Best AI tools for hosted fine tuning

Together AI

Featured

Fine-tuning · Llama / Mistral / Qwen / DeepSeek and others

8.6

Fine-tune & serve open-weight models (Llama, Mistral, DeepSeek).

Modal

Fine-tuning · Infrastructure (any model you can host)

8.7

Serverless GPUs and infra for training & serving ML.

Replicate

Fine-tuning · Thousands of community + first-party models

8.5

One-API platform for running and fine-tuning open-source models.

Llama

Fine-tuning · Llama 4 (Maverick, Scout), Llama 3.3/3.2/3.1

8.3

Meta's open-weight LLM family covering 1B mobile models up to 405B frontier and natively multimodal 10M-context Llama 4 variants.

RunPod

Fine-tuning · Bring-your-own (any open-weight or custom model)

8.3

On-demand GPU cloud and serverless inference platform built specifically for AI workloads.

vLLM

Fine-tuning · Multi-model (open-weight LLMs: Llama, Qwen, DeepSeek, Mistral, Gemma, Phi, etc.)

8.3

Open-source high-throughput inference engine for serving LLMs with PagedAttention and continuous batching.

CoreWeave

Fine-tuning · DeepSeek

8.2

AI-native GPU cloud built for large-scale training, fine-tuning, and inference on NVIDIA hardware.

Ludwig

Fine-tuning · Multi-model (PyTorch + HuggingFace Transformers)

8.2

Declarative, YAML-driven deep learning framework for fine-tuning LLMs and multi-modal models without writing training loops.

OpenPipe

Fine-tuning · Llama, Mistral, Qwen and other open-weight base models

8.2

Fine-tuning and reinforcement learning platform for turning expensive prompts into cheap, fast, task-specific models.

Unsloth

Fine-tuning · Llama, Mistral, Gemma, Qwen, GLM (multi-model)

8.2

Open-source LLM fine-tuning toolkit with custom kernels that train 2-30x faster and use up to 90% less VRAM.

Hugging Face AutoTrain

Fine-tuning · Multi-model (Hugging Face Hub)

8.1

No-code fine-tuning and training pipeline that spins up state-of-the-art models on the Hugging Face Hub.

Lambda

Fine-tuning · NVIDIA VR200 NVL72, NVIDIA GB300 NVL72, NVIDIA HGX B200, NVIDIA HGX B300, NVIDIA H100

8.1

On-demand NVIDIA GPU cloud built specifically for training, fine-tuning, and serving large AI models.

Optuna

Fine-tuning

8.1

Open-source Python framework for automated hyperparameter optimization across any ML stack.

Ray Tune

Fine-tuning

8.1

Open-source Python library for distributed hyperparameter tuning at any scale.

Together AI Fine-tuning

Fine-tuning · Multi-model (any Hugging Face open-source model)

8.1

Managed fine-tuning platform for open-source LLMs and vision models with LoRA, full fine-tuning, and RL support.

Fireworks AI

Fine-tuning · Multi-model (DeepSeek, Qwen, GLM, Kimi, Gemma, Minimax, others)

7.9

Production inference and fine-tuning platform for open-source LLMs, tuned for speed and enterprise economics.

Lamini

Fine-tuning · Lamini (built on open base models)

7.7

Memory-tuning platform for grounding LLMs in your facts.

FedML

Fine-tuning · Bring-your-own (PyTorch, Hugging Face)

7.3

Distributed training, fine-tuning, and serving platform with federated learning roots.

LLaMA Factory

Fine-tuning · Multi-model (LLaMA, Mistral, Qwen, Gemma, Phi, LLaVA, ChatGLM, Yi)

7.2

Open-source, no-code WebUI for fine-tuning 100+ open LLMs with LoRA, QLoRA, DPO, and PPO.

Paperspace Gradient

Fine-tuning · Bring-your-own (PyTorch, TensorFlow, Hugging Face)

7.2

End-to-end MLOps platform with GPU notebooks, training jobs, and model deployment, now folded into DigitalOcean.

H2O AutoML

Fine-tuning · H2O-3 (GBM, XGBoost, GLM, DRF, Deep Learning, Stacked Ensembles)

7.1

Open-source automated machine learning that handles feature engineering, model selection, and stacked ensembling out of the box.

W&B Sweeps

Fine-tuning · Multi-model (Llama, DeepSeek, Qwen, Kimi)

7.1

Hyperparameter optimization from Weights & Biases with Bayesian search and Hyperband early stopping.

Forefront

Fine-tuning · Multi-model (Mistral-7B, Mixtral, Phi-2)

7.0

Fine-tune and serve open-source LLMs on your own data without managing GPUs.

Velda

Fine-tuning

6.7

Serverless GPU orchestration that runs AI training and batch jobs without Docker or Kubernetes.

Colossal-AI

Fine-tuning · Framework-agnostic; used with LLaMA, GPT, Stable Diffusion, ViT, and other PyTorch-based open-weight models

Making large AI models cheaper, faster, and more accessible through distributed training

Language Model Builder

Fine-tuning · In-house small transformer models trained by the user; exports to safetensors

Learn how LLMs work by building one on your Mac

PyTorch Lightning

Fine-tuning · Framework-agnostic — trains any PyTorch model (transformers, CNNs, diffusion, RL nets, etc.)

The deep learning framework for professional AI researchers and ML engineers