AI Platforms for Finance & Quant Trading covers the infrastructure, tooling, and marketplaces that let quantitative teams build, validate, deploy and operate AI-driven strategies. As of 2026 this topic centers on three converging shifts: agentic quant tools that automate research and execution workflows, tokenized markets that create new on-chain data and execution venues, and institutional AI quant stacks that emphasize governance, low‑latency inference, and end‑to‑end model lifecycle management. Key platform roles are: unified ML clouds (e.g., Vertex AI) for training, fine‑tuning and production deployment; model-acceleration clouds (e.g., Together AI) for scalable GPU training and serverless inference; document-agent and RAG frameworks (e.g., LlamaIndex) to turn research corpora, filings and tick-level logs into queryable agents; developer productivity assistants (GitHub Copilot, JetBrains AI Assistant) to speed strategy coding and tests; knowledge and workflow layers (Notion) to capture research, runbooks and model documentation; and analytics/BI platforms (Sisense) for embedded insight and monitoring. In practice, institutional stacks assemble these components into governed pipelines: enterprise data platforms ingest tick, alternative and on‑chain tokenized feeds; RAG agents and vector stores accelerate signal discovery; models are trained and stress‑tested in acceleration clouds; and agents or models are deployed through marketplaces or orchestration frameworks with observability, backtesting, and compliance controls. Marketplaces for models and autonomous agents are growing, enabling reusable signal components but also raising provenance, auditability and latency questions. For quant teams evaluating platforms, priorities in 2026 are integration with low‑latency execution, robust data lineage and explainability, scalable inference, and clear governance for agentic behaviors — all balanced against operational cost and regulatory risk.