Machine learning frameworks are the libraries that fit, train and ship models, from boosted trees to neural networks.
Most are downloaded, not bought, so this guide ranks on who maintains the code, what it licenses under, where trained models can run and what hosted extras cost, with community-run projects ahead of vendor-backed ones.
Vendors can pay for more visibility on this website, for example a higher place in a ranking or more room on a page. Commercial relationships can also affect which products we feature and how prominently. Every vendor link is marked the same, whatever the arrangement behind it. How that works.
In short
What machine learning frameworks software does
A machine learning framework is a code library that trains predictive models on your data, runs them on CPUs, GPUs or devices, and saves them for later use.
To rank machine learning frameworks tools we asked, in turn, how much work setup takes, what it truly costs, whether data can be taken back out, whether leaving is possible, and who the product suits. The ranking then gives places 1 to 3 to challengers outside the best-known brands, if the category has such vendors, and the incumbents follow. Reasons for the choice of tests, and for skipping a score out of ten, can be read on the how we work page.
Classical machine learning library maintained by a community, not a company
Ranked #1 of 12 in Best Machine Learning Frameworks in 2026.
Pricing on requestElsewhere
The project is described as community-developed, with financial support from Probabl and other institutions and grants rather than from one owner. That structure puts it first in this ranking, because nobody can change terms on you.
The footer reads BSD License, though the variant is not named. The history is documented: Google Summer of Code in 2007, then INRIA leading the first public release in 2010. The cost is support, which is the FAQ only.
What stands out
Community-run
BSD licence
Started in 2007
Where it costs you
The about page names no features, so task fit has to be confirmed in the documentation.
Support is the FAQ; the page tells users not to email the authors.
Right for
Teams that want a community-governed library with no commercial counterparty
Wrong for
Buyers who need a vendor to call for support
Not stated by the vendorNo fee or plan appears on the pages read; support questions are routed to the FAQ
Gradient boosting library for regression, classification and ranking on tables
Ranked #2 of 12 in Best Machine Learning Frameworks in 2026.
Pricing on requestElsewhere
The footer credits XGBoost Contributors, and the repository is under the dmlc GitHub organisation. Bindings are listed for C++, Python, R, Java, Scala and Julia, which suits mixed-language shops.
Training can be distributed over several machines and connects to Spark and Dask. It runs on Windows, Linux and macOS and on major clouds. What it leaves out is any commercial wrapper: no vendor, no price page and no stated route to paid help.
What stands out
Gradient boosting
Spark and Dask
Six language bindings
Where it costs you
No company, support desk or service terms are named on the site.
Scope is table-style prediction tasks; the site lists nothing for image or text models.
Right for
Teams ranking or classifying tabular records, with Spark or Dask already in place
Wrong for
Teams wanting a hosted service or contractual support
Not stated by the vendorNo pricing or plans shown on the page read; the code is licensed Apache-2.0
Gradient boosting framework aimed at faster training and lower memory use
Ranked #3 of 12 in Best Machine Learning Frameworks in 2026.
Pricing on requestElsewhere
Speed and memory are the stated selling points, along with parallel, distributed and GPU learning. Interfaces are documented for Python, R and C.
The ownership story needs care: a March 2026 README note records the project leaving the microsoft GitHub account for lightgbm-org, which means bookmarks, pinned URLs and old tutorials may still show the earlier home. The licence is MIT. Nothing on the docs page describes paid support.
What stands out
Gradient boosting
GPU learning
Python, R and C APIs
Where it costs you
The repository moved in March 2026, so older links and scripts may point to the former owner.
No company, support desk or country is named on the docs page.
Right for
Teams tuning boosted models where training time and memory are the limit
Wrong for
Anyone who needs a named vendor accountable for the code
Not stated by the vendorNo pricing on the docs page read; the code is licensed under MIT
Deep learning framework with local install and cloud launch options
Ranked #4 of 12 in Best Machine Learning Frameworks in 2026.
Pricing on requestElsewhere
Governance is the notable fact: the PyTorch Foundation owns the framework and calls itself vendor-neutral, hosted at the Linux Foundation. Production tools named on the site are TorchServe for serving and torch.distributed for distributed training, with TorchScript covering eager and graph modes.
Featured projects include Captum for interpretability and skorch for scikit-learn compatibility. Installation is yours to manage, across stable and nightly builds, and the site gives no figure for what training will cost.
What stands out
Eager and graph modes
TorchServe
Foundation-hosted
Where it costs you
No pricing or service terms are shown; the real cost is the compute you rent or own.
The site names no country and no support offer.
Right for
Teams building custom neural networks who want neutral governance and many cloud routes
Wrong for
Teams that want one price and a support contract
Not stated by the vendorNo prices on the pages read; downloaded and run on your own or cloud hardware
Python library for composable transformations of NumPy-style programs
Ranked #5 of 12 in Best Machine Learning Frameworks in 2026.
Pricing on requestElsewhere
The docs describe composable transformations: compile with jit, differentiate with grad, batch with vmap, and spread work with shard_map. Code is written in NumPy style and runs on GPU and TPU hardware.
Because it is a library and not a service, you provide the hardware and the operations. Layers are not part of the pitch, which is why the Flax library below it exists. No company is named as maintainer.
What stands out
jit, grad and vmap
GPU and TPU
Apache-2.0
Where it costs you
It is a library of transformations; neural network layers need a separate package such as Flax.
The docs give no maintainer company, country or support offer.
Right for
Researchers who want to write array maths and transform it with compile and grad
Wrong for
Teams that want ready-made layers and training loops out of the box
Not stated by the vendorNo cost information on the docs pages read; code licensed Apache-2.0, no hosted service
Neural network library and ecosystem built on top of JAX
Ranked #6 of 12 in Best Machine Learning Frameworks in 2026.
Pricing on requestElsewhere
Layers are the gap Flax fills for JAX users. The docs present NNX as the new API, with Python reference semantics, state filters for fine-grained control of model state, and a Functional API for JAX integration.
Linen stays supported for the near future, which keeps older projects working but leaves a newcomer choosing between two styles. The repository sits under the google organisation, though the docs name no maintainer.
What stands out
JAX ecosystem
NNX API
Linen kept
Where it costs you
Two APIs coexist, NNX and Linen, so tutorials and code examples may use different styles.
It only works with JAX, and the docs show no support offer.
Right for
JAX users who want layers and model state without writing them by hand
Wrong for
Teams that are not already committed to JAX
Not stated by the vendorNo pricing on the docs page read; code licensed Apache-2.0
Common model file format and operator set for moving between frameworks
Ranked #7 of 12 in Best Machine Learning Frameworks in 2026.
Pricing on requestElsewhere
Think of ONNX as the exit door of this category. It supplies a shared operator set and file format, and the site links lists of supported frameworks and accelerators, so a model trained in one place can move to another.
Governance is a community project, with the site describing graduate status in LF AI. It trains nothing itself, and the page read states neither company nor price.
What stands out
Interchange format
LF AI graduate
Open governance
Where it costs you
It is a file format and operator set, so you still need another framework to train a model.
The page read names no company, country or support route.
Right for
Teams that train in one framework and must run the model in another
Wrong for
Teams looking for a framework to train models in
Not stated by the vendorNo pricing on the page read; code licensed Apache-2.0
Training wrapper for PyTorch with a cloud option or your own hardware
Ranked #8 of 12 in Best Machine Learning Frameworks in 2026.
Free tierPublished pricingElsewhere
Unlike most entries here, this one publishes rates: a zero-cost plan with credits and limits, then pay-as-you-go, with example CPU rates listed on the pricing page.
The software side is Apache-2.0, with Fabric for custom training loops and a Trainer built on Fabric primitives. You may run on Lightning's cloud or on your own hardware. Those starter caps, a pair of seats and a pair of GPUs, decide when you outgrow the plan.
What stands out
Fabric training loops
Cloud or own hardware
Published rates
Where it costs you
The free plan is limited to 2 seats, 2 concurrent GPUs and 50GB of storage.
It builds on PyTorch, and the site names no country for the company.
Right for
PyTorch users who want a priced cloud option next to the library
Wrong for
Teams who need more than two seats on the starter plan
Not stated by the vendorFree plan at zero cost with credits, then pay-as-you-go published on the pricing page
Model library and hub for pretrained models, hosted inference and datasets
Ranked #9 of 12 in Best Machine Learning Frameworks in 2026.
Published pricingElsewhere
Costs are stated here in more detail than elsewhere in the category: a per-user monthly rate for Team and Enterprise, an hourly rate for GPU compute, and Inference Providers described as having no service fees across more than 45,000 models.
Transformers itself carries an Apache-2.0 licence and targets PyTorch. Spaces, Inference Endpoints and Datasets sit in the hub. A PRO plan is linked but unpriced, and no company location appears.
What stands out
Pretrained models
Inference Endpoints
Published seat prices
Where it costs you
The PRO plan is linked without a price, so one tier cannot be costed from the page.
The page names no country, and GPU compute is billed by the hour on top of seats.
Right for
Teams that want pretrained models plus a paid hub for sharing and serving
Wrong for
Buyers who need every plan priced on one page
Not stated by the vendorTeam and Enterprise plans and GPU compute rates published; unlimited public hosting stated
Deep learning framework with mobile, browser and production pipeline tooling
Ranked #10 of 12 in Best Machine Learning Frameworks in 2026.
Pricing on requestElsewhere
Deployment range is the argument for TensorFlow. Models can reach Android, iOS, Raspberry Pi and Edge TPU through LiteRT, run inside Node.js or a browser via TensorFlow.js, and flow through TFX pipelines.
Around the core sit tf.keras, tf.data, TensorBoard and TensorFlow Datasets. The Apache-2.0 repository costs nothing to fetch. The home page does not state who owns the project or where, so accountability is unclear.
What stands out
LiteRT on devices
TensorFlow.js
TFX pipelines
Where it costs you
The page read names no owner and no country, only the TensorFlow team.
Device and browser targets add separate runtimes to learn and test.
Right for
Teams shipping models to phones, edge boards or browsers as well as servers
Wrong for
Teams who want a named owner or support contract
Not stated by the vendorNo prices or paid plan on the home page read; code licensed Apache-2.0
Deep learning teaching software and courses built around PyTorch
Ranked #11 of 12 in Best Machine Learning Frameworks in 2026.
Pricing on requestElsewhere
The homepage ties software to teaching: fastai for PyTorch, nbdev, a course and a book on practical deep learning. It also announces that fast.ai is joining Answer.AI, linking a post from November 2024, so the organisation behind it has changed.
The Solveit platform is described in general terms only. No price, plan, licence or location is shown there, which limits how far a buyer can check it.
What stands out
fastai for PyTorch
nbdev
Courses and book
Where it costs you
The homepage shows no prices, plans or licence terms for the software.
Ownership is in transition, since the site says fast.ai is joining Answer.AI.
Right for
Learners who want a PyTorch-based library tied to a deep learning course
Wrong for
Teams needing contractual terms or a production service
Not stated by the vendorNo prices or plans on the homepage read
Minimal MIT-licensed deep learning stack sold with tinybox GPU hardware
Ranked #12 of 12 in Best Machine Learning Frameworks in 2026.
Pricing on requestElsewhere
The README stresses that anyone can inspect and change the compiler and IR, and names TinyJit for function-level compilation. The code is MIT.
Alongside it, tiny corp sells tinybox machines, configurable up to 4 GPUs and a 32-core EPYC CPU, shipping in 1 to 8 weeks. Payment is by bank transfer, and buyers outside the US pay import duties and VAT. No company address appears on the site.
What stands out
MIT licence
Compiler visible
tinybox hardware
Where it costs you
Hardware orders are made to order, paid by wire only, with a 20% restocking fee on returns.
The site gives no company address, and the tinybox price was not readable.
Right for
Developers who want to read and modify a small compiler-based framework
Wrong for
Teams that need a mature ecosystem or a stated vendor country
Not stated by the vendorSoftware has no fee on the pages read; tinybox is sold made to order via a configurator
How to choose machine learning frameworks software
A machine learning framework is a code library that trains predictive models on your data, runs them on CPUs, GPUs or devices, and saves them for later use. Compare on the following points, in this order, before you compare features.
01
Work out how much a sales call is worth to you
Prices are public at 2 products (PyTorch Lightning, Hugging Face Transformers) and on request at the remaining 10, so budget time for quotes if your favourite is in that second group.
02
Look at where the vendor sits
Headquarters range over 1 countries and 1 regions: Elsewhere 12. Legal exposure follows the vendor, not only the data centre, so read the contracting entity before you sign.
Choosing between scikit-learn, XGBoost and LightGBM for table data
Most business prediction work runs on rows and columns, and this is where the quietest libraries earn their place. scikit-learn is a community project supported by Probabl and other institutions, and XGBoost credits its own contributors in the site footer and is Apache-2.0. LightGBM adds parallel, distributed and GPU learning and carries an MIT licence.
None of the three sells a plan, so the decision is about fit and plumbing. XGBoost covers ranking among its objectives and spreads training over machines through Spark and Dask. LightGBM advertises lower memory use. Run the same dataset through two of them before committing, and note training time, memory and how easily a model is saved and loaded by the tools around it.
Time two libraries on one real dataset and record memory use
Check the language bindings against the languages your team writes
Confirm whether your Spark or Dask cluster is supported
Deployment targets that separate PyTorch, TensorFlow and JAX
The three deep learning options differ more in where models end up than in how they train. TensorFlow points to LiteRT on handsets and boards, TensorFlow.js in the browser, and TFX for pipelines. PyTorch names TorchServe for production and torch.distributed for distributed training, and offers launch paths on several clouds.
JAX is a library of transformations, with jit, grad and vmap, run on GPU and TPU hardware, with Flax supplies the layers on top. A phone or browser target points to TensorFlow, while research code that must be transformed freely suits JAX. List every place your model must run before you pick one, including the device that will run it last.
List every device or browser the trained model must run on
Check how the serving tool handles your expected request volume
Decide whether your team wants layers supplied or written by hand
What sits on top: PyTorch Lightning, Hugging Face Transformers and fast.ai
Wrappers and hubs change the bill more than the frameworks beneath them. PyTorch Lightning publishes a starter plan with credits and small seat and GPU caps, then pay-as-you-go, and its code runs on your own hardware too. Hugging Face lists Team and Enterprise seats per user per month and GPU compute by the hour, while its Transformers library is Apache-2.0.
fast.ai pairs a library with courses and has announced it is joining Answer.AI. Read these as separate decisions: the library is cheap to adopt, while hosted inference, extra seats and GPU hours are where costs appear. Estimate your monthly GPU hours before choosing a hosted route, and test the library on local hardware first.
Estimate monthly GPU hours before choosing a hosted route
Count the seats you will need against starter plan limits
Test the library on local hardware before paying for hosting
Interchange formats, licences and who maintains the code
Moving a model out is a purchase criterion here, even though nothing is bought. ONNX offers a common operator set and file format for passing models between frameworks, under open governance as an LF AI graduate project. Licences are readable on the repositories: Apache-2.0 for most, MIT for LightGBM and tinygrad, BSD for scikit-learn. Ownership moves too.
PyTorch belongs to the PyTorch Foundation, and LightGBM changed its repository home in March 2026. tinygrad is run by tiny corp, which also sells hardware. Pin versions, record the licence in your inventory and export a trained model once to prove the route works before you need it, because an untested exit tends to fail on the day a migration is due.
Export one trained model to ONNX and load it elsewhere
Record each library's licence and repository owner in your inventory
Pin versions and watch for repository moves
What goes wrong most often when buying machine learning frameworks software
Choosing a deep learning framework for a table of customer records when a boosted tree library would train faster.
Costing hosted inference by seats alone and forgetting that GPU hours are billed on top.
Following an older tutorial to a repository address that has since moved to a new organisation.
Assuming a trained model can leave a framework without testing an export to a neutral format.
07
Frequently asked questions
6 answers
What is the best machine learning frameworks in 2026?
scikit-learn takes first place among the 12 we reviewed. scikit-learn describes itself as a community project developed across the world, backed by Probabl, other institutions and private grants.
It began in 2007 as a Google Summer of Code project, and its first public release came in February 2010. The about page shows no price and names no company. It also asks users not to email the authors, so help comes from the FAQ, not a vendor desk.
Which machine learning frameworks tools publish their pricing?
2 out of 12 state a price model on their own site:
PyTorch Lightning: Free plan at zero cost with credits, then pay-as-you-go published on the pricing page.
Hugging Face Transformers: Team and Enterprise plans and GPU compute rates published; unlimited public hosting stated.
Everything else on the list is priced on request.
Is there a free machine learning frameworks tool?
PyTorch Lightning have a free plan or free self-hosted edition.
Where are these machine learning frameworks vendors established?
Across 1 countries (Elsewhere 12):
scikit-learn: Not stated by the vendor.
XGBoost: Not stated by the vendor.
LightGBM: Not stated by the vendor.
PyTorch: Not stated by the vendor.
JAX: Not stated by the vendor.
Flax: Not stated by the vendor.
ONNX: Not stated by the vendor.
PyTorch Lightning: Not stated by the vendor.
Hugging Face Transformers: Not stated by the vendor.
TensorFlow: Not stated by the vendor.
fast.ai: Not stated by the vendor.
tinygrad: Not stated by the vendor.
What should you use instead of scikit-learn?
Second is XGBoost, which suits teams ranking or classifying tabular records, with Spark or Dask already in place. Third is LightGBM, which suits teams tuning boosted models where training time and memory are the limit.
Who should not buy scikit-learn?
Buyers who need a vendor to call for support. The about page names no features, so task fit has to be confirmed in the documentation..
This list covers 12 machine learning frameworks products. A product we left out can be put to us by its maker, with a short note on its purpose and its customers; we decide on inclusion ourselves. The rules are under software advice.
