Google Colab Pro vs Colab Pro+ — Technical Overview, Compute Units, and Usage Guide

📅 31 Jan 2026 📂 General 👁 63 views

This article explains Google Colab Pro and Google Colab Pro+ from a technical and operational perspective.
It is intended for IT professionals, system administrators, data engineers, ML engineers, and support teams who manage or support workloads using Google Colab.

The focus is on:

Compute units
Resource behavior (GPU/TPU, RAM, sessions)
Limitations and risks
Practical usage and troubleshooting

2. Product Overview

What Is Google Colab?

Google Colab is a managed Jupyter notebook environment hosted by Google. It allows execution of Python code in the cloud with optional access to GPUs and TPUs.

Colab Subscription Tiers

Plan	Monthly Compute Units	Key Capabilities
Free	Limited	Shared CPU/GPU, short sessions
Colab Pro	100 units	Faster GPUs, more RAM
Colab Pro+	500 units	Best GPUs, background execution

3. Architecture and Resource Behavior

Compute Units (CU)

Compute Units represent consumption-based credits used when running:

GPUs (T4, L4, A100 — availability varies)
TPUs
High-RAM runtimes
Long-running or background sessions

Consumption increases with:

GPU/TPU usage
High memory usage
Session duration
Background execution (Pro+ only)

Runtime Allocation (Non-Guaranteed)

Colab uses a fair-use scheduling model:

Resources are not dedicated
GPU type and RAM size vary
Availability depends on global demand

Example runtime inspection:

!nvidia-smi

Check memory:


!free -h

4. Colab Pro vs Pro+ — Technical Differences

Feature	Colab Pro	Colab Pro+
Compute Units	100 / month	500 / month
GPU Priority	Medium	High
High-RAM Access	Yes	Yes
Background Execution	No	Yes
Long Sessions	Limited	Extended
Best for	Dev, testing	Training, pipelines

5. Use Cases and Supported Environments

Suitable Use Cases

Machine learning model training
Data preprocessing
Prototyping AI workflows
Educational labs
Temporary GPU workloads

Not Recommended For

Production workloads
SLA-based services
Regulated data processing
Persistent backend services

6. Step-by-Step: Enable and Configure Colab Pro / Pro+

Step 1: Select Subscription

Open Google Colab
Navigate to Settings → Subscriptions
Choose Pro or Pro+

Step 2: Select Runtime Type


Runtime → Change runtime type
Hardware accelerator → GPU / TPU

Step 3: Verify Allocation


import torch
print(torch.cuda.is_available())

7. Common Errors, Root Causes, and Fixes

Error: GPU Not Available

Cause

High global demand
Compute units exhausted

Fix

Switch to CPU temporarily
Wait and reconnect
Upgrade to Pro+

Error: Session Disconnects

Cause

Idle timeout
Long execution without output

Fix


from time import sleep
while True:
    sleep(60)

(Use cautiously; excessive keep-alive may violate fair use)

Error: Out of Memory (OOM)

Cause

Model too large
Dataset loaded fully into RAM

Fix

Use batch loading
Reduce model size
Use generators

8. Security Considerations and Risks

Data Security

Notebooks run on shared infrastructure
No compliance guarantees (HIPAA, PCI, etc.)
VM resets wipe local data

Risks

Credential leakage in notebooks
Accidental sharing of notebooks
Data persistence misunderstanding

Recommendation

Never store secrets in plain text
Use environment variables


import os
os.environ["API_KEY"]

9. Best Practices and Recommendations

Resource Optimization

Stop idle runtimes
Use smaller datasets for testing
Cache data to Google Drive only when required

Operational Practices

Export notebooks regularly
Monitor compute unit usage
Avoid background execution abuse

For Teams

Not suitable for shared service accounts
Each user requires individual subscription

10. Limitations Summary

No guaranteed GPU model
No fixed uptime
No enterprise SLA
Internet access restricted in some regions
Unsuitable for always-on workloads

11. Conclusion

Colab Pro and Colab Pro+ provide cost-effective, on-demand compute acceleration for development and experimentation.
They are ideal for temporary ML and data workloads, but not replacements for production infrastructure.

Proper understanding of compute units, session behavior, and limitations is critical for effective use.

To get quotation for Google Workspace, contact

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BISON INFOSOLUTIONS
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Authorized Google Workspace Partner

Mobile: +91 92125-22725
Helpdesk: 70-479-479-70 | 76-588-588-76
Email: info@bison.co.in
Website: www.bison.co.in

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Google Colab Pro vs Colab Pro+ — Technical Overview, Compute Units, and Usage Guide

2. Product Overview

What Is Google Colab?

Colab Subscription Tiers

3. Architecture and Resource Behavior

Compute Units (CU)

Runtime Allocation (Non-Guaranteed)

4. Colab Pro vs Pro+ — Technical Differences

5. Use Cases and Supported Environments

Suitable Use Cases

Not Recommended For

6. Step-by-Step: Enable and Configure Colab Pro / Pro+

Step 1: Select Subscription

Step 2: Select Runtime Type

Step 3: Verify Allocation

7. Common Errors, Root Causes, and Fixes

Error: GPU Not Available

Error: Session Disconnects

Error: Out of Memory (OOM)

8. Security Considerations and Risks

Data Security

Risks

9. Best Practices and Recommendations

Resource Optimization

Operational Practices

For Teams

10. Limitations Summary

11. Conclusion

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