#184
B
Rank #150
BentoML
A Python framework for packaging, serving, and scaling ML models as production APIs, with first-class support for building deployable containers ('Bentos'). Its standout trait is treating containerization as a core build artifact rather than an afterthought.
65.8
/ 100
⚙ Full Stack
Python serving runtime with an ASGI (Starlette-based) HTTP layer, model registry stored on local disk or cloud object storage, and a `bentofile` build spec that produces a self-contained Docker image per model service.
📈 Scaling Analysis
Scales well horizontally for stateless inference by running multiple replicas of a built Bento behind a load balancer, and supports adaptive batching to improve GPU utilization, but multi-model orchestration and autoscaling still require an external layer like Kubernetes HPA or Yatai.
🚀 Running on Nexlayer
Deploy the built Bento image as its own pod (`bentoml-service.pod:3000`); for GPU inference request a GPU nodepool. There's no required database — model artifacts and the registry can live on a mounted volume, keeping the deployment to a single pod for most use cases.