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Software Development · head to head

Amp vs scikit-learn

A

Amp

Software Development

Amp is the frontier agent

From
$20/monthly
Rated
-
Full reviewWebsite
scikit-learn logo

scikit-learn

Machine Learning

Machine learning in Python

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only scikit-learn has a free tier, so it costs nothing to try first.
  • Each has a real cost: Amp minimum $20/month subscription cost with limited orb hours (750); scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays

Where they differ

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

Attributes where Amp and scikit-learn differ
AttributeAmpscikit-learn
Starting price$20/monthlyFree
Pricing modelsubscriptionUnknown
Free tierNoYes
PlatformsWebPython, Linux, macOS, Windows
CategorySoftware DevelopmentMachine Learning
FoundedUnknown2007

Identical on both: 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 Amp

Nothing recorded that scikit-learn does not also cover.

Only in scikit-learn

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

What people use each for

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

Amp

No use cases recorded yet. See the Amp review.

scikit-learn

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

Where each one falls short

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

Amp

  • Minimum $20/month subscription cost with limited orb hours (750)
  • High-tier Gigawatt plan at $200/month required for ultra mode agents
  • Usage limits apply after included quotas are consumed each month
  • Each user limited to maximum of 2 simultaneous subscriptions

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

Amp

$20/monthly
  • Megawatt$20/monthly
    • All product features
    • 750 hours of orbs
    • Use your ChatGPT subscription
  • Gigawatt$200/monthly
    • Everything in Megawatt plus: 1
    • 000 hours of xxlarge orbs
    • $200 included agent usage
  • Education Discount$10/monthly
    • For students and teachers: full access at $10/month (50% off Megawatt)
  • Unconstrained$undefined/usage
    • Pay for what you use at standard rates
    • all agent modes
    • API pricing for model tokens and orbs

scikit-learn

Free

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

Which should you pick?

Choose Amp if

Nothing in the data separates Amp from scikit-learn on the points above - pick on price and on how each one feels to use.

Amp 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 Amp or scikit-learn better?
Neither clearly leads. Amp starts at $20/monthly and scikit-learn at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Amp or scikit-learn?
scikit-learn has a free tier; the other does not. Paid plans start at $20/monthly for Amp and Free for scikit-learn.
Does Amp or scikit-learn run on more platforms?
Amp runs on Web. scikit-learn runs on Python, Linux, macOS, Windows.
Can I use scikit-learn for free?
Yes. scikit-learn has a free tier, so you can try it without paying. Amp starts at $20/monthly.
What can Amp do that scikit-learn cannot?
scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction.

Answered from the vendors’ own pages

Amp: How much does Amp cost per month?

Amp offers two main monthly plans: Megawatt at $20/month (750 hours of orbs) and Gigawatt at $200/month (1,000 hours of xxlarge orbs). Students and teachers get a 50% education discount ($10/month). Source: https://ampcode.com/pricing

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
Amp: What usage is included in Amp subscriptions?

At minimum, Amp subscriptions include agent usage equal to the subscription cost per month. Depending on usage patterns, you may receive additional usage beyond the base guarantee. Megawatt includes 750 orb hours and Gigawatt includes 1,000 hours of xxlarge orbs.

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
Amp: Can I use my own ChatGPT subscription with Amp?

Yes, Amp subscribers who link a ChatGPT subscription pay no per-token fees and can use as many tokens as their third-party subscription allows, making it ideal for users who already have ChatGPT access.

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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