Chassis

Open-source tool that auto-packages ML models into production-ready Docker containers with a prediction API.

Free· Free, open source (Apache-style community project)Agents

Best for

Pick Chassis if you have a trained Python ML model and need a reproducible container with a prediction API ready for cloud, on-prem, or edge.

Skip if

Skip it if you need training, fine-tuning, or evaluation tooling, or if your stack already standardizes on KServe, BentoML, or Seldon.

Chassis is an open-source Python SDK that takes a trained ML model and emits a containerized prediction service with one function call. It supports the usual framework zoo (Scikit-learn, PyTorch, TensorFlow) and compiles for both x86 and ARM, so the same model can ship to a Kubernetes cluster, a Raspberry Pi, or an NVIDIA Jetson without re-wrapping the dependency hell each time.

The target user is the MLOps-light data scientist who can write Python and `pip install chassisml`, but doesn't want to hand-author a Dockerfile, a gRPC server, or a Kubernetes manifest just to expose a model. It is fully free and community-maintained under the modzy/chassis GitHub org, with Modzy as the original commercial sponsor; deployment to Modzy's platform is one of several supported targets (containerd, Docker, Kubernetes are all first-class).

Worth noting: Chassis is plumbing, not a model. It does not train, fine-tune, or evaluate; it solves the unglamorous packaging step. The project's release cadence has been quiet (v1.5 has been the current major for a while), so treat it as a stable utility rather than an aggressively evolving platform.

Editor's take

Chassis sits in a useful niche: the boring final mile between a notebook and a deployable container. The Python-only ergonomics are genuinely nice, and edge-target support is unusually thorough. Just go in knowing it's a packaging tool with modest momentum, not a competitor to BentoML's broader serving story.

— The AI Tool Bible editorial team