This topic covers databases and storage systems engineered for extremely low read/write latency and fast vector search to support modern AI workflows (RAG, agents, streaming inference) alongside blockchain and decentralized workloads that demand cryptographic integrity and high throughput. Systems such as Tidehunter and peer projects focus on NVMe-optimized storage, RDMA/NVMe‑oF networking, hardware acceleration and hybrid memory tiers to keep embedding lookups, state reads, and consensus-relevant writes within tight latency budgets. Relevance in 2026 stems from two converging trends: wider deployment of real‑time LLM agents and RAG pipelines (driven by frameworks like LangChain and LlamaIndex) and increased interest in decentralized/post‑quantum blockchains where on‑chain/off‑chain storage, verification, and low-latency access matter for user experience and safety. Developer tools—LangChain for agent orchestration, LlamaIndex for turning documents into retrievable indexes, and AI-first dev platforms (Tabnine, Windsurf, Qodo) that accelerate and govern code—depend on predictable retrieval and storage performance to meet SLAs. Key design considerations include vector index placement, sharding and replication strategies, consistency vs latency tradeoffs, network fabrics (RDMA/DPU), compact on‑disk formats for embeddings, and secure bridging between decentralized ledgers and off‑chain stores. Post‑quantum cryptography increases key and signature sizes and influences throughput and storage patterns, so systems must balance cryptographic requirements with latency goals. Practitioners should evaluate latency percentiles, throughput under mixed AI/blockchain workloads, hardware requirements, privacy controls, and upgrade paths for PQC; the right choice often combines specialized low‑latency storage with project‑level orchestration via AI and developer tooling.