The Best Hydrosphere Alternatives in 2026

ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.

Cog packages machine learning models into containers to support deployment across environments.

ModelDB tracks machine learning model versions, metadata, and experiments.

Flexible, high-performance serving system for machine learning models.

Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.

DJL is a Java framework for running deep learning models across different engines.

dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.

The Feast repository describes an open source feature store for AI and machine learning projects.

PrimeHub offers an open-source platform for managing machine learning operations and workflows.

MLRun provides an open-source MLOps platform for managing machine learning applications, pipelines, and CI/CD delivery.

DeepDetect provides a deep learning server and command-line interface for Torch and TensorRT.

Apache SINGA supports distributed deep learning through a system and library for model training.

Sagify describes its project as a way to work with large language models and machine learning more easily.

Machine learning operations and large language model operations are the focus of Starwhale’s platform.

The MegEngine framework supports deep learning and automatic differentiation, according to its repository description.

ClearML manages AI experiments, data, pipelines, orchestration, scheduling, and model serving in one MLOps platform.

Cascade supports MLOps workflows for smaller projects without a full platform.

Lucent brings the Lucid library approach to PyTorch, according to its repository description.

MiroShark describes a swarm intelligence engine for simulating scenarios with stated time and cost targets.

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

TPOT uses genetic programming to optimize machine learning pipelines in Python.

Merlin supports machine learning model management, deployment, and serving in Kubernetes environments.

The Datumbox framework is written in Java for developing machine learning and statistical applications.

Build neural networks and support machine learning research with Sonnet, a library based on TensorFlow.