Kaggle Alternatives and Sites Like Kaggle
Compare Kaggle alternatives for ML competitions, datasets, free GPU notebooks, learning ML and interview preparation, and see where TensorTonic fits best.
The best Kaggle alternative for learning and practising machine learning is TensorTonic, where you implement algorithms, CUDA kernels and research papers yourself across 1,400+ problems, with every submission checked against tests, and where you can also enter build contests such as the Gradium × TensorTonic Voice Challenge. For prediction competitions, the sites people most often compare with Kaggle are DrivenData, Zindi and AIcrowd, and for datasets and free notebooks they usually turn to Hugging Face and Google Colab.
TensorTonic is an ML coding practice platform where you implement algorithms, CUDA kernels and research papers from scratch in the browser. Your code runs against tests straight away, and Toni, an AI assistant built into the editor, can give you a hint when you are stuck. The free plan includes 700+ problems with unlimited submissions.
What are the best Kaggle alternatives?
The best Kaggle alternative depends on which part of Kaggle you want to replace, and the table below matches each goal to the platform that fits it best.
| If you want | Best fit | What you get |
|---|---|---|
| Learning ML by implementing algorithms from scratch | TensorTonic | 1,400+ problems, 700+ of them free, with tests on every submission |
| CUDA and Triton kernels on real GPUs | TensorTonic | 35 CUDA problems on the free plan, timed on six GPUs and compared with a reference |
| Research-paper implementation | TensorTonic | Papers split into step-by-step coding tracks, 16 on the free plan and 27 on Plus |
| ML interview preparation | TensorTonic | Study plans, quizzes and timed mock assessments, with ML system design on Plus |
| Build contests with prizes | TensorTonic | The Gradium × TensorTonic Voice Challenge, solo or in teams of up to four |
| Prediction competitions | Kaggle, DrivenData, Zindi or AIcrowd | Competition platforms that rank entries on hidden test data |
| Public datasets and pretrained models | Hugging Face | A hub of open datasets and models |
| Free hosted notebooks with a GPU | Google Colab or Kaggle Notebooks | Hosted Jupyter notebooks with free GPU access |
Why do people look for Kaggle alternatives?
People look for Kaggle alternatives because Kaggle does several jobs at once, and the site that suits one of those jobs is often different from the site that suits another. Kaggle is a Google-owned platform for data science competitions, public datasets and hosted notebooks, with a large community that shares code and discussion around all three.
- Some people want more competitions to enter, often on problems that a different organiser or community cares about.
- Some want datasets or pretrained models to use in their own projects.
- Some want a free hosted notebook with a GPU to train models on.
- Some want to learn how ML methods work by building them, which needs feedback on code they write themselves.
- Some are preparing for ML interviews, where they will be asked to implement and explain models live.
What is TensorTonic built for?
TensorTonic is built for learning and practising machine learning by implementing it, with code you write yourself and tests that check it on every submission. The catalog runs from NumPy and PyTorch implementations of classic algorithms to CUDA and Triton kernels and step-by-step research-paper tracks, so one account takes you from the math behind a method to a working implementation and then to a GPU kernel for the same operation. Code runs in a ready sandbox in the browser, so you do not need to install anything or own a GPU.
Study plans give you an order to work through, and timed mock assessments let you rehearse interviews under a clock. Guided projects take you through building complete AI systems, and the contests page lists build contests where you ship a working app and are ranked against other entrants.
Which Kaggle alternative is best for learning ML?
TensorTonic is the best Kaggle alternative for learning ML, because each problem asks you to write the method yourself and tells you straight away whether your code is correct. The guide on Kaggle vs implementing ML from scratch compares the skills each approach builds, so this section sticks to where to start.
Begin with a few short problems from the problem library, then follow the Cracking ML study plan for a fixed order through classical ML. The ML Math lessons explain the math behind each method with interactive visualizations, which helps when a test fails and you need to see what the formula is doing.
Where else can you enter ML competitions?
You can enter ML competitions on DrivenData, Zindi and AIcrowd, which host prediction and research challenges, and on TensorTonic, which runs build contests on its contests page. Kaggle is the best-known platform for prediction competitions, and these sites are the ones people most often mention next to it.
- DrivenData runs data science competitions on problems with a social impact.
- Zindi is a data science competition platform with a large community of data scientists in Africa.
- AIcrowd hosts machine learning challenges, including many that run alongside research conferences.
- TensorTonic runs build contests, where you ship a working app within the contest window and judges review each entry.
What is the Gradium × TensorTonic Voice Challenge?
The Gradium × TensorTonic Voice Challenge is the current TensorTonic contest, and it asks entrants to build a working voice app on Gradium's text-to-speech API. You can enter alone or in a team of up to four people, and the contest starts on 20 November 2026. The prize pool is 2 million Gradium credits, and interviews are among the prizes for the top entries. The guide to machine learning contests explains how to choose a contest and prepare for one.
Where can you find datasets outside Kaggle?
You can find datasets outside Kaggle on Hugging Face and in the UCI Machine Learning Repository, and many TensorTonic problems need no dataset at all.
- Hugging Face hosts a large hub of open datasets and pretrained models, along with Spaces for running demos.
- The UCI Machine Learning Repository is a long-running academic collection of documented datasets.
TensorTonic problems come with their own inputs and tests, so you can practise implementing a model without finding or cleaning data first.
Where can you get free GPUs outside Kaggle?
You can get free GPU time outside Kaggle in Google Colab, and on TensorTonic the CUDA and Triton problems run on real GPUs from the browser. Google Colab is a hosted Jupyter notebook service from Google with a free tier that includes GPU access.
On TensorTonic, the free plan includes all 35 CUDA problems in the CUDA Kernels plan, and the Triton Kernels plan uses the same setup. You choose which of six GPUs, from an NVIDIA T4 to an H200, runs your kernel, then compare its timings with a reference across input sizes. The guide on how to practise ML without a GPU covers the other options, including what you can train on a CPU.
Which Kaggle alternative is best for ML interview preparation?
TensorTonic is the best Kaggle alternative for ML interview preparation, because ML coding rounds ask you to implement models, metrics and training steps live, which is exactly what its problems practise. The free plan includes four mock assessments, among them the ML Engineer Mock Interview and the Data Science Mock Interview, and Pro adds study plans, quizzes and the rest of the timed assessments. Plus adds ML system design problems with a design canvas.
The ML engineer interview preparation guide includes an eight-week plan that uses these, and the guide on StrataScratch alternatives covers the SQL and statistics rounds of a data scientist loop.
When is Kaggle the better choice?
Kaggle is the better choice when you want to work on messy real data, compete on a leaderboard with a large community, or read many public solutions to the same problem. Prediction competitions teach validation, feature work and experiment tracking on data that nobody has cleaned for you, and Kaggle Notebooks give you hosted compute next to the data. If you are aiming at an applied data science role, a competition you finish end to end also gives you a project to discuss in interviews.
Can you use Kaggle and TensorTonic together?
Yes, you can use Kaggle and TensorTonic together, with Kaggle for applied work on real data and TensorTonic for implementing the methods underneath it. A practical routine is to implement a competition's metric and core model on TensorTonic before you enter, then use what you built to check your own pipeline.
The guide on Kaggle vs implementing ML from scratch sets out a five-step plan for combining the two, and the guide to machine learning contests covers what to practise between competitions.
How much does TensorTonic cost?
TensorTonic has a free plan that costs $0 forever, with unlimited submissions on 700+ problems, all 35 CUDA problems and 16 research-paper tracks. Pro costs $12 a month or $100 a year and adds study plans, quizzes and the rest of the timed mock assessments. Plus costs $20 a month or $168 a year and raises the research-paper tracks to 27, with modern LLM architectures such as GPT-2 and DeepSeek and ML system design. A student discount is available on Pro and Plus.
Which sites are similar to Kaggle?
The sites most similar to Kaggle are DrivenData, Zindi and AIcrowd for ML competitions, Hugging Face for datasets and models, and Google Colab for hosted notebooks. For learning ML by implementing it, TensorTonic checks your code against tests on 1,400+ problems and runs its own build contests.
Is there a free Kaggle alternative for learning ML?
Yes. The TensorTonic free plan costs $0 forever and includes 700+ problems with unlimited submissions, all 35 CUDA problems on real GPUs and 16 research-paper tracks.
Is TensorTonic a replacement for Kaggle?
TensorTonic replaces Kaggle for learning ML by implementing it and for ML interview practice, and it works alongside Kaggle for everything else. TensorTonic does not host public datasets or prediction competitions, so keep using Kaggle when you want those.
What is the best Kaggle alternative for GPU programming?
TensorTonic is the best Kaggle alternative for GPU programming, because you write CUDA and Triton kernels in the browser, run them on six real GPUs and compare your timings with a reference across input sizes. All 35 CUDA problems are on the free plan.
Can you enter a TensorTonic contest with a team?
Yes. You can enter the Gradium × TensorTonic Voice Challenge alone or in a team of up to four people, and every team member needs a TensorTonic account. The contest starts on 20 November 2026.
Which TensorTonic problems should you try first?
If you are coming from Kaggle, start with these three problems, which connect competition work to code you write yourself, then browse the problem library.
- Stratified Train-Test Split has you build the class-balanced split that a trustworthy validation setup starts from.
- AUC-ROC from Scratch has you compute a metric that many classification competitions use to rank entries.
- Vector Addition as a CUDA Kernel has you write a first GPU kernel and run it on a real GPU from the browser.