This topic evaluates the ecosystem of frameworks and toolchains used to fine‑tune, serve and orchestrate large language models (LLMs) in production agent‑style applications. As of 2025‑12‑04, teams balance three converging pressures: lower latency and lower cost for inference, repeatable fine‑tuning and evaluation workflows, and integration with retrieval‑augmented generation (RAG) and agent frameworks. Key categories include inference servers (e.g., NVIDIA Triton), model hubs and fine‑tuning platforms (Hugging Face, MosaicML), orchestration/fabric layers (QVAC Fabric and similar fabrics), and supporting services for data collection, evaluation and hosting (OpenPipe). Hardware and systems vendors such as Rebellions.ai add a layer of specialization with energy‑efficient inference accelerators and co‑optimized software stacks. Emerging and complementary approaches include decentralized infrastructure (Tensorplex Labs) that pairs model development with blockchain/DeFi primitives, developer‑focused agent toolkits like LlamaIndex for document agents, agentic IDEs such as Warp, and open instruction‑tuned models (nlpxucan/WizardLM) used as fine‑tuning bases. Current trends reflected across these tools are stronger end‑to‑end observability and SDKs for capture and evaluation, tighter coupling of data pipelines to fine‑tuning, quantization and compilation for inference efficiency, and a split between managed hosted platforms and open, portable toolchains. For teams building RAG/agent systems, the practical tradeoffs are well defined: choose a fine‑tuning and data platform that preserves provenance and evaluation, pick an inference stack that matches latency/throughput and hardware, and adopt orchestration layers that support hybrid cloud, edge or decentralized deployment while keeping reproducibility and cost under control.