AI-powered backtesting for crypto trading combines high-frequency market and on‑chain data, sophisticated ML models, and production infrastructure to evaluate strategies under realistic market conditions. As of early 2026, growth in algorithmic crypto activity, more accessible cloud ML services, and demand for reproducible, auditable results make integrated backtesting stacks essential for traders and firms. Key capabilities include robust data pipelines, semantic search for alternative signals, realistic execution simulation (slippage, fees, latencies), walk‑forward evaluation, and model monitoring for drift and risk. Leading platforms and components serve distinct roles: Vertex AI provides an end‑to‑end managed environment to build, train, fine‑tune, evaluate, and deploy models at scale; Google Gemini’s multimodal models and APIs can accelerate signal synthesis, scenario generation, and natural‑language strategy augmentation; Pinecone offers a production-grade vector database to power semantic search, retrieval‑augmented generation (RAG) of research documents, and fast nearest‑neighbor lookups for analog signal retrieval; Domo and Sisense supply enterprise analytics, data integration, visualization, and embedded BI for real‑time dashboards, cohort analysis, and operational reporting. Combining these components supports modern backtesting workflows: unified data ingestion and feature stores, model development and scenario testing, high‑performance retrieval for research, and scalable deployment with observability. Practitioners should prioritize reproducibility, realistic execution assumptions, and governance (model risk and compliance) when selecting tools. The current landscape favors modular stacks that integrate managed ML infrastructure, vector search, and enterprise analytics to move validated crypto strategies from simulation to production with traceability and control.