The Best Sagify Alternatives in 2026

Ray provides an AI compute engine with a distributed runtime and libraries for machine learning workloads.

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

This Docker-based starter kit provides a runtime for machine learning work in Jupyter notebooks.

The Feast repository describes an open source feature store for AI and machine learning projects.

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

OptiMate is a collection of libraries for optimizing AI model performance; the repository is marked as a legacy project.

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

traceAI provides open source tracing for AI applications and frameworks using OpenTelemetry.

Helicone provides open-source observability for LLM applications, with tools to monitor, evaluate, and experiment.

Upstage builds large language models and document-processing engines for workplace workflows.

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.

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

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

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

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

PyToune provides a simplified framework and utilities for working with PyTorch.

Xinference runs language, speech, and multimodal models through a unified inference API across cloud, on premises, or laptop setups.

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

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

The Datumbox framework is written in Java for developing machine learning and statistical applications.

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

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