try either at no cost before committing.
What is QuestDB best used for?
QuestDB is most often used for time-series analytics ingesting up to 20m rows/second from iot sensors or financial data feeds, real-time dashboarding with 32ms time-to-first-row latency for minute-level analytics, applications requiring multi-tier storage (hot ingest, real-time sql, cold parquet archive). Of those, time-series analytics ingesting up to 20m rows/second from iot sensors or financial data feeds and real-time dashboarding with 32ms time-to-first-row latency for minute-level analytics are not what scikit-learn is typically brought in for.
What can QuestDB do that scikit-learn cannot?
QuestDB covers High Throughput Ingestion, SQL Support, Time-series Optimization, SIMD Vectorization. scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction. Both handle Pandas, Linux support, Windows support, Mac support.

Answered from the vendors’ own pages

QuestDB: How much does QuestDB Enterprise cost?

QuestDB does not publish specific pricing for the Enterprise tier. Customers must contact QuestDB via their enterprise contact form to receive a custom quote.

Source
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.

Source
QuestDB: Does QuestDB offer a free version?

Yes, QuestDB Open Source is completely free and recommended for evaluation, prototyping, and pilot projects. Enterprise features, high availability, security, and dedicated support require the paid Enterprise tier.

Source
scikit-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.

Source
QuestDB: What deployment options does QuestDB offer?

QuestDB offers open source deployment, Enterprise deployment, and Bring Your Own Cloud (BYOC) deployment. Pricing details for BYOC and Enterprise tiers are not published and require direct contact with sales.

Source
scikit-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.

Source
scikit-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.

Source
scikit-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.

Source
scikit-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.

Source
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