The Best Merlin Alternatives in 2026

Eurybia tracks model drift and validates data during machine learning model deployment.

MMDeploy provides a model deployment framework from the OpenMMLab project.

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

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.

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

Kubeflow provides a machine learning toolkit for deploying workflows on Kubernetes.

Package, configure, and iterate on a model with Truss at whatever level of control your model needs.

The fenn Python framework supports machine learning workflows and LLM agents, with trainers, templates, logging, and configuration management.

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

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

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

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

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

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

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

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

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

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

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.

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

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

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