> ## Documentation Index > Fetch the complete documentation index at: https://docs.baseten.co/llms.txt > Use this file to discover all available pages before exploring further. # Building with Baseten export const QwenIconCard = ({title, href}) => } horizontal />; export const OpenAIIconCard = ({title, href}) => } horizontal />; These examples walk through common ways to deploy and serve models on Baseten. Each section below covers a different packaging approach, so pick whichever fits your model and workflow. If you're new to Baseten, start with [Deploy your first model](/examples/deploy-your-first-model). ## Engines Config-only deploys on Baseten's optimized inference engines. This is the fastest path for LLMs, embeddings, and other common architectures, with no Python or Dockerfile required. See [engines](/engines) for architecture support, quantization options, and performance guidance. ## Custom Docker servers Bring your own inference server, such as vLLM, SGLang, or anything that speaks HTTP. Baseten runs the container, and you own the serving stack. See [Docker server](/development/model/custom-server) for configuration. ## Custom Python models Write the Truss `Model` class for full control over load and predict. Use when no engine or open-source server fits your architecture. See [custom model code](/development/model/custom-model-code) for the API. ## Chains Compose multi-step AI workflows across models, routing, parallelism, and post-processing. See [Chains](/development/chain/overview) for the SDK. ## Training Train and fine-tune models with Baseten's scalable training infrastructure. From [fine-tuning large language models](/training/getting-started) to training custom models, our platform provides the tools and compute you need. Our training infrastructure supports popular frameworks including VERL, Megatron, and Unsloth, as well as models trained directly with Hugging Face Transformers.