The Best Xinference Alternatives in 2026

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

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

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.

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

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

Deploy a ML inference service on a budget in less than 10 lines of code.

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

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

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

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

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

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.

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

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

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

Torchvision provides datasets, transforms, and models for computer vision with PyTorch.

The cONNXr project provides a pure C runtime for ONNX models intended for embedded devices.

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

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

Flower provides a federated AI framework for machine learning projects.

Infer.NET runs Bayesian inference in graphical models for a range of machine learning problems.

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