Bottleneck vs Numba in 2026: Pricing, Setup and Features

Compare Bottleneck, Numba and PyOverdrive by purpose, setup, pricing and questions to ask before choosing.

By SayDeploy · 2026-10-26

SayDeploy is published by LunarWerx, which makes some of the tools compared here.

Bottleneck provides a collection of fast NumPy array functions written in C ^[1]. Numba translates a subset of Python and NumPy code into fast machine code as an open source JIT compiler ^[2]. PyOverdrive lets existing code use supported faster paths after calling pyoverdrive.enable(), while unsupported calls run on stock NumPy ^[3]. Bottleneck's sources say it covers fast NumPy array functions ^[1], Numba's sources say it covers translating Python and NumPy code into machine code ^[2], and PyOverdrive's sources say it covers supported NumPy functions through its enable call ^[3].

Option	Free to use	Paid plan	Install
Bottleneck	Price not published	Not published	Binary wheels for the most common platforms ^[1]
Numba	Price not published	Not published	Python 3.9 through 3.12 supported ^[2]
PyOverdrive	Free, open source under the MIT License ^[3]	Not published	Install with pip straight from the GitHub repository ^[3]

The short answer

On needs, Bottleneck's sources describe fast NumPy array functions ^[1]. Numba's sources describe translating Python functions to optimized machine code at runtime ^[2]. PyOverdrive's sources describe supported NumPy functions using faster paths with stock NumPy fallback ^[3].

What Bottleneck and Numba are built for

Bottleneck's own page says it is a collection of fast NumPy array functions written in C ^[1]. It says only arrays with data types int32, int64, float32 and float64 are accelerated ^[1]. Bottleneck is distributed under a Simplified BSD license ^[1].

Numba's own page says it is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code ^[2]. It says Numba translates Python functions to optimized machine code at runtime using the LLVM compiler library ^[2]. Numba is designed to be used with NumPy arrays and functions ^[2].

What PyOverdrive is built for

PyOverdrive is made by the team that publishes this site.

PyOverdrive patches supported NumPy functions after calling pyoverdrive.enable(), so existing code uses faster paths and unsupported calls use stock NumPy ^[3]. A fast path runs only when a check confirms the input is in its measured range, and other cases go to stock NumPy ^[3].

You can compare fast paths with stock NumPy on your own machine using python -m pyoverdrive --selfcheck ^[3]. You can also use pyoverdrive.explain() to see which path a call would take and why without running the call ^[3]. You can turn off one fast path with disable_path() or PYOVERDRIVE_DISABLE, or restore original NumPy functions with pyoverdrive.disable() ^[3].

Pricing and fees

Bottleneck is distributed under a Simplified BSD license ^[1], and the facts provided here do not state a purchase price. Numba is open source ^[2], and the facts provided here do not state a purchase price.

PyOverdrive is open source under the MIT License ^[3]. A paid price for PyOverdrive is not published here.

Setting it up and daily use

Bottleneck provides binary wheels for the most common platforms ^[1]. Its source install requires Python greater than 3.9 and NumPy 1.16.0 or newer ^[1]. Bottleneck comes with a benchmark suite ^[1].

Numba supports Python 3.9 through 3.12 ^[2]. Numba works with Jupyter notebooks for interactive computing and distributed execution frameworks like Dask and Spark ^[2].

PyOverdrive is installed with pip straight from the GitHub repository ^[3]. It needs Python 3.12 or newer ^[3]. The package is marked OS independent, and the test suite has been run on Windows and Linux x86-64 ^[3].

Things to check before you choose

Check whether you need existing NumPy functions, Python functions translated at runtime, or a tool that patches supported NumPy functions. Bottleneck, Numba and PyOverdrive describe these areas differently in their sources ^[1] ^[2] ^[3].

Check the data types, Python version, operating system and workflow you use. Bottleneck lists supported accelerated data types ^[1], Numba lists supported Python versions and CPUs ^[2] ^[2], and PyOverdrive lists its Python requirement and platforms ^[3] ^[3].

How to choose

If the need is a collection of fast NumPy array functions written in C, Bottleneck's sources describe that use case ^[1].

If the need is translating a subset of Python and NumPy code into fast machine code, Numba's sources describe that purpose ^[2].

If the need is to keep existing code using NumPy functions while enabling supported faster paths and stock NumPy fallback for unsupported calls, PyOverdrive's sources describe that behavior ^[3].

If the need is checking fast paths on a local machine, PyOverdrive's sources describe self-check and calibration commands ^[3] ^[3].

Frequently asked questions

Is Bottleneck or Numba better for NumPy work?

The right choice depends on the need being checked. Bottleneck's page describes a collection of fast NumPy array functions written in C ^[1]. Numba's page describes translating a subset of Python and NumPy code into fast machine code ^[2]. Review the specific functions and workflow before choosing.

Does Bottleneck support every NumPy array type?

Bottleneck's page says only arrays with data types int32, int64, float32 and float64 are accelerated ^[1]. The available facts do not describe other data type support details, so check the functions and data used by the application.

What does Numba do with Python and NumPy code?

Numba's page says it is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code ^[2]. It also says Numba translates Python functions to optimized machine code at runtime using the LLVM compiler library ^[2].

How does PyOverdrive change NumPy usage?

PyOverdrive's sources say calling pyoverdrive.enable() patches supported NumPy functions so existing code uses faster paths, while calls it does not support run on stock NumPy ^[3]. A fast path only runs after a check confirms the input is in its measured range ^[3].

How do you check PyOverdrive fast paths?

PyOverdrive's sources say python -m pyoverdrive --selfcheck compares every fast path with stock NumPy on your own machine ^[3]. The sources also say python -m pyoverdrive --calibrate re-times the threaded paths at their thresholds on the CPU and switches off any that do not pay there ^[3].

It is open source under the MIT License. ^[3]

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