Vector Database stories
Customers can now keep document search and embedding work inside BigQuery, as Google rolls out AI search tools to cut extra pipelines and indexes.
Mortgage file reviews could be cut to minutes after SlateWorks' AI auditor beat 25 teams in ABBYY's Bangalore hackathon.
Businesses can now keep AI retrieval and live app data in one place, as the service cuts data copying and extra sync pipelines.
Up to 4.16 times faster vector ingestion could help OpenSearch customers cut AI search latency and reduce storage overhead.
The retailer said the overhaul cut infrastructure maintenance by 50% and improved product recommendations, ratings and Gift Finder responses.
Most enterprises will need costly infrastructure changes before agentic AI can move from pilots to production, Google Cloud research says.
Throughput for AI vector searches can rise fourfold on AlloyDB, as Google's preview aims to cut latency without changing pgvector code.
The release aims to ease a key hurdle for firms moving AI agents into production by unifying memory, retrieval and access across environments.
Banks under regulatory pressure may be able to modernise databases without moving sensitive records to public cloud infrastructure.
Customers should see faster AI search and training on AWS as NVIDIA makes GPU indexing the default and adds new EC2 G7 instances.
Enterprises can now run AI agents on live PostgreSQL data with governance controls, as EDB expands its Postgres AI platform.
AI agents can now tap enterprise data in Microsoft OneLake with citations, as Pinecone claims lower token use and faster responses.
Production AI agents often fail on stale or fragmented data, and Redis is betting its new Iris platform can fix that runtime gap.
Enterprises running AI agents can now cut infrastructure overhead, as MongoDB adds automated embeddings, memory and faster database performance.
Enterprises under pressure to scale AI can now use Teradata's new platform to govern data and agents across cloud and on-premises systems.
The new system aims to cut infrastructure friction for firms shifting AI from pilots to always-on agents across cloud and on-premises setups.
It promises lower token costs, faster responses and tighter control of sensitive data as firms push agents into production.
Businesses can now run transactional, graph and vector workloads together as Google broadens Spanner for AI applications across clouds and on-premises.
The new system aims to cut complexity for enterprises as AI agents need live access to structured, unstructured and vector data.
Customers in Southeast Asia can now keep AI data closer to home, as Pinecone adds local residency and lower latency in Singapore.