Irresistible force, meet immovable object. Tech leaders are under pressure to satisfy growing demand for AI while keeping a lid on costs.That is becoming harder as Anthropic, OpenAI, and GitHub shift some services away from flat-rate subscriptions toward usage-based billing. Database vendors claim they can help by cutting the number of calls made to AI models and handling the new workloads generated by developer agents. According to IDC research director Devin Pratt, "demand for the underlying capability is strong, because agentic adoption is already broad."
Around 79 percent of organizations are either investing significantly in agentic AI with a set budget or already running agentic applications in production, according to IDC.
"The appetite for the data infrastructure beneath it is real," Pratt said. "The open question is whether the specialists win or the capability gets absorbed into the platforms enterprises already run, much as it did with vector databases."Among the specialists taking on the omnipresent cloud platforms and their embedded data services is Pinecone. It carved out a niche as a vector database vendor for users wanting to put LLMs into production. Although that term became commonplace among mainstream database companies, Pinecone always argued it maintained a technical advantage for the biggest use cases. Building on top of its vector database technology, the vendor has recently launched Nexus – a "knowledge engine, not a retrieval system" – and embedded it in Microsoft's OneLake, a hybrid data lake and data warehouse environment. Pinecone's idea is that by compiling a knowledge base of an organization's data structure and content, its technology can avoid burning through tokens back and forth between the data and AI agents. Nexus is designed to structure, contextualize, and compose specialized contexts – derived artifacts – in advance of agent demand. Pinecone product veep Jeff Zhu told The Register the idea was to prevent agents from repeating the same work to understand the structure of business data and its context. "All these coding agents, for example, are really good at doing a bunch of exploratory work if you ask them a question," Zhu said. "It's going to make a call, get the table schema, do some exploratory work, figure out what the top rows of this one table are, and ultimately it will eventually get to the right answer most of the time, but it's going to burn through a bunch of tokens, because every single time it creates a specific answer to a question, it has to understand your business context and rediscover it every single time."Nexus provides a semantic layer of business data for a given use case or outcome. Imagine a finance analyst agent versus an HR agent: it is very possible they could use the same data, but they would want very different outcomes as a result, Zhu said. "Our system is an engine that can support any number of use cases. Based on your particular use case, and the tasks that you want to accomplish, we use your data sources – which can be SQL databases, unstructured documents, PDFs, and so on – we build a task-specific context which is used for that individual agent's job or role."
