Cheapest Google Colab Alternatives: Get Free GPUs for Data Science and Deep Learning in 2025

Here are the best alternatives to Google Colab to get GPUs for Deep Learning in 2025—ranked by cost, simplicity, and features.

TL;DR: Compare Cheap Cloud GPU Providers

1. Thunder Compute: Cheapest Hourly Cost Without Interruptions

• Why it beats Colab: On‑demand T4s at $0.27/hr, A100s at $0.57/hr: ~3–4× cheaper than Colab’s pay‑as‑you‑go rate once CU are exhausted. No automatic shutdowns, persistent storage.

• What you get: One click or command to enter cloud instances, easy VSCode integration, and cheap access to advanced GPUs.

• Ideal for: Budget hyper‑parameter sweeps, overnight fine‑tunes, or anything that can’t risk Colab pre‑emptions.

Get started with the VSCode extension or CLI here

2. Kaggle Notebooks: Still the Most Generous Free GPU

Kaggle offers free T4 or P100 GPUs with a weekly quota of 30 GPU‑hours. Sessions last up to 9 hours, and background execution lets training continue once you close the tab.

Pros

• 20 GB persistent storage

• Direct access to Kaggle datasets & competitions

Cons

• No A100 GPUs

• Public by default; private notebooks require an upgrade

Tip: Use the new dual‑T4 option (beta) for distributed training with DataParallel.

3. Google Colab Free, Pro, Pro+: Familiar UX, Rising Costs

Colab added a compute‑unit model in 2024. A T4 burns ~11.7 CU/hr; an A100, ~62 CU/hr. Pay‑as‑you‑go is $9.99 for 100 CU (~8.5 T4 hours) or you can subscribe to Pro ($9.99/mo) or Pro+ ($49.99/mo) for higher burst quotas.

Pain points

• Unpredictable throttling when CU deplete

• Pre‑emptible VMs can shut down mid‑epoch

• A100 availability restricted to Pro+

Colab remains handy for quick prototyping or educational content, but costs ramp fast for sustained training runs.

4. AWS SageMaker Studio Lab: 4 Hours a Day for Free

Studio Lab supplies a T4 GPU for up to 4 hours per session and caps GPU use at 4 hours per 24‑hour window.

Strengths

• AWS backend and GitHub integration

• No credit card required

Limitations

• Long queue times for GPU slots

• No paid upgrade path (must jump to full SageMaker)

Great for teaching labs or proof‑of‑concepts that finish quickly.

5. Paperspace Gradient: Generous RAM, Middling GPU Prices

Gradient’s free community notebooks offer M4000 GPUs (8 GB VRAM) and 30 GB RAM, with a 6‑hour auto‑shutdown. Paid on‑demand notebooks start around $0.45/hr for a T4.

• Upside: slick notebook UI, easy dataset uploads.

• Downside: Free GPUs go out of stock during US daytime; storage is only 10 GB on free tier.

6. RunPod Secure Cloud: Raw VMs at Marketplace Prices

RunPod isn’t a managed notebook like Colab; it’s a marketplace for bare‑metal or fractional GPUs. The A40 at $0.44/hr is popular for inference, while an A100 80 GB starts at $1.19/hr.

• BYO Jupyter or VS Code over SSH

• Community Cloud instances can be interrupted; Secure Cloud adds guarantees at a small premium.

Choosing the Right Alternative

How to Move a Colab Project to Thunder in <10 Minutes

1. Export your Colab notebook (File → Download .ipynb).

2. Install the Thunder Compute VSCode/Cursor Extension

3. Connect to an instance and drag your .ipynb file into the instance filesystem

Frequently Asked Questions

Do these platforms throttle heavy users?

Yes—anything free will throttle. Paid hourly clouds (Thunder, RunPod, Lambda) bill strictly for usage and do not throttle, but stock can sell out.

Is an A100 always faster than a T4?

For large‑batch training or >7 billion‑parameter models, absolutely. For smaller CNNs or lightweight fine‑tunes, the price/perf sweet spot is often a T4.

What about TPUs?

TPUs are rarely available outside Colab’s pay‑as‑you‑go units and Google Cloud’s high‑end pricing; most indie projects stick to CUDA GPUs.

Bottom Line

Colab is unbeatable for zero‑cost tinkering, but once your deep‑learning notebook needs predictable runtimes—or your wallet needs predictable costs—switching to a low‑cost, on‑demand GPU cloud like Thunder Compute or marketplace options like RunPod saves serious money. For purely free workloads, Kaggle’s 30 GPU‑hours/week remains the most generous.

Happy training!