Beam vs Theta EdgeCloud

Only charged when your code runs, no charges for cold starts or server spin-up

Memory snapshots and GPU checkpoint restore bring containers back in seconds, which Beam reports as up to 35x faster than a traditional cold boot

Run untrusted code safely in isolated environments for AI agents and code interpreters

Pause and resume sessions while maintaining filesystem, memory, and running processes

Scale to zero when idle, burst to thousands of containers in seconds

Bring your own Docker images for full environment control, including running the Docker daemon inside containers

Combines traditional cloud GPUs via Google Cloud and AWS with 30,000+ community-operated edge nodes for flexible compute.

Supply-demand driven pricing where node operators set rates and users select GPUs based on availability and cost.

Routes heavy training jobs to cloud/datacenter GPUs and distributes parallelizable inference across edge nodes.

Reroutes jobs to healthy nodes if a community node goes offline mid-task, ensuring workload continuity.

Docker-based job execution with Jupyter Notebook and SSH access for flexible development environments.

On-demand model inference APIs and dedicated AI model serving with support for popular generative AI models.