Top Google Colab Pro Alternatives (December 2025) - Pricing and Availability
Here are the best Google Colab Alternatives to get cheap (or free) GPUs for Deep Learning in August 2025—ranked by cost, simplicity, credit, and features.
TL;DR: Compare Google Colab Alternatives
| Provider | Typical Notebook GPUs | Cheapest on‑demand price | Free tier / credits | Session limits | Best for |
|---|---|---|---|---|---|
| Thunder Compute | T4 (16 GB), A100 (40 GB) | T4 $0.27 / hr, A100 $0.66 / hr | None | Pay‑as‑you‑go, no hard stop; billed for usage | Budget tiny‑to‑mid experiments that need uninterrupted runs |
| Google Colab (Free) | K80 / T4 (variable) | Free | N/A | 12 h per session, pre‑emptible | Quick trials, classroom demos |
| Google Colab Pro | T4 most of the time | $9.99 / mo + compute units (~$1.20 / GPU‑h)* | 100 CU on signup | Soft usage cap, still pre‑emptible | Beginners wanting longer runtimes |
| Kaggle Notebooks | T4 / P100 | Free | None | 9 h per session, 30 h per week | Competitions, light fine‑tunes |
| AWS SageMaker Studio Lab | T4 | Free | None | 4 h per session, 4 h per 24 h | Short GPU demos, teaching |
| Paperspace Gradient (Free) | M4000 GPU | Free | None | 6 h idle shutdown | Learning PyTorch/TensorFlow |
| Paperspace Gradient (Paid) | T4, A4000 | T4 ≈ $0.45 / hr | $10 credit on Pro plan | 12 h auto‑shutdown | Private team notebooks |
| RunPod Secure Cloud | A40, A100 | A40 $0.44 / hr, A100 $1.19 / hr | None | No hard stop | DIY VM + SSH—notebook optional |
1. Thunder Compute: Cheapest Hourly Cost Without Interruptions
- Why it beats Colab: On‑demand T4s at $0.27/hr, A100s at $0.66/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
| Need / Scenario | Go with |
|---|---|
| Longest uninterrupted training for the money | Thunder Compute T4/A100 |
| Totally free, light workloads | Kaggle Notebooks or SageMaker Studio Lab |
| Zero‑setup classroom demos | Google Colab Free |
| High‑end GPU for one‑off job | RunPod or Thunder A100 |
| GUI‑centric, team collaboration | Paperspace Gradient Pro |
How to Move a Colab Project to Thunder in <10 Minutes
- Export your Colab notebook (File → Download .ipynb).
- Install the Thunder Compute VSCode/Cursor Extension
- Connect to an instance and drag your .ipynb file into the instance filesystem
FAQs
Q: Do these platforms throttle heavy users?
A; Yes—anything free will throttle. Paid hourly clouds (Thunder, RunPod, Lambda) bill strictly for usage and do not throttle, but stock can sell out.
Q: Is an A100 always faster than a T4?
A: 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.
Q: 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!