Stop graphing everything: When GraphRAG actually beats vector RAG

If you have built anything with retrieval-augmented generation (RAG) in the last two years, you have lived its central frustration: You chop your documents into chunks, embed them, retrieve the top fe [...]

Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

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 [...]

SurrealDB 3.0 wants to replace your five-database RAG stack with one

Building retrieval-augmented generation (RAG) systems for AI agents often involves using multiple layers and technologies for structured data, vectors and graph information. In recent months it has al [...]

AWS claims 90% vector cost savings with S3 Vectors GA, calls it 'complementary' - analysts split on what it means for vector databases

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 [...]

Databricks' serverless database slashes app development from months to days as companies prep for agentic AI

Five years ago, Databricks coined the term 'data lakehouse' to describe a new type of data architecture that combines a data lake with a data warehouse. That term and data architecture are n [...]

AWS enters the context layer race with a graph that learns from agents, not manual curation

Building a context layer between enterprise data stores and AI agents is bespoke work, with no standard service to automate or maintain the graphs over time. Amazon is making a direct play to change t [...]

From shiny object to sober reality: The vector database story, two years later

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 [...]

Asana's AI agents share memory across your company — but not your secrets

Enterprise teams building AI agents keep hitting the same wall: a chatbot that can answer a prompt but can't remember what the last five people asked it, and can't tell you whether last mont [...]

Oracle converges the AI data stack to give enterprise agents a single version of truth

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 [...]