The Best Unsloth Alternatives in 2026

9Router routes AI requests across providers with automatic fallback and OpenAI-compatible endpoints.

SpaceXAI’s Grok API documentation covers chat, image and video generation, voice, and structured outputs.

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

Ollama runs language models locally and provides tools to work with model files and APIs.

Flama serves predictive and generative AI models as APIs, with compatible endpoints, a chat interface, and MCP support.

Unsloth Studio provides a local workspace for running and training AI models.

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

txtai combines semantic search, language-model orchestration and AI workflows in an open-source framework.

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

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

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

ONNX-MLIR compiles ONNX models using MLIR compiler infrastructure for model deployment workflows.

OpenLIT provides observability and evaluation for AI agents and coding agents, tracing models, tools, prompts, costs, and workflows.

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

Rockpool supports training spiking neural networks with PyTorch and JAX and deploying them to neuromorphic hardware.

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

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

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

Kitaru provides agent traces that teams can run and inspect during application workflows.

Kale simplifies data science workflows for deploying Kubeflow Pipelines.

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

Pachyderm provides data-focused pipelines and version control for datasets and related workflows.

The fenn Python framework supports machine learning workflows and LLM agents, with trainers, templates, logging, and configuration management.