This topic covers vector databases and long-term context solutions—systems designed to store, index, and retrieve embeddings and rich metadata so LLMs and retrieval-augmented applications can access persistent memory and enterprise knowledge at scale. As of Jan 2026, widespread LLM adoption, larger multimodal datasets, and stricter data/compliance needs have made reliable long-term context storage a core infrastructure requirement. Core categories include hosted and open-source vector databases (Pinecone, Milvus, Qdrant, Weaviate) and complementary platforms for data, embeddings, and orchestration. Pinecone provides a fully managed, production-focused vector index; Milvus is an open-source, horizontally scalable engine optimized for high-throughput nearest-neighbor search; Qdrant emphasizes payload filtering and developer ergonomics for hybrid search; Weaviate integrates semantic search with modular add-ons (knowledge graph-like schemas and vector modules). Supporting tools shape the pipeline: Activeloop Deep Lake offers multimodal data storage and versioning; Vertex AI and Cohere provide model hosting, embeddings, and fine-tuning services; LlamaIndex and LangChain orchestrate RAG workflows and document-agent logic; Perplexity-like engines demonstrate consumer-facing retrieval and sourced answering. Key trends influencing choices are hybrid retrieval (dense vectors + sparse/keyword), namespace and metadata filtering for governance, vector compression and ANN improvements for cost/latency, and enterprise features—access controls, encryption, snapshot/versioning, and on-prem or VPC deployments. For practitioners, the practical stack usually combines an embeddings provider, a vector store for long-term context, and an orchestration layer to implement RAG, session memory, or enterprise search—balancing latency, scalability, privacy, and maintainability for production LLM applications.