Beam

Overview

Beam is a serverless GPU platform that makes it easy to run ML workloads in the cloud. The platform provides on-demand access to GPUs with automatic scaling, allowing developers to run training jobs, inference, and batch processing without managing infrastructure. Beam emphasizes simplicity and developer experience.

The platform is designed for developers who want GPU access without the complexity of Kubernetes or cloud configuration. Beam handles provisioning, scaling, and monitoring, letting developers focus on their ML code.

Key Features

**Serverless GPUs**: On-demand GPU access

**Python Decorators**: Deploy with simple decorators

**Auto-Scaling**: Scale to zero automatically

**Task Queue**: Distributed task processing

**Persistent Storage**: Shared storage volumes

**Webhooks**: HTTP endpoints for models

**Monitoring**: Built-in observability

**Multiple GPU Types**: A10, A100, H100

When to Use Beam

Beam is ideal for:

ML training and fine-tuning

Batch inference workloads

Data processing with GPUs

Developers wanting simple GPU access

Rapid prototyping on GPUs

Applications with variable GPU needs

Pros

Very simple to use

Python decorator-based deployment

Pay only for what you use

Good for ML workloads

Free tier available

Fast deployment

Modern developer experience

No Kubernetes required

Cons

Python-focused

Newer platform

Limited enterprise features

Smaller than major platforms

Documentation still growing

Some features in beta

Vendor lock-in

Less suitable for complex workflows

Pricing

**Free**: $10 credit monthly

**Usage-Based**: Pay per second of compute

**GPU Pricing**: Varies by type (~$1-3/hour)

**No Monthly Fees**: Pure pay-as-you-go