Best Google Colab alternatives, ranked by what you actually get for free#

Colab was my starting point too. It is easy to open, and then it disconnects in the middle of a run and your files are gone. I started keeping a list of where else I could get a free GPU, and how each one actually behaves when you try to use it for more than a demo.

I rank these on three things: how many free GPU hours you get, how stable those hours are, and how fast you can go from signup to a running notebook.

How I rank them#

Tier list summary#

S tier, the ones I actually rely on#

A tier, very good with one catch#

B tier, decent but flawed#

C tier, not what I would pick#

D tier, skip it#

What each platform is actually like#

Google Colab (B tier)#

Colab never tells you exactly how much GPU time you have left. It depends on load. Sessions can run 12 hours on paper, but mine usually dropped after 2 to 3 hours. When it drops, your files go with it because there is no persistent storage. If you already have a Google account it is easy to start. I use it for quick experiments, not for anything I need to finish.

Paperspace Gradient (A tier)#

Disconnects about as often as Colab in my experience, and it never states a clear hour limit. The difference is persistent storage, so you do not re-upload your dataset every time. The free GPU is an M4000, which is weaker than a T4. Note that notebooks are public by default, so check the setting if your work is private. Better than Colab for one reason: your files stay put.

Kaggle (S tier)#

30 hours a week, stated up front, no guessing. The GPU is a Tesla P100, roughly on par with a T4, with 4 CPUs and 29 GB RAM. Sessions mostly stay up. The interface feels like Colab. There is no persistent storage, so I download and upload data each session. Owned by Google. Still the most generous quota I have found.

AWS SageMaker Studio Lab (S tier)#

4 GPU hours plus 12 CPU hours a day, on a T4 like Colab. It rarely drops, storage persists, and it runs most of what I throw at it. You have to register on the AWS site first, which takes a few minutes. Slightly fewer GPU hours than Kaggle, but the storage makes up for it.

Saturn Cloud (B tier)#

The free plan gives you either 64 GB RAM or 1 T4 plus 16 GB RAM. The interface feels dated and it drops more than I would like. The raw resources are fine, the experience around them is not.

Deepnote (D tier)#

2 CPUs and 5 GB RAM, no GPU. You see it advertised a lot. It cannot do ML work at this size, so I stopped trying.

Lightning AI (S tier), my favorite for real work#

22 GPU hours a month on the free tier, plus one studio with 4 CPUs that stays free for CPU use. You add a GPU to the studio with one click. The interface feels like VS Code, and once I am connected it stays connected. It supports T4, L4, and A10G. Phone verification is required, no credit card. This is the one I open when I want a normal dev environment, not just a notebook.

Azure ML Notebooks (C tier)#

This is $20 in credits on signup, not a free tier that stays free. Setup is fiddly and the credits run out. I found no reason to pick it over the free options above.

Google Vertex ML Notebooks (C tier)#

Same story as Azure. It needs a Google Cloud account and the $300 credit runs out. Not free in any lasting sense.

Hugging Face Spaces (C tier)#

No GPU on the free tier, just 2 CPUs and 16 GB RAM with unlimited runtime. Fine for small CPU models and demos. Not where I would train.

GitHub Codespaces (B tier)#

Two CPU options, 2-core with 8 GB RAM or 4-core with 16 GB RAM, 60 hours a month, VS Code in the browser with a terminal. Good for smaller models and general coding. No GPU, so it caps what you can do for ML.

What changed by 2026#

Kaggle#

Now dual T4 GPUs (30 GB combined), up from a single P100. Still 30 hrs a week, 9-hour session limits. 73 GB persistent storage was added since the older guides. No internet during GPU sessions, which is a security measure. Still my S tier pick for quota size.

Lightning AI#

Now 80 free GPU hours a month, up from 22. T4, L4, A10G on free, with A100 and H100 on paid tiers. 50 GB persistent storage. Free studios restart every 4 hours. Supports VS Code extensions, Cursor, SSH, and CLI. Still S tier, and more generous than before.

Google Colab#

About 15 to 30 hours a week on T4 or P100, 12-hour session limits, still random drops. Colab Pro ($9.99/mo) and Pro+ ($49.99/mo) exist if you want to pay. Still B tier for me. Convenient, just not steady.

Paperspace Gradient#

Free tier is M4000 with 8 GB VRAM. Queue times can get long. Now part of DigitalOcean. Still A tier, and storage is still the reason.

New platforms worth knowing (2026)#

How I split work to get the most free GPU#

1. Kaggle first, 30 hrs a week that I can count on.
2. Lightning AI for the actual dev environment, 80 hrs a month.
3. Google Colab for quick tries and overflow.
4. Groq or Cerebras for free LLM inference, not for training.

Across platforms that adds up to about 150 free GPU hours a month.

What I take away from this#

• Kaggle is the most generous and the most steady free GPU I have used.
• Lightning AI is where I do the work, because the VS Code setup feels normal.
• Colab is fine for a fast test, not for a long training run.
• Persistent storage matters more than it looks. Losing your dataset on every drop gets old fast.
• Combined free tiers cover DistilBERT, GPT-2 small, ViT, and 7B fine-tunes with QLoRA without paying.