The Best TFServing 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.

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

The Hydrosphere repository describes an MLOps platform for machine learning operations.

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.

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

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

FlexML offers an easy to use and flexible automated machine learning library for Python.

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

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

The goro project provides a high level machine learning library written for Go.

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

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

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

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.

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

FastChat offers an open platform for training, serving, and evaluating large language models, and hosts release repositories for Vicuna and Chatbot Arena.

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.

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

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