# AWS Opens SageMaker Training Sessions for Custom AI Model Workflows By Simon Yoon Canonical URL: https://www.tokenpost.com/news/technology/39071 Published: 2026-10-23T14:58:24.000Z Updated: 2026-10-23T14:58:24.000Z Section: Technology > The service is available in three AWS Regions and supports four base models, with token-based billing for prefill, sampling and training. Amazon Web Services (AWS) made SageMaker AI Training Sessions generally available Thursday, Oct. 22, giving developers a way to write custom model-training routines while SageMaker manages the GPU operations. The Python interface exposes sample(), forward_backward() and optim_step(). Developers can combine these operations for supervised fine-tuning, reinforcement learning or a training objective of their own. SageMaker assigns GPU capacity, coordinates distributed jobs, recovers interrupted work and saves checkpoints. AWS also released a cookbook with ready-to-run examples for supervised fine-tuning and reinforcement learning. Four base models are supported at launch: gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B and Qwen3.8-27B. Training Sessions are available in US East (N. Virginia), US West (Oregon) and Asia Pacific (Mumbai). Model access differs by location; Qwen3.8-27B, for example, is listed in US West (Oregon). Billing is based on processed tokens during prefill, sampling and training. Reward scoring performed in a customer’s own environment does not incur GPU-time charges. Karini AI CEO Nitin Wagh said his team fine-tuned gpt-oss-20b for multi-turn tool calling against an Amazon Neptune knowledge graph. “Setup just worked, with no manual tuning,” Wagh said. The product sits between preset serverless customization and infrastructure-focused options such as SageMaker Training Jobs and HyperPod. Developers can set the post-training procedure while AWS continues to run the GPU layer. AWS introduced serverless customization in December 2025, added full fine-tuning for more than 25 open-source models in August and announced configurable sequence lengths on Oct. 21. The new APIs add another way to customize models within SageMaker. The release follows [Together AI’s Oct. 19 post-training launch](), which offers reserved GPU capacity for model customization. ## Links in this article - [Together AI’s Oct. 19 post-training launch](https://www.tokenpost.com/news/technology/35231)