The Best dstack Alternatives in 2026

Burn provides a flexible, efficient, and portable tensor library for building deep learning applications.

Fiber presents a simple approach to distributed computing for AI workloads.

Deep learning in Rust, with shape checked tensors and neural networks.

MLX provides an array computing framework designed for Apple silicon devices.

Model deployment for AI applications is supported by nndeploy, a framework described as easy to use and high performance.

MMDeploy provides a model deployment framework from the OpenMMLab project.

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

PAI schedules resources and manages clusters for AI workloads across distributed computing environments.

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

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

ONNX-MLIR compiles ONNX models using MLIR compiler infrastructure for model deployment workflows.

The Darknet neural network framework is written in C and CUDA and supports CPU and GPU computation.

Catniff provides JavaScript developers with a Torch-like deep learning framework.

DEAP provides a Python framework for prototyping evolutionary computation algorithms and testing new ideas.

The ChefBoost Python framework supports decision trees such as ID3, C4.5, CART, CHAID, regression trees, and ensemble methods.

DeepEval is an LLM evaluation framework, according to its repository description.

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

Synthetic data and AI feedback pipelines can be built with Distilabel, a framework based on research papers.

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

The linfa project provides a machine learning framework written in Rust.

PyToune provides a simplified framework and utilities for working with PyTorch.

PaddlePaddle provides a deep learning framework for training models across devices and platforms.

Hypernets offers a general automated machine learning framework for developing end to end AutoML toolkits in specific domains.

FATE supports federated learning through an industrial grade framework.