Market insights

Top Google Colab Pro Alternatives (September 2025) - Pricing and Availability

September 16, 2025
13 mins read

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

  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

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!

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