venturebeat

2025-10-18

Abstract or die: Why AI enterprises can't afford rigid vector stacks

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.

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venturebeat

2025-12-03

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 [...]

Match Score: 296.41

venturebeat

2025-11-16

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 [...]

Match Score: 207.44

venturebeat

2025-11-02

Moving past speculation: How deterministic CPUs deliver predictable AI performance

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 [...]

Match Score: 171.18

venturebeat

2025-10-01

GitHub leads the enterprise, Claude leads the pack—Cursor’s speed can’t close

In the race to deploy generative AI for coding, the fastest tools are not winning enterprise deals. A new VentureBeat analysis, combining a comprehensive survey of 86 engineering teams with our own ha [...]

Match Score: 72.53

venturebeat

2025-11-04

Snowflake builds new intelligence that goes beyond RAG to query and aggregate thousands of documents at once

Enterprise AI has a data problem. Despite billions in investment and increasingly capable language models, most organizations still can't answer basic analytical questions about their document re [...]

Match Score: 65.15

venturebeat

2025-11-01

Large reasoning models almost certainly can think

Recently, there has been a lot of hullabaloo about the idea that large reasoning models (LRM) are unable to think. This is mostly due to a research article published by Apple, "The Illusion of Th [...]

Match Score: 65.08

venturebeat

2025-12-09

Databricks' OfficeQA uncovers disconnect: AI agents ace abstract tests but stall at 45% on enterprise docs

There is no shortage of AI benchmarks in the market today, with popular options like Humanity's Last Exam (HLE), ARC-AGI-2 and GDPval, among numerous others.AI agents excel at solving abstract ma [...]

Match Score: 61.85

Destination

2025-01-10

Citizen Sleeper 2 asks how we stay human in a hopeless future

Life for Sleepers is fraught. They gain consciousness in a state of indentured servitude, an emulated human mind inside an android body, forced to work until they’re discarded. Those who escape don [...]

Match Score: 50.81

venturebeat

2025-10-04

Beyond Von Neumann: Toward a unified deterministic architecture

A cycle-accurate alternative to speculation — unifying scalar, vector and matrix computeFor more than half a century, computing has relied on the Von Neumann or Harvard model. Nearly every modern ch [...]

Match Score: 50.23