Beam
Deploy AI workloads instantly with serverless GPU infrastructure
Target Audience
- AI Developers
- ML Engineers
- DevOps Teams
- Startup CTOs
Hashtags
Social Media
Overview
Beam simplifies cloud AI development by handling infrastructure complexities so developers can focus on building. Get instant access to GPUs, automatic scaling, and production-ready APIs with just a few lines of Python. It's like having a DevOps team in your pocket for machine learning projects.
Key Features
GPU Autoscaling
Automatically scales from 0 to 300 containers based on demand
Hot Reloading
Switch hardware configurations with one line of Python
Production APIs
Deploy ready-to-use APIs with built-in auth and metrics
Distributed Storage
Globally accessible volumes for model weights/data
CI/CD Integration
Deploy directly from GitHub Actions pipelines
Use Cases
Deploy Hugging Face models in production
Scale real-time inference APIs automatically
Debug AI models with production-like environment
Reduce cloud costs with pay-per-use GPUs
Migrate from SageMaker/Vertex AI
Pros & Cons
Pros
- Eliminates DevOps overhead for GPU management
- Substantial time savings in deployment (minutes vs hours)
- Cost-effective scaling vs traditional cloud providers
- Active Slack community with fast support
Cons
- Primarily Python-focused (limited language support)
- Requires cloud development familiarity
Frequently Asked Questions
Is there a free tier?
Yes, you get 15 hours of free GPU credit to start
What GPUs are available?
NVIDIA A100-40GB GPUs are explicitly mentioned
Can I use other programming languages?
Primarily supports Python through their SDK
Integrations
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