Beam Cloud
On-demand GPU infrastructure for serverless AI inference and sandboxes.
Pricing
About Beam Cloud
Beam is a serverless GPU computing platform that enables developers to run AI workloads on-demand without managing infrastructure. It provides access to GPUs (including 4090s and H100s) for running inference, task queues, and long-running AI agent sandboxes with sub-second cold starts and zero rate limits. The platform integrates logic and hardware through a Python SDK, eliminating the need for YAML, Dockerfiles, or manual infrastructure management. Beam targets machine learning engineers and AI companies who need flexible compute scaling without vendor lock-in. The platform is distinctive for its stateful sandboxes that support persistent storage, Docker-in-Docker capabilities, and the ability to bring your own cloud infrastructure. A notable limitation is that the service relies on external knowledge bases for comprehensive guidance—the documentation may require ongoing reference during development.
At a glance
- Company
- Smartshare, Inc.
- Platforms
- Web, API, Python SDK, GitHub Actions
- API
- Available
- Integrations
- GitHub Actions, AWS, GCP
- Last verified
- July 2026
Who It's For
- •Machine learning engineers and AI developers
- •AI startups and companies building with large language models
- •Teams requiring GPU compute without infrastructure management
- •Developers needing flexible, pay-as-you-go GPU resources
How It Works
- 1Users define GPU workloads in Python using the Beam SDK with decorators to specify hardware and configuration
- 2Code is deployed to Beam's distributed infrastructure or user's own cloud (AWS, GCP, bare metal)
- 3Sub-second cold starts are achieved through distributed storage layers, memory snapshots, and GPU checkpoint restoration
- 4Automatic scaling manages workload distribution across containers based on queue depth or defined thresholds
- 5Results are returned via callbacks, logging, and monitoring systems integrated into the platform
How to Use Beam Cloud
- 1Install the Beam Python SDK
- 2Define functions with GPU specifications using Beam decorators (@endpoint, @task_queue, @sandbox)
- 3Deploy code using 'beam deploy' command or GitHub Actions integration
- 4Monitor execution through built-in logging and observability tools
- 5Retrieve results via callbacks or direct API queries
Key Features
- •Serverless GPU inference with sub-second cold starts
- •Durable task queues with retries and callbacks
- •Stateful sandboxes with persistent storage and snapshots
- •Multi-cloud support (Beam-managed and bring-your-own cloud)
- •Python-based SDK with no YAML or Dockerfile requirements
- •Docker-in-Docker support in containers
- •Queue depth autoscaling to thousands of containers
- •Secrets management and comprehensive logging
- •GitHub Actions integration for CI/CD deployment
Use Cases
- •LLM inference serving at scale with dynamic autoscaling
- •Task queue processing with GPU acceleration for batch jobs
- •AI agent sandboxes for stateful, long-running environments
- •Image generation and ComfyUI workflows
- •Model fine-tuning and reinforcement learning environments
Pros & Cons
Advantages
- •Sub-second cold starts eliminate latency typical in serverless GPU computing
- •Bring-your-own-cloud option prevents vendor lock-in and allows flexible infrastructure strategy
- •Simple Python SDK removes infrastructure complexity compared to traditional cloud GPU services
- •Backed by Y Combinator with production focus and industry credibility
Disadvantages
- •Pricing starts at $0.69/hr for GPUs, which may add up quickly for sustained workloads compared to reserved instances
- •Limited information available about production-grade SLAs and uptime guarantees beyond SOC 2 Type II compliance
- •Ecosystem is smaller than established providers like AWS Lambda with GPUs or dedicated GPU services
Alternatives
See all Beam Cloud alternatives →Related Tools
View All Tools
Firecrawl
API platform for AI agents to search, scrape, and interact with web data.
Adept AI
AI research lab building intelligent agents for human-computer collaboration.
Alaya AI
Web3 AI data infrastructure for decentralized data sampling and auto-labeling.
Automorphic
Infrastructure platform for building self-improving custom language models.
Codenull AI
No-code AI platform for building custom AI models without coding
Datature
End-to-end computer vision AI platform for data labeling, model training, and deployment.
Reviews for Beam Cloud
Based on 0 reviews
Rating Distribution
No Reviews Yet
Be the first to share your experience with Beam Cloud!
Frequently Asked Questions
What is Beam Cloud?
Beam is a serverless GPU computing platform that enables developers to run AI workloads on-demand without managing infrastructure. It provides access to GPUs (including 4090s and H100s) for running inference, task queues, and long-running AI agent sandboxes with sub-second cold starts and zero rate limits.
How much does Beam Cloud cost?
Beam Cloud has paid plans starting at $0.00.
Is Beam Cloud free?
Beam Cloud is a paid tool and does not offer a free plan.
What are the best Beam Cloud alternatives?
Popular Beam Cloud alternatives include Cerebrium, Dify AI, Modal.
What is Beam Cloud used for?
Beam Cloud is commonly used for LLM inference serving at scale with dynamic autoscaling, Task queue processing with GPU acceleration for batch jobs, AI agent sandboxes for stateful, long-running environments.
Does Beam Cloud have an API?
Yes, Beam Cloud offers an API for developers.
What platforms does Beam Cloud support?
Beam Cloud is available on Web, API, Python SDK, GitHub Actions.
Information Accuracy
Please note: While we regularly update all tool information including descriptions, features, pricing, and other details, this information may change over time as tools evolve and update their offerings. For the most current and accurate information, we recommend visiting the official website directly. Our goal is to provide you with comprehensive and up-to-date information to help you make informed decisions.