AI fine-tuning platform

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

Serverless GPUs and infra for training & serving ML.

Build and scale generative AI applications with foundation models

Google Cloud's unified platform for building, deploying, and scaling enterprise AI agents and models.

Enterprise AI platform for building, deploying, and governing models and agents

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

Fine-tune GPT-4o-mini and friends on your own data.

Foundation models from 01.AI — open-weight Yi family plus frontier Yi-Lightning and Yi-Large

End-to-end MLOps and GenAI platform with open-source experiment tracking and enterprise GPU orchestration.

Enterprise AI platform unifying data, ML, LLMs, and agents under one governed workflow.

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

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

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

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

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

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

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

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

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

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

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

End-to-end platform for training and deploying ML models on microcontrollers, sensors, and other edge hardware.

Ray-powered platform for training, serving, and scaling LLMs.

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

Memory-tuning platform for grounding LLMs in your facts.

Microsoft's open-source trainer that fine-tunes AI agents with RL and prompt optimization, framework-agnostic.

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

Open-source toolkit for running the full ML lifecycle on Kubernetes.

Kubernetes-native data versioning and pipeline engine for reproducible ML at petabyte scale.

MLOps platform for versioned pipelines, distributed training, and LLM evaluation across any cloud.

Enterprise AI platform for building, deploying, and governing models and agents at scale.

Open-source workbench for building, evaluating, and fine-tuning AI agents across 190+ models.

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

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

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

Enterprise MLOps and GenAI platform for taking models from notebook to production at scale.

Infrastructure-agnostic MLOps platform for the full ML/DL lifecycle across public, hybrid, and on-prem clouds.

Enterprise agent platform from Scale AI that connects your data, orchestrates multi-agent workflows, and learns from human feedback inside your own VPC.

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

AWS's end-to-end platform for building, training, and deploying machine learning models and AI agents at enterprise scale.

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

Apache-licensed distributed deep learning library focused on scalable training across GPUs and nodes.

GitHub-style collaboration platform for ML datasets, experiments, and models with MLflow and DVC under the hood.

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

An open-source optimization layer that sits between your app and Ollama to squeeze more performance out of local LLMs.

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

The deep learning framework for professional AI researchers and ML engineers