The Best Keras Alternatives in 2026

QKeras provides quantization tools for deep learning with TensorFlow Keras.

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

Replicate provides a cloud API for running and fine-tuning open machine learning models and deploying custom models.

DyNet provides tools for building dynamic neural networks and deep learning models.

NVIDIA NIM offers APIs for trying models used to build enterprise generative AI applications.

ModelFox supports training, deployment, and monitoring for machine learning models.

Z.AI’s developer documentation explains how to access its models and services through an API.

Snowgoose provides access to models from OpenAI, Anthropic, Google, and other providers in one AI workspace.

Synexa AI deploys AI models through an API, with examples for image generation, video generation and image restoration.

List of awesome compiler projects and papers for tensor computation and deep learning.

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

Hopsworks provides a feature store and MLOps platform for building and operating production machine learning systems.

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

ThinkDiffusion provides cloud workspaces for Stable Diffusion, ComfyUI, Flux, and other image-generation software.

The SpaceXAI API console offers developer tools for Grok text, voice, and image generation APIs.

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

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

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

This repository contains machine learning code examples that use Comet.ml.

Gradio creates web interfaces for machine learning models in Python so developers can share interactive demos and apps.

DeepLake provides multimodal data infrastructure for AI agents, including retrieval and training.

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

PyTorch TNT supplies lightweight tools and utilities for training with PyTorch.

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