Colab vs AirCloud: Which Jupyter Notebook Fits You?
- Aug 4
- 7 min read
Updated: Aug 7
Back in college, when I took a data analysis class, I spent more time setting up a dev environment than actually writing code (this was before GPT existed). Installing Visual Studio, matching Python versions, and fixing library errors sometimes ate up the entire 3-hour class before I wrote a single line of code 😂
Google Colab was a genuinely surprising tool for someone like me. Just open a browser and run code immediately? It let me focus on solving problems instead of spending time setting up an environment.
But ironically, as I started training and fine-tuning machine learning models more often, Colab became the thing that frustrated me most. Training would be running smoothly, then the session would disconnect. I'd wait for a GPU again. I'd re-download the model again. Things I never used to think twice about started to matter, one by one.
Colab is still a great development environment today. But it isn't the best choice for every environment. Which one fits better depends on the scale of your project and how you work. In this post, we'll compare Colab and AirCloud across pricing, GPU, data, and AI assistants and look at which environment fits your way of working better.
1. Same Jupyter Notebook, Different Operating Model
Both Google Colab and AirCloud run an IPython kernel on top of a JupyterLab interface you open in your browser. You run code cell by cell, and variables and memory are shared across cells as long as the kernel stays alive, in the same way on both. Looking at the user experience alone, it's hard to tell the two services apart.
The difference starts in how that notebook is operated. How you're billed, whether you can choose your own GPU, whether your work environment survives after a session ends, and which model powers the AI assistant, all of these shape a very different experience even on the same Jupyter notebook.
2. Monthly Subscription vs. Pay-as-You-Go: Which Pricing Model Works Better?
Colab Pro gives you 100 compute units (CUs) for $9.99 a month, and Pro+ gives you 500 CUs for $49.99 a month. Both plans are subscriptions, so you're billed on your billing date regardless of usage, and unused CUs roll over for up to 90 days. Once you run out, you can buy more at $9.99 per 100 CUs.
One thing worth noting is how fast CUs burn. Google doesn't specify in its official docs exactly how many hours of use each GPU tier consumes per CU. Its official explanation is that burn rate varies by session even on the same GPU, which makes it hard to calculate in advance how many hours you'll actually get in a given month before you pay.
AirCloud fixes this to a single unit. Each GPU type has a set hourly rate ($0.52/hr for the RTX 4090, $0.77/hr for the RTX 5090, $1.63/hr for the RTX PRO 6000), and you're billed only for the hours your instance is running. For example, a user running an RTX 4090 for about 20 hours a month would be billed roughly $10.40, and if you never turn it on that month, you pay nothing. Reserving for 12 months or more also gets you a discount of up to 25%.
If your usage is steady and you don't need to worry about CU burn rate, a subscription is convenient too. But if your usage is irregular or you want to pay exactly for what you use, hourly pay-as-you-go pricing is easier to calculate.
3. Does Your Data Survive When the Runtime Disconnects?
Whether your work survives after the runtime ends matters more than it might seem. GPU workloads rarely wrap up in a few minutes. Just think about training a Stable Diffusion model or a LoRA: you download the model, install the libraries you need, check and tweak the runtime output, repeat that cycle, and it's common to keep working in the same environment for days. Along the way, everything the job needs, notebooks, model files, checkpoints, generated outputs, piles up inside a single workspace.
On Colab, this workspace is tied to the session by default. The /content folder is temporary storage attached to the currently running runtime, and once the runtime ends or resets, any models, generated images, checkpoints, or log files stored inside it can disappear along with it. You can mount Google Drive to preserve data, but deciding which files to move to Drive, and saving checkpoints or copying outputs along the way, is entirely on you.
AirCloud is designed for exactly this situation, letting you separate compute from storage. You can create a Persistent Volume and attach it to a container whenever you need to, and the data stored there stays intact even if you stop or delete the instance. So if you save model files, checkpoints, and generated images to a persistent volume, you can pick up right where you left off even after spinning up a brand-new GPU instance.
A single persistent volume can also be attached to multiple JupyterLab instances or containers. Since a new environment can use the same storage as before, you never have to re-copy or re-download the notebooks, data, and model files you need.
In short, on Colab, the GPU and storage act as a single bundle. When the session ends, your workspace can be wiped along with it. AirCloud, on the other hand, lets you manage the GPU and storage separately. You spin up a GPU whenever you need one, and your data just stays put on the persistent volume.
4. Can You Actually Choose the GPU You Want?
Colab only describes GPU and TPU access as "subject to availability." The Pro/Pro+ plans mention "priority access to more powerful GPUs," but that's a priority, not a guarantee. Plenty of users report paying for Pro+, requesting a top-tier GPU, and getting assigned a lower-end one instead. If you need a specific GPU guaranteed, you have to use a separate product, GCP Marketplace Colab.
This difference isn't just about speed. A different GPU means different VRAM and different compute performance. A notebook that ran fine yesterday might get assigned a different GPU today, forcing you to shrink your batch size, or some models might not run at all. If you're repeating the same experiment or need your team working in an identical environment, being able to pin down your GPU spec starts to matter a lot.
AirCloud puts this decision in your hands. You choose the GPU you need, RTX 4090, RTX 5090, RTX 6000 Pro, and more, at deploy time and run it. That makes it easy to repeat experiments or pick up a project later on the exact same notebook and GPU setup you configured.
5. Which AI Assistant Should You Use?
Colab's AI assistant runs on Gemini. You can ask it questions about your code through chat or get quick fixes for errors, and it recently added a Data Science Agent feature that generates an entire notebook from a natural-language description. That said, the models you can use are still locked to Gemini (Gemini 2.5 Flash, Gemini 3 Flash, and Gemini 3.1 Pro for Pro-and-above subscribers), so it's hard for users to bring in any model outside that lineup.
AirCloud also supports an AI coding assistant right inside the notebook. Jupyternaut is AirCloud's Jupyter AI-based coding assistant, and it ships by default with Qwen 3.6 35B, a model that's become popular for agentic coding lately. It handles the basics, writing code from natural language, fixing errors, explaining functions, and if you need to, you can connect any other LLM that supports an OpenAI-compatible API, so you can keep using whatever AI model your project or team has already standardized on as your notebook's AI assistant.
You can access this feature directly from the AirCloud tab on the left side of the notebook. The default Qwen 3.6 35B works as soon as you enter your Air API key, and if you connect a different LLM, you just swap the model on the same screen.
From the sidebar, you can ask questions about your code in natural language, generate code cells with the /generate command, and fix broken code with the /fix command. If you hit an AttributeError or a NameError, for example, it explains the cause and suggests a fixed version of the code you can apply right away.
If you're working solo, which AI assistant you use might not matter much. But if you're developing as a team, being able to use your company's standard model right inside the notebook means your AI assistant stays consistent with the rest of your project environment too.
6. At a Glance
Category | Colab Pro/Pro+ | AirCloud |
Cost | Pro: $9.99/mo. Pro+: $49.99/mo. | $0.30/hr ~ |
Pricing Model | Monthly subscription, CU burn rate undisclosed | Hourly pay-as-you-go, usage-based billing |
GPU Allocation | Availability-based, no specific GPU guaranteed | Choose GPU type at deploy time (RTX 4090/5090/6000 Pro, etc.) |
Data Persistence | Temporary disk wiped at session end, manual Drive backup required | Persistent Volume support, kept across stop, redeploy, and autoscaling |
AI Assistant | Gemini-based, model fixed | Jupyter AI-based, custom model selection |
Security Certification | Follows Google Cloud's security policy | ISO 27001 certified |
7. Which Jupyter Notebook Do You Actually Need?
It's clear why Google Colab is still the first choice for so many developers. You can start a GPU environment for free (with some limits), and just opening a browser gets you running code with no environment setup at all. For quick exercises, lectures, or exploring data, right now, it's simply the best coding environment there is.
But is Colab really the best option for fine-tuning jobs that run for hours, or for repeated batch inference?
As we've seen in this post, the answer depends on how you work. Do you want to rent a GPU only when you need it and pay exactly for that usage? Does your project need a fixed GPU spec? Does your workspace need to survive without disappearing on you? Once these questions start to matter, a platform that lets you manage the GPU and your workspace together is naturally a better fit than one where you're simply renting a GPU.
In the end, what matters most is choosing the operating model that fits your project. Even for the same 20 hours on the same GPU, your monthly bill can range anywhere from $9 to $99 depending on how the service prices it. Using the pricing, GPU, data, and AI assistant criteria covered in this post, pick the environment that fits your way of working better. AirCloud can be one of those options.
