Beam vs Google Cloud
Compare GPU pricing, features, and specifications between Beam and Google Cloud cloud providers. Find the best deals for AI training, inference, and ML workloads.
Beam
Provider 1
Google Cloud
Provider 2
Comparison Overview
GPU Pricing Comparison
| GPU Model ↑ | Beam Price | Google Cloud Price | Price Diff ↕ | Sources |
|---|---|---|---|---|
L4 24GB VRAM • Google Cloud | Not Available |  $0.56/hr★ Best | — | |
L4 24GB VRAM • | ||||
Tesla T4 16GB VRAM • Google Cloud | Not Available | $0.35/hr★ Best | — | |
Tesla T4 16GB VRAM • | ||||
Tesla V100 32GB VRAM • Google Cloud | Not Available | $2.48/hr★ Best | — | |
Tesla V100 32GB VRAM • | ||||
L4 24GB VRAM • Google Cloud | Not Available | $0.56/hr★ Best | — | |
L4 24GB VRAM • | ||||
Tesla T4 16GB VRAM • Google Cloud | Not Available | $0.35/hr★ Best | — | |
Tesla T4 16GB VRAM • | ||||
Tesla V100 32GB VRAM • Google Cloud | Not Available | $2.48/hr★ Best | — | |
Tesla V100 32GB VRAM • | ||||
Features Comparison
Beam
- Pay-per-millisecond Billing
Only charged when your code runs, no charges for cold starts or server spin-up
- Sub-second Cold Starts
Fast function initialization with checkpoint restore technology
- Sandbox Snapshots
Snapshot and restore sandbox state for faster deployments
- Autoscaling
Automatic scaling based on demand with deployment logs included
- Custom Docker Images
Bring your own Docker images for full environment control
- Open Source
Core engine (beta9) is open-source and can be self-hosted
Google Cloud
- Compute Engine
Scalable virtual machines with a wide range of machine types, including GPUs.
- Google Kubernetes Engine (GKE)
Managed Kubernetes service for deploying and managing containerized applications.
- Cloud Functions
Event-driven serverless compute platform.
- Cloud Run
Fully managed serverless platform for containerized applications.
- Vertex AI
Unified ML platform for building, deploying, and managing ML models.
- Preemptible VMs
Short-lived compute instances at a significant discount, suitable for fault-tolerant workloads.
Pros & Cons
Beam
Advantages
- Pay-per-millisecond billing with no charges for cold starts
- Sub-second cold starts with checkpoint restore
- Up to 80% savings compared to always-on instances
- Open-source core engine (beta9) available for self-hosting
Considerations
- Smaller GPU selection compared to major cloud providers
- Newer platform with smaller community
- Focused primarily on serverless workloads
Google Cloud
Advantages
- Flexible pricing options, including sustained use discounts
- Strong AI and machine learning tools (Vertex AI)
- Good integration with other Google services
- Cutting-edge Kubernetes implementation (GKE)
Considerations
- Limited availability in some regions compared to AWS
- Complexity in managing resources
- Support can be costly
Compute Services
Beam
Google Cloud
Compute Engine
Offers customizable virtual machines running in Google's data centers.
Google Kubernetes Engine (GKE)
Managed Kubernetes service for running containerized applications.
- Automated Kubernetes operations
- Integration with Google Cloud services
Cloud Functions
Serverless compute platform for running code in response to events.
- Automatic scaling and high availability
- Pay only for the compute time consumed
Pricing Options
Beam
Google Cloud
On-Demand
Pay for compute capacity per hour or per second, with no long-term commitments.
Sustained Use Discounts
Automatic discounts for running instances for a significant portion of the month.
Committed Use Discounts
Save up to 57% with a 1-year or 3-year commitment to a minimum level of resource usage.
Preemptible VMs
Save up to 80% for fault-tolerant workloads that can be interrupted.
Getting Started
Beam
- 1
Create an account
Sign up on the Beam platform
- 2
Set up Python environment
Create a virtual environment and install the Beam SDK
- 3
Authenticate with API token
Configure your API token to connect to Beam
Google Cloud
- 1
Create a Google Cloud project
Set up a project in the Google Cloud Console.
- 2
Enable billing
Set up a billing account to pay for resource usage.
- 3
Choose a compute service
Select Compute Engine, GKE, Cloud Functions, or Cloud Run based on your needs.
- 4
Create and configure an instance
Launch a VM instance, configure a Kubernetes cluster, or deploy a function/application.
- 5
Manage resources
Use the Cloud Console, command-line tools, or APIs to manage your resources.
Support & Global Availability
Beam
Google Cloud
Global Regions
40+ regions and 120+ zones worldwide.
Support
Role-based (free), Standard, Enhanced and Premium support plans. Comprehensive documentation, community forums, and training resources.
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