
Beam offers a serverless inference platform for AI products, enabling users to run any workload with sub-second container starts and zero idle GPU costs. The platform provides elastic scaling, built-in retries, and no timeouts, allowing developers to simply bring their code while Beam handles the infrastructure. Key features include deploying serverless endpoints with authentication, autoscaling, logging, and task tracking, as well as running task queues and custom containers. Beam supports various use cases like hosting Streamlit and Gradio apps, running Jupyter notebooks, and handling diverse programming languages and frameworks. The platform emphasizes a fast developer experience, enabling quick deployment of models on GPUs with minimal code, and offers features like GPU switching, local debugging, multiple workers per container, importing remote Dockerfiles, and deploying from GitHub Actions. Beam aims to be a production-grade infrastructure layer for AI applications, backed by fast support and comprehensive logging and monitoring.

Beam offers a serverless inference platform for AI products, enabling users to run any workload with sub-second container starts and zero idle GPU costs. The platform provides elastic scaling, built-in retries, and no timeouts, allowing developers to simply bring their code while Beam handles the infrastructure. Key features include deploying serverless endpoints with authentication, autoscaling, logging, and task tracking, as well as running task queues and custom containers. Beam supports various use cases like hosting Streamlit and Gradio apps, running Jupyter notebooks, and handling diverse programming languages and frameworks. The platform emphasizes a fast developer experience, enabling quick deployment of models on GPUs with minimal code, and offers features like GPU switching, local debugging, multiple workers per container, importing remote Dockerfiles, and deploying from GitHub Actions. Beam aims to be a production-grade infrastructure layer for AI applications, backed by fast support and comprehensive logging and monitoring.
Product: Serverless inference platform for AI with sub-second container starts and zero idle GPU costs
Stage / Funding (evidence): Total disclosed funding: $3,500,000 (last funding date 2022-05-10)
Team size (evidence): 6 employees
Use cases: Deploy serverless endpoints, task queues, host apps (Streamlit/Gradio), run notebooks, custom containers
Infrastructure for deploying and running GPU-backed AI workloads and serverless inference
Cloud infrastructure / AI tooling
3500000.00