The Best Replicate Alternatives in 2026

Cortex provides cloud infrastructure for deploying, managing, and scaling machine learning models in production.

PyTorch Lightning supports pretraining and fine tuning AI models across GPU setups.

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

FLAML provides a fast Python library for automated machine learning and tuning.

Keras provides a deep learning API for building and training machine learning models.

MLOps framework to package, deploy, monitor and manage thousands of production machine learning models.

Bodywork deploys machine learning pipelines to Kubernetes and handles containers and orchestration.

Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments.

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

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

Curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning.

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

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

Caffe provides an open framework for building and running deep learning models.

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

Ludwig provides a low code framework for building custom LLMs, neural networks, and other AI models.

The open source Feast feature store lets teams define, manage, discover, and serve features for machine learning.

Manifest routes language model requests from agents to providers, with subscription, usage-based, and local model options.

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

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

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

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

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

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