Batch Inference is generally available on Runpod
Run the work that can wait as Batch Inference and keep real-time capacity for live traffic.
All
Google Colab is the easiest way to get a free GPU and a notebook in the same browser tab. That is a real thing to be good at. But Colab's paid tiers do not sell you a GPU. They sell you metered access to whatever hardware is free when your notebook asks, and Google says so plainly.
On Runpod you pick the GPU, you get that GPU, and you pay for it by the second until you stop it. This post is about when that difference starts to matter.
Colab does not guarantee compute, and says so
This is not a competitive talking point, it is in Google's own FAQ. On why resources are not guaranteed:
In order to dynamically offer powerful GPUs at scale for a low price, Colab needs to maintain the flexibility to adjust usage limits and hardware availability dynamically.
Colab FAQ
That flexibility is not limited to the free tier. On the paid plans specifically:
These plans have similar dynamics in that resource availability may change over time. Paid users whose compute unit balance is exhausted will revert to the free-of-charge tier policies and restrictions until the balance is increased.
Colab FAQ
Colab's paid tiers meter usage in compute units. Different GPUs burn units at different rates, so the faster the card you are assigned, the faster your balance drains. When the balance runs out you drop back to free-tier policies and restrictions until you top it up.
Which card you get is its own variable. Google's FAQ says the types of GPUs and TPUs available in Colab vary over time, and that paid plans give you access to premium GPUs subject to availability. Colab also does not publish its usage limits, in part because they change.
None of this is a criticism. It is a reasonable trade, and for a lot of work it is the right one. It stops being the right one at a specific point, which is the next section.
When "probably a GPU" stops being good enough
Three situations, and they are the ones people write to us about.
A training run that takes longer than a session. Colab notebooks time out if you are idle, and in general run for at most 12 hours depending on availability and your usage patterns. Pro+ supports continuous execution up to 24 hours if you have sufficient compute units. A fine-tune that needs eleven hours is a fine-tune you are babysitting.
Work where the GPU has to be the same GPU every time. Benchmarking, anything you are going to publish numbers from, anything where a result has to be reproducible. If the hardware under the notebook can change between runs, your measurements are not comparable to each other.
Anything with a deadline. "Subject to availability" is fine on a Tuesday afternoon project. It is not fine the night before a demo.
Google agrees, and points you off Colab for it. The FAQ repeats in several places that if you want guaranteed resources without Colab-enforced usage limitations, the answer is GCP Marketplace, Colab Enterprise, or a local runtime you control.
What you get on Runpod instead
You choose the GPU from the list and deploy it. Pods are billed per second, there is no subscription, and nothing reclaims the card out from under you while the pod is running. Deploying Jupyter on one to eight GPUs takes seconds.
The cards people usually move to, with current rates:
- RTX A5000, 24 GB – Secure Cloud {{gpu:rtx-a5000}}/hr
- RTX 3090, 24 GB – Secure Cloud {{gpu:rtx-3090}}/hr
- RTX 4090, 24 GB – Secure Cloud {{gpu:rtx-4090}}/hr
- L4, 24 GB – Secure Cloud {{gpu:l4}}/hr
- A40, 48 GB – Secure Cloud {{gpu:a40}}/hr
- RTX A6000, 48 GB – Secure Cloud {{gpu:rtx-a6000}}/hr
- A100 PCIe, 80 GB – Secure Cloud {{gpu:a100-pcie}}/hr
Secure Cloud runs in Runpod-vetted data centers, and you get the card you selected.
The practical difference is not a price comparison, it is what you can plan around. A ten-hour run on a card you chose costs what ten hours on that card costs, and you knew that before you started it.
Storage
Colab connects to Google Drive in one click, which is genuinely convenient and worth saying.
On Runpod you choose. Cloud Sync moves pod data to Amazon S3, Google Cloud Storage, Azure Blob Storage, Dropbox or Backblaze B2, and bandwidth is free. One thing to know: Cloud Sync targets Google Cloud Storage, not Google Drive. Drive transfers use a different method.
For work that persists between pods, a network volume is usually the better answer than syncing out and back. And worth saying plainly: Runpod is not built for long-term storage, so keep a copy of anything you cannot regenerate somewhere else.
Which one should you use
Colab if you are learning, prototyping, or doing anything where a free GPU and a notebook is the whole requirement. It is better at that than we are and it costs nothing.
Runpod when the hardware has to be a known quantity. A specific GPU, for as long as you want it, billed by the second, with no subscription to size against your worst month.
Plenty of people use both, and that is a sensible setup: prototype in a notebook, run the real job on hardware you picked. You can also point a Colab notebook at a Runpod pod and keep the interface while the compute runs on a card you chose.
FAQ
Does Colab Pro guarantee you a GPU?
No. Google's FAQ states that the paid plans have similar dynamics to the free tier in that resource availability may change over time, and that premium GPUs are available subject to availability. Paid tiers raise your limits and your priority. They do not reserve hardware for you.
How much does Google Colab Pro cost?
Colab Pro and Pro+ are still available as their own subscriptions, and an eligible paid Google AI plan now also includes Colab benefits. Check Google's current pricing for the figures, since the plan lineup changed recently. The structural point matters more for planning: whichever route you take, you are buying compute units rather than a GPU, and different cards consume units at different rates.
Can I run a job on Colab for longer than 24 hours?
Not reliably. Notebooks generally run for at most 12 hours depending on availability and usage patterns, and Pro+ supports continuous execution up to 24 hours if you have sufficient compute units. Both are subject to idle timeouts, and you can expect backend termination if your compute units run out. For a run that has to finish, a pod you control has no equivalent ceiling.
What is the cheapest way to rent a GPU by the hour?
On Runpod, rates start at {{gpu:rtx-a5000}}/hr for a 24GB RTX A5000 on Secure Cloud, and billing is per second, so an eleven-minute job costs eleven minutes.
Can I use Google Colab with a Runpod GPU?
Yes. You can point a Colab notebook at a Runpod pod and keep the notebook interface while the compute runs on hardware you selected. We have a walkthrough: Connect Google Colab to Runpod for Custom GPU Power.
Related posts
- Encrypted Volumes on Runpod: Protect Your Data at Rest
- Connect Google Colab to Runpod for Custom GPU Power
- Easily Backup and Restore Using Runpod Cloud Sync and Backblaze B2 Cloud Storage
- How to Run a GPU-Accelerated Virtual Desktop on Runpod
- How to Configure Basic Terminal Access on Runpod
- How to Achieve True SSH in Runpod
Author profile: Zhen Lu
