Beta9 is an open-source serverless runtime designed for deploying and scaling AI workloads with minimal infrastructure management. Built in Go, it provides a Pythonic interface to run GPU-accelerated inference endpoints, interactive sandboxes, and background tasks without managing servers. It’s ideal for developers and ML engineers who want to deploy LLMs, fine-tuned models, or data processing pipelines without the operational burden of Kubernetes or cloud VM management. Beta9 powers the commercial Beam platform and can be self-hosted for free, offering a seamless path from local experimentation to production-scale AI deployment.
What You Get
- Fast Image Builds - Launch containers in under a second using a custom container runtime optimized for AI workloads, reducing deployment latency significantly compared to traditional Docker-based systems.
- Parallelization and Concurrency - Fan out AI inference or data processing tasks across hundreds of containers automatically, enabling high-throughput workloads like batch image generation or real-time video analysis.
- First-Class Developer Experience - Features include hot-reloading during development, webhooks for event-driven triggers, and scheduled jobs for recurring tasks, improving iteration speed and workflow integration.
- Scale-to-Zero - Workloads automatically scale to zero when idle, eliminating costs during periods of inactivity—ideal for low-traffic endpoints or experimental models.
- Volume Storage - Mount distributed storage volumes to persist data across container restarts, supporting use cases like model checkpoints, dataset caching, or user uploads.
- GPU Support - Run workloads on cloud GPUs (NVIDIA 4090, H100) or bring your own GPU hardware with built-in CUDA compatibility for fine-tuning and inference tasks.
Common Use Cases
- Building a multi-tenant SaaS dashboard with real-time LLM inference - Deploy an autoscaling endpoint that handles thousands of concurrent user queries to a fine-tuned LLM, with automatic scaling based on queue depth and GPU utilization.
- Creating a mobile-first e-commerce platform with 10k+ SKUs and image-based search - Use Beta9’s background task system to process and embed product images at scale, then serve embeddings via a serverless inference endpoint for similarity search.
- Problem: Slow model deployment cycles → Solution: Hot-reload and sandboxed testing - Developers test LLM-generated code in isolated sandboxes before deploying to production, reducing bugs and accelerating iteration without managing VMs or containers manually.
- DevOps teams managing microservices across multiple cloud providers - Self-host Beta9 on-premises or in hybrid clouds to maintain control over GPU resources while using the same Python API as the managed Beam platform.
Under The Hood
Beam Cloud is a serverless computing platform designed to simplify function deployment and execution using Kubernetes-native infrastructure and a modular architecture. It enables developers to run code in a distributed, scalable environment with minimal infrastructure concerns.
Architecture
Beam Cloud adopts a microservices-oriented architecture that emphasizes modularity and clear separation of concerns across its components.
- The system is organized into distinct modules such as gateway and worker, each with dedicated entry points and deployment configurations
- gRPC is used extensively for inter-service communication, enabling efficient and reliable distributed interactions
- The SDK layer abstracts core service access and includes robust error handling for resilient client-side operations
- Deployment strategies leverage Kubernetes manifests and cloud-native tooling to support both local and cloud environments
Tech Stack
Built primarily in Go, Beam Cloud leverages modern infrastructure and communication technologies to support scalable, distributed systems.
- The platform is implemented in Go with gRPC services and Kubernetes-based deployment patterns for cloud-native execution
- Key dependencies include Kubernetes manifests, AWS and Fly.io integrations, and gRPC for service communication
- Development workflows are supported by Makefiles, Docker containers, and Helm charts for orchestration
- Testing includes end-to-end capabilities and Python-based automation scripts for deployment validation
Code Quality
The codebase demonstrates a mixed quality profile with structured components but inconsistent testing and documentation practices.
- Modular design shows clear separation of concerns, though some modules exhibit technical debt and uneven code conventions
- Error handling is implemented in key areas but lacks consistency across the entire system
- Type annotations are present, contributing to improved code clarity and maintainability in some parts
- Test coverage is limited, with only basic end-to-end validation and no comprehensive unit or integration tests
What Makes It Unique
Beam Cloud stands out through its innovative blend of Kubernetes-native deployment and simplified developer experience.
- It introduces a streamlined SDK that abstracts infrastructure complexity while maintaining low-level control over function execution
- The use of gRPC-based communication and containerized worker patterns enables efficient, scalable task distribution
- Multi-cloud deployment strategies and support for diverse infrastructure providers offer flexibility not commonly found in similar platforms
- The platform’s focus on developer-centric APIs and simplified function deployment differentiates it from traditional serverless offerings