Beam Cloud

Name: Beam Cloud GPU Cloud
Brand: Beam Cloud
Availability: InStock
Rating: 9.2 (228 reviews)

🤖 Managed Inference

Serverless Inference

🏢 San Francisco, CA, USA📅 Since 2021★ 9.2/10🌐 Website ↗

Avg Latency

<2 seconds (Cold Start)

Rate Limits

Auto-scaling

Free Tier

✓ Available

API Protocol

Standard REST API

The Developer’s Serverless GPU

Beam Cloud provides a radically simplified developer experience for deploying machine learning models. Instead of struggling with Kubernetes or Docker, developers write a simple Python function, add a decorator to specify the required hardware (e.g., an A10G or A100 GPU), and deploy it instantly via the Beam CLI. Beam automatically provisions the container, handles the dependencies, and returns a scalable REST API endpoint or Webhook.

Asynchronous Parallel Processing

Beam shines in workloads that require massive parallel execution. For example, if a developer needs to process 10,000 PDF documents through an OCR and LLM summarization pipeline, Beam allows them to fan out the workload across 1,000 concurrent serverless containers instantly. They only pay for the exact seconds the GPUs are active, making it highly cost-effective for sporadic, heavy workloads.

Volume Storage Integration

Handling large model weights (which can exceed 50GB) is a major pain point in serverless AI. Beam solves this natively by providing highly optimized networked Volumes. Developers can cache massive Hugging Face models directly in Beam’s storage layer, drastically reducing the cold-start times typically associated with serverless GPU platforms and ensuring rapid inference response times.

Supported Workloads

LLMVisionScrapingCustom Python

Pros & Cons

Pros

Incredible developer experience (deploy via CLI)
Massive scale-out for parallel processing
Generous free tier for prototyping

Cons

Requires packaging models in Python
Vendor lock-in to Beam's specific architecture

Served Models

BYOM

Data Privacy Policy

SOC 2 Type II

Standard REST API

Standard REST API. This provider uses a proprietary REST architecture with JSON payloads. You will need to use standard HTTP clients (e.g., fetch, axios, requests) to interact with their inference endpoints.

Quick Start Snippet

Python

import requests
headers = {
 'Authorization': 'Bearer YOUR_API_KEY',
 'Content-Type': 'application/json'
}
data = {
 'model': 'your-chosen-model',
 'prompt': 'Hello, world!'
}
response = requests.post('https://beam.cloud/v1/completions', headers=headers, json=data)

View Official Documentation →

Website

Visit Official Site ↗

Billing Model

Per-second billing

You are charged exclusively for the duration the GPU is actively processing your request. Excellent for bursty workloads.

Generous Free Tier Available

Start building without a credit card. Perfect for prototyping and testing the API before scaling into production workloads.

View Official Pricing Schedule →

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