Vector databases (DBs), once specialist research instruments, have become widely used infrastructure in just a few years. They power today's semantic search, recommendation engines, anti-fraud measures and gen AI applications across industries. There are a deluge of options: PostgreSQL with pgvector, MySQL HeatWave, DuckDB VSS, SQLite VSS, Pinecone, Weaviate, Milvus and several others.The riches of choices sound like a boon to companies. But just beneath, a growing problem looms: Stack instability. New vector DBs appear each quarter, with disparate APIs, indexing schemes and performance trade-offs. Today's ideal choice may look dated or limiting tomorrow.To business AI teams, volatility translates into lock-in risks and migration hell. Most projects begin life with lightweight en [...]
Vector databases emerged as a must-have technology foundation at the beginning of the modern gen AI era. What has changed over the last year, however, is that vectors, the numerical representations o [...]
When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing [...]
For more than three decades, modern CPUs have relied on speculative execution to keep pipelines full. When it emerged in the 1990s, speculation was hailed as a breakthrough — just as pipelining and [...]
Something shifted in enterprise RAG in Q1 2026. VB Pulse data spanning January through March tells a consistent story: the market stopped adding retrieval layers and started fixing the ones it already [...]
Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into [...]
A new open-source framework called PageIndex solves one of the old problems of retrieval-augmented generation (RAG): handling very long documents.The classic RAG workflow (chunk documents, calculate e [...]
Enterprise data teams moving agentic AI into production are hitting a consistent failure point at the data tier. Agents built across a vector store, a relational database, a graph store and a lakehous [...]