Updated 2021-02-21. Note, that I have mainly experience with Cortex and BentoML view is based on cursory overview of their documentation.
Common
- both deploy and serve models via API
- both support major ML frameworks (TensorFlow, Pytorch)
- both have good documentation
Comparison
| Feature | BentoML.org | Cortex.dev |
|---|---|---|
| Language | fully Python - easier to modify? | Go & Python wrapper (Updated) |
| Deployment | Delegated to other tools: Docker, Kubernetes, AWS Lambda, SageMaker, GCP, Azule ML and more. | Works currently only with local Docker and AWS EKS, GCP (Updated) |
| Service configuration | from Python | from Python (Updated) |
| Service packaging and distribution | Can be packaged, saved via Python command to management repository with a web dashboard or PyPI | Packaging only via Docker images without explicit support |
| Horizontal scaling | Configured separately in other clustering tools. Working on an opinionated Kubernetes deployment. | Configurable in Cortex. May be less flexible (private cloud deploy may require custom scripts) |
| User interface | CLI, Web UI | CLI |
| Metrics | Prometheus metrics | Prometheus metrics (Updated) |
| API Auto-Docs | Swagger/OpenAPI | N/A |
| User support | Responsive unpaid Slack Channel, but Slack is not the best tool for support | Very responsive Gitter and now Slack |
| Suggest anything else? |
My Experience with Cortex
Here is a blog post on Cortex use at GLAMI. It is a bit outdated take as Cortex now has its own wrapper. Consider using this Cortex client for Python, which is a Python wrapper around Cortex CLI that we use at GLAMI for MLOps. It has couple of extra features, that keep us using it for now.