ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.
Open source
24 alternativesHydrosphere
The Hydrosphere repository describes an MLOps platform for machine learning operations.
About Hydrosphere
Hydrosphere Serving is an open source cluster for deploying and versioning machine learning models in production. It wraps models built in any language or framework in Docker images and exposes them through HTTP, gRPC, and Kafka interfaces, with version control for models and pipelines and traffic shadowing between model versions.
It can be installed with Docker or Kubernetes, each with specific prerequisites for Docker, Compose, Helm, and Kubernetes versions.
Key features
- Deploy and version machine learning models in production
- Serve models built in any language or framework
- Wrap models in Docker images for cluster deployment
- Expose models through HTTP, gRPC, and Kafka interfaces
- Shadow traffic across model versions for comparison
- Version control models and pipelines as deployed
Best for: Teams deploying and versioning machine learning models on their own Docker or Kubernetes clusters.
Good for
- Open source under Apache-2.0 license
- Installs on Docker or Kubernetes clusters
- Serves framework-agnostic models via HTTP, gRPC, and Kafka
Watch out for
- Docker setup requires Docker 20+ with BuildKit and Compose 1.29+
- Kubernetes setup requires Helm 3.0+ and Kubernetes 1.16+
Hydrosphere at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Hydrosphere FAQ
How can Hydrosphere Serving be installed?
It can be installed with Docker or with Kubernetes using Helm.
What interfaces do deployed models expose?
Deployed models expose HTTP, gRPC, and Kafka interfaces.
What is needed for a Docker-based install?
Docker 20+ with BuildKit enabled and Docker Compose 1.29+.
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