Alternatives

The Best FedML Alternatives in 2026

20 alternatives1 free · 19 open source

Looking for something like FedML? Here are 20 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 1 are free or have a free plan and 19 are open source.

About FedMLgithub.com/FedML-AI/FedML
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FedML supports distributed model training, model serving, and federated learning, with a scheduler for AI jobs across cloud and local clusters.

PricingNot confirmed
Platforms—

Top picks at a glance

Quick comparison

#AlternativePricingOpen sourcePlatforms
1GraphPipe —Not statedWeb
2Kserve —Yes—
3KFServing —Yes—
4PowerInfer —Yes—
5Triton Inference Server —Yes—
6Jina —Yes—
7Aphrodite Engine —Yes—
8Shimmy —Yes—
9bitsandbytes —Yes—
10libonnx —YesLinux
20 shown

All FedML alternatives

2

Kserve

github.com/kserve/kserveOpen source
Also covers deployment, machine learning, and open source · same kind of open-source project

Deploy generative and predictive AI models on Kubernetes with KServe’s distributed inference platform.

3

KFServing

github.com/kubeflow/kfservingOpen source
Also covers deployment, machine learning, and open source · same kind of open-source project

KServe provides distributed inference for generative and predictive AI models on Kubernetes.

6

Jina

github.com/jina-ai/jinaOpen source
Also covers machine learning and open source · same kind of open-source project

Jina Serve helps developers build multimodal AI applications with cloud-native tools.

8

Shimmy

github.com/Michael-A-Kuykendall/shimmyOpen source
Also covers machine learning and open source · same kind of open-source project

A Rust inference engine for GGUF models, Shimmy uses WebGPU and supports the OpenAI API.

10

libonnx

github.com/xboot/libonnxOpen source
Also covers machine learning and open source · same kind of open-source project

A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.

11

MindSpore

github.com/mindspore-ai/mindsporeOpen source
Also covers open source · same kind of open-source project

MindSpore provides a deep learning framework for training and inference across mobile, edge, and cloud scenarios.

12

GPUStack

github.com/gpustack/gpustackOpen source
Also covers open source · same kind of open-source project

GPUStack manages GPU clusters for serving AI models and provides on demand GPU instances accessible through SSH.

14

OpenLLM

github.com/bentoml/OpenLLMOpen source
Also covers open source · same kind of open-source project

OpenLLM runs open-source language models as cloud API endpoints compatible with OpenAI clients.

15

Flux

github.com/black-forest-labs/fluxFreemiumOpen source
Also covers open source · same kind of open-source project · free plan

Run image model inference with FLUX through the official repository from Black Forest Labs.

16

LLMKube

github.com/defilantech/LLMKubeOpen source
Also covers open source · same kind of open-source project

LLMKube runs self hosted LLM inference across heterogeneous GPU fleets using Kubernetes and several model runtimes.

17

ANEE

github.com/abkmystery/ANEEOpen source
Also covers open source · same kind of open-source project

ANEE wraps pretrained transformer inference with an energy budget and early exits inside model execution.

19

NobodyWho

github.com/nobodywho-ooo/nobodywhoOpen source
Also covers open source · same kind of open-source project

NobodyWho runs language models locally through an inference engine designed for efficient operation across devices.

No alternatives match this filter.

Alternatives to similar tools

All alternatives
What is the best alternative to FedML?
Based on features, tags and platforms, the closest alternatives to FedML are GraphPipe, Kserve, and KFServing.
Is there a free alternative to FedML?
Yes. Flux is free or offer a free plan.
Are there open-source alternatives to FedML?
Yes: Kserve, KFServing, PowerInfer, and Triton Inference Server.

Alternatives are suggested from the purpose and features described in our listings. Different pages for the same product appear once. Read each description to check whether it fits your task. How we choose alternatives · Suggest an alternative