Key features
- Decorator deployment: `@endpoint` turns APAC Python function into GPU REST API
- Task queues: APAC async batch processing with auto-scaling GPU workers
- Volume caching: APAC model weight persistence between invocations
- No Docker: APAC teams declare dependencies in Python; Beam handles containers
- GPU types: A10G, A100, T4 for APAC inference and training workloads
- Pay-per-use: APAC billing only during active GPU execution, not idle time
Best for
- APAC ML engineering teams who need to deploy Python-based ML models and batch jobs on GPU without DevOps overhead — particularly APAC teams moving from Jupyter notebooks to production endpoints who want to avoid Kubernetes configuration.
Limitations to know
- ! Smaller APAC ecosystem and community than Modal or AWS SageMaker
- ! US-based — APAC data residency requirements may limit use for sensitive workloads
- ! Niche positioning — APAC teams may find Modal or SageMaker more mature for production
About Beam
Beam is a serverless ML deployment platform for APAC Python ML teams — providing GPU-backed function deployment, REST API endpoints, and scheduled batch jobs without requiring Kubernetes, Docker Compose, or cloud provider configuration. APAC ML engineers use Beam to move from a working Python ML script to a production endpoint in minutes rather than days.
Beam's deployment model wraps APAC Python functions with environment declarations — specifying Python version, pip requirements, and GPU type in a Beam `Image` and applying a `@endpoint` or `@task_queue` decorator creates a production-ready APAC API or batch processor. APAC ML teams do not write Dockerfiles, configure load balancers, or manage auto-scaling policies.
Beam supports task queues for APAC batch ML workloads — a fine-tuning or batch inference queue accepts tasks via API, processes them on GPU workers that auto-scale based on queue depth, and stores results in Beam's output storage. APAC ML teams building asynchronous inference pipelines (overnight batch classification, scheduled data processing) use task queues without managing their own APAC worker infrastructure.
Beam's data volumes persist between APAC function invocations — APAC ML models loaded from Hugging Face Hub on first invocation are cached in the Beam volume and loaded from cache on subsequent APAC invocations, eliminating repeated APAC model download latency. This model caching pattern makes Beam cost-effective for APAC endpoints where cold starts would otherwise download multi-GB model weights.
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