Where to Actually Run Python Without Setting Up a Local Environment
I spent about three years debugging code on my laptop before I started throwing everything into browser-based runners just to get results faster. The trade-off is real, but the time savings are too. Here is what I have found worth using, and more importantly, what actually falls apart when you push it. Google offers two straightforward paths for executing Python in the cloud without any infrastructure to manage. Google Colab and Vertex AI Workbench. They solve different problems, and picking the wrong one for your workload will waste more time than setting up a local machine would have. Colab runs notebooks directly in your browser. It gives you free access to a GPU, which most people never actually utilize properly. The free tier hands you a Tesla T4 if you are lucky, with session timeouts around 90 minutes and a maximum runtime per cell of 12 hours on the paid tiers.
The real bottleneck nobody warns you about is the ephemeral filesystem. Every time your session restarts, everything disappears. I hit this hard when I was training a moderate-sized transformer model. The model checkpoint was 8 gigabytes. My training ran for six hours, got to epoch 14, and the session timed out. I had saved the model to the local VM filesystem, so it was gone. The workaround was uploading checkpoints directly to Google Cloud Storage after every five epochs using a simple gcsfs write. That added maybe 30 seconds per checkpoint but saved me from losing three days of fine-tuning. It is not elegant, but it works consistently. For data analysis work, Colab connects easily to Google Drive, which removes the worst of the file management headache. Mount your Drive with one cell, point your pandas reads at it, and you are moving. The downside is that Drive I/O is slow. Reading a 500-megabyte CSV from a shared Drive folder over the mounted API takes roughly 45 seconds on a good connection. Copying it locally first drops that to about four seconds.
Vertex AI Workbench: When You Need Something That Stays Put
Vertex AI Workbench is the other option, and it is closer to a full virtual machine than a notebook environment. You provision a persistent instance, it runs indefinitely until you shut it down, and you pay by the hour for the underlying compute. The pricing varies depending on machine type and GPU addition, but budget roughly $0.10 to $0.50 per hour for a reasonably capable setup without a GPU. This matters when you are doing something that requires persistent state beyond a single notebook session. I use it for long-running background processes like scheduled data pipeline jobs that run overnight. Colab cannot do that reliably. Vertex lets you SSH into the instance, set up systemd services, or just leave a Jupyter server running with a consistent filesystem. The catch is that Vertex requires a GCP billing account and proper IAM configuration. If you are inside an organization that uses centralized cloud billing, getting your access request approved can take anywhere from a day to two weeks depending on your company's IT process. Plan around that if you are trying to move fast.
Get the Full Details
Cloud Shell: The Bare Minimum Option
Google Cloud Shell gives you a temporary browser-based terminal with Python preinstalled and about 5 gigabytes of persistent home storage. It is not a notebook. It is a shell. But sometimes that is exactly what you need. I use Cloud Shell for quick scripting tasks, one-off data transformations, or testing snippets before committing them to a larger project. It has zero setup friction, but the environment is restrictive. No GPU access. No persistent installations beyond what you can stack into your home directory each session. And the bandwidth throttle can be frustrating if you are pushing large files. Uploads and downloads are capped, and 5 gigabytes of persistent storage fills up quickly if you are working with datasets.
Common Mistakes People Make Across All Three
The first mistake is assuming that because the environment is cloud-hosted, your data is safe by default. It is not. Colab sessions can be terminated at any time. Cloud Shell home directories get wiped on prolonged inactivity. Vertex AI instances are your responsibility to shut down or you keep paying for them. The second mistake is ignoring package installation overhead. Installing heavy packages like PyTorch or TensorFlow in a Colab cell takes longer than you expect because the environment rebuilds its pip cache each session. Pinning your packages and checking whether they are already installed before running pip install can save you two to three minutes per session start. The third mistake is treating these environments as production systems. None of them are designed for that. Colab has no SLA. Cloud Shell times out after inactivity. Vertex is closer but still requires significant ops overhead to make it reliable at scale. If you are running code that other people depend on, containerize it and deploy it properly on Cloud Run or a Compute Engine instance instead.
What Actually Happens When Things Break
Runtime disconnections in Colab happen frequently during long executions, especially when sharing a GPU with other users. The kernel dies silently. You get a red error indicator and your variable state is gone. I learned to write a heartbeat check function that pings an external endpoint every few minutes, and if it stops responding, the script logs the failure and saves what it can. It sounds excessive until you lose a 10-hour job because a network blip killed your kernel. Vertex AI Workbench instances sometimes fail to mount shared drives correctly after a restart. The mount point appears but returns permission denied on every file access. Restarting the instance usually fixes it, but the underlying cause tends to be a stale credential cache in the VM's metadata service. Deleting the old credentials and letting the instance re-authenticate clears it without needing a full reboot. Cloud Shell loses your work if you close the tab and come back later, unless you intentionally saved everything to persistent storage. It is easy to forget because the interface feels responsive and alive while you are working. Just treat it like a public computer that occasionally resets itself.
Bottom Line
For quick experiments and interactive data work, Colab is the fastest way to get Python running with GPU access in under a minute. For anything that needs to persist beyond a single session or run on a schedule, Vertex AI Workbench is the more responsible choice despite the higher setup cost. Cloud Shell fills a narrow lane for terminal-based tasks where you do not need notebooks or GPUs. Each has failure modes that bite you in predictable ways. Knowing them before you hit them saves you from losing hours of work to environment quirks that nobody documents clearly.
