Compare Python acceleration paths

Choose the least disruptive way to make Python faster

Epochly helps teams speed up AI inference and Python workloads without starting with a new language, a new runtime, or a distributed rewrite. Compare it with the tools people usually reach for next.

Detailed comparison pages are now live for Numba, Cython, PyPy, Mojo, Ray, and Dask. Use the hub table below to compare the full set in one scan.

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Fastest first step

Keep Python, keep CPython, keep shipping

Zero code changes

One pip install, no decorators, no rewrites, no new build steps

Seven tools compared

Deep dives for Numba, Cython, Pypy, Mojo, Ray, Dask

Why teams start here

Speed, rollback, and observability in one decision surface

Live deep dives

Start with the comparisons you can read end-to-end today

Every alternative named on this hub now has a live deep dive. Start with the page that matches your bottleneck, then use the matrix below to compare the full set side by side.

Detailed page live now

Epochly vs Numba

Best when you want more than numerical-loop JIT and do not want to decorate or reshape every hot path by hand.

What you get: JIT compilation for numerical loops

What you give up: Works only on numerical subset. Decorators on every function. Cold-start penalty per function. Object mode fallback can be slower than plain Python.

Epochly advantage: Works on any Python code. No decorators. No cold start.

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Epochly vs Cython

Best when you need faster Python without moving work into .pyx files, a C build step, or a rewrite project.

What you get: Near-C performance for rewritten code

What you give up: Full rewrite in .pyx files. C compiler toolchain required. Cryptic build errors. Days of learning curve.

Epochly advantage: Zero code changes. No build step. No new syntax.

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Epochly vs PyPy

Best when you want to stay on CPython and keep package compatibility instead of switching runtimes.

What you get: General Python speedup (~3x average)

What you give up: C extension incompatibility (PyTorch, many packages). Stuck on Python 3.10/3.11. Must replace your interpreter entirely.

Epochly advantage: CPython native. Works with every package. Latest Python supported.

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Epochly vs Mojo

Best when you want more speed from Python today without adopting a new language for performance-critical code.

What you get: A fresh language/runtime path for low-level performance work

What you give up: A language migration, new tooling, and a longer adoption curve before gains land in production.

Epochly advantage: Keeps the current Python stack intact while you test performance improvements first.

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Epochly vs Ray

Best when your first problem is slow Python work inside each service or worker, not cluster orchestration.

What you get: Cluster scheduling, actors, and scale-out coordination

What you give up: More infrastructure, more APIs, and more operational surface area than a single-machine optimization pass.

Epochly advantage: Improves the work inside each process before you add orchestration overhead.

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Epochly vs Dask

Best when you want to reduce Python overhead before adding distributed dataframe or array infrastructure.

What you get: A partitioned execution model for dataframes, arrays, and ETL stages

What you give up: A larger platform shift when the problem may still be Python overhead inside the existing workflow.

Epochly advantage: Lets teams try the smaller runtime decision before adopting distributed data infrastructure.

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Feature matrix

Compare the trade-offs in one scan

Focus on the questions technical buyers actually ask first: how much code changes, how much operational risk, and whether the tool fits the stack already in production.

Getting Started

Epochly is the only tool that requires zero changes to your existing Python code.

Decision factor	Epochly	Numba	Cython	PyPy	Mojo	Ray	Dask
Install method	pip	pip	pip + C compiler	Binary download	Modular SDK	pip	pip
Code changes needed	None	Decorators + types	Full .pyx rewrite	None	New language	New APIs	New APIs
Time to first result	Minutes	Hours	Days	Minutes	Weeks	Hours	Hours
Works with existing code

Safety & Risk

Epochly is the only tool with a progressive safety model. Every other tool is all-or-nothing.

Decision factor	Epochly	Numba	Cython	PyPy	Mojo	Ray	Dask
Progressive enhancement levels	5 levels (L0–L4)	—	—	—	—	—	—
Automatic anomaly detection
Automatic fallback to safe execution
Runtime disable without code changes
Risk rating per optimization
Zero overhead when disabled			—	—	—

Production Operations

Epochly is the only single-machine performance tool with enterprise-grade observability.

Decision factor	Epochly	Numba	Cython	PyPy	Mojo	Ray	Dask
Built-in observability
Fleet-wide telemetry
Centralized policy control
Audit-ready telemetry

Compatibility

Epochly works everywhere CPython runs, with no compatibility surprises.

Decision factor	Epochly	Numba	Cython	PyPy	Mojo	Ray	Dask
Python version support	3.9+	3.9–3.12	Wide	3.10–3.11	—	3.9–3.12	3.9–3.13
CPython compatible
Works with all packages
Jupyter support
Docker-friendly deployment

Performance

Epochly is the only tool that delivers up to 193x speedup without touching a single line of code.

Decision factor	Epochly	Numba	Cython	PyPy	Mojo	Ray	Dask
Speedup without code changes	Up to 193x	—	—	~3x	—	—	—
Multi-technique (JIT + GPU + parallel)
GPU acceleration
Automatic optimization
Graceful on unsuitable code			—		—

Epochly is the fastest path to faster Python. For specialized needs, combine it with Numba (numerical hot loops), Cython (maximum single-function speed), or Ray/Dask (distributed workloads). Epochly optimizes everything else and adds the safety net and observability that no other tool provides.

Decision guide

Need faster Python? Start here.

Answer these questions in order. The goal is to choose the smallest safe move first, not the most ambitious architecture on day one.

Can you rewrite your code?

Use Epochly— Zero changes required

Yes, numerical code

Consider Numba— But watch for object mode slowdowns

Yes, need max single-function speed

Consider Cython— Significant learning curve

Yes, starting fresh

Evaluate Mojo— New language, nascent ecosystem

Do you need distributed computing?

Yes, across machines

Use Ray or Dask— Add Epochly on each node

No, single machine

Use Epochly— No cluster infrastructure needed

Do you need production safety features?

Yes

Use Epochly— Only option with progressive enhancement + rollback

No, scripts/notebooks only

Epochly, Numba, or PyPy— All viable for non-production use

Do you need fleet observability?

Yes

Use Epochly— Only option with built-in telemetry

Any tool works— Pick based on other requirements

Use Epochly when...

You need faster Python today, not in 6 months
Your codebase is large and refactoring is not feasible
You need fleet-wide visibility into performance
Safety, rollback, and observability matter in production

Works alongside your stack

Already using Numba? Epochly optimizes everything else
Planning a Mojo migration? Get gains today while you transition
Using Ray/Dask? Epochly speeds up each worker node
Have Cython extensions? Epochly handles the pure Python

Next step

Want the fast path first?

Start free and see whether Epochly helps on your workload. If you are deciding for a team, compare pricing and benchmark methodology before you widen the system.

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