The Best ClearML Alternatives in 2026

ClearML combines experiment and data management, pipelines, scheduling, orchestration, and model serving for AI workloads.

Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.

BentoML helps build and serve AI apps and models through inference APIs, job queues, and multi-model pipelines.

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

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

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

QKeras provides quantization tools for deep learning with TensorFlow Keras.

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

FATE supports federated learning through an industrial grade framework.

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

The fuku-ml project provides a machine learning library described as simple to use.

Sinabs supports fast training of spiking neural networks with PyTorch and inference on neuromorphic hardware.

PyTorch Ignite provides a high level library for flexibly training and evaluating neural networks.

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

Kedro-Viz visualizes Kedro data and machine learning pipelines and tracks experiments.

Kedro helps create reproducible, maintainable, and modular data science and data engineering pipelines.

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

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

Kale simplifies data science workflows for deploying Kubeflow Pipelines.

Pathway offers cloud templates for RAG, AI pipelines, and enterprise search that stay synchronized with connected data sources.

Feature Stores for ML shares information and knowledge about feature stores and their role in machine learning pipelines.

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

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

Flama serves predictive and generative AI models as APIs, with compatible endpoints, a chat interface, and MCP support.