All news with #vector database tag

Microsoft Ignite 2025: Building with Agentic AI and Azure

🚀 Microsoft Ignite 2025 showcased a suite of Azure and AI updates aimed at accelerating production use of agentic systems. Anthropic's Claude models are now available in Microsoft Foundry alongside OpenAI GPTs, and Azure HorizonDB adds PostgreSQL compatibility with built-in vector indexing for RAG. New Azure Copilot agents automate migration, operations, and optimization, while refreshed hardware (Blackwell Ultra GPUs, Cobalt CPUs, Azure Boost DPU) targets scalable training and secure inference.

Amazon OpenSearch: GPU-Accelerated Auto-Optimized Vectors

🚀Amazon OpenSearch Service now offers GPU-accelerated, auto-optimized vector indexes that let teams build billion-scale vector databases in under an hour. Serverless GPU acceleration can speed index builds up to 10X while reducing indexing cost to roughly a quarter of previous expenses. Auto-optimize jobs evaluate k-NN algorithms, quantization, and engine settings against specified latency and recall targets to produce configuration recommendations without manual tuning. These capabilities support vector collections and OpenSearch 2.17+/3.1+ domains across multiple regions.

Amazon S3 Vectors GA: Scalable, Cost‑Optimized Vector Store

🚀 Amazon S3 Vectors is now generally available, delivering native, purpose-built vector storage and query capabilities in cloud object storage. It supports up to two billion vectors per index, 10,000 indexes per vector bucket, and offers up to 90% lower costs to upload, store, and query vectors. S3 Vectors integrates with Amazon Bedrock, SageMaker Unified Studio, and OpenSearch Service, supports SSE-S3 and optional SSE-KMS encryption with per-index keys, and provides tagging for ABAC and cost allocation.

AWS Bedrock Knowledge Bases Adds Multimodal Retrieval

🔍 AWS has announced general availability of multimodal retrieval in Amazon Bedrock Knowledge Bases, enabling unified search across text, images, audio, and video. The managed Retrieval Augmented Generation (RAG) workflow provides developers full control over ingestion, parsing, chunking, embedding (including Amazon Nova multimodal), and vector storage. Users can submit text or image queries and receive relevant text, image, audio, and video segments back, which can be combined with the LLM of their choice to generate richer, lower-latency responses. Region availability varies by feature set and is documented by AWS.

OpenSearch Service Introduces Agentic Search for NLP Queries

🔎 Amazon Web Services has introduced Agentic Search for OpenSearch Service, an agent-driven layer that interprets natural-language intent, orchestrates search tools, and generates OpenSearch DSL queries while providing transparent summaries of its decision process. The built-in QueryPlanningTool uses LLMs to plan and emit DSL, removing the need for manual query syntax. Two agent types are available: conversational agents with memory and flow agents optimized for throughput. Administrators can configure agents via APIs or OpenSearch Dashboards, and Agentic Search is supported on OpenSearch Service version 3.3+ across AWS Commercial and GovCloud regions.

Amazon OpenSearch Service: OpenSearch 3.3 Now Available

📢 Amazon OpenSearch Service now supports OpenSearch 3.3, introducing search performance, observability, and agentic AI integration improvements. Vector search enhancements include agentic search for natural-language queries without complex DSLs, batch processing for the semantic highlighter to lower latency and improve GPU utilization, and optimizations in the Neural Search plugin. The release also makes Apache Calcite the default query engine for PPL, adds a broader PPL command library, and improves the approximation framework for more responsive pagination and dashboards. A new workload management plugin enables grouping of search traffic and tenant-level network isolation to prevent resource overuse.

BigQuery AI: Unified ML, Generative AI, and Agents

🤖 BigQuery AI consolidates BigQuery’s built-in ML, generative AI functions, vector search, and agent tools into a unified platform. It enables users to apply generative models and embeddings directly via SQL, perform semantic vector search, and run end-to-end ML workflows without moving data. Role-specific data agents and assistive features like a data canvas and code completion accelerate work for engineers, data scientists, and business users.

Google: Leader in 2025 Gartner Magic Quadrant for CDBMS

📈 Google announces it was named a Leader in the 2025 Gartner Magic Quadrant for Cloud Database Management Systems for the sixth consecutive year and positioned furthest in vision. The post presents the company's AI-native Data Cloud—a unified stack integrating BigQuery, Spanner, AlloyDB, Looker, and Dataplex—to support agentic AI. Google highlights embedded specialized agents, developer tooling (Data Agents API, ADK, Gemini CLI) and Agent Analytics in BigQuery to accelerate AI-driven applications while asserting cost and governance benefits on a single, open platform.

BigQuery Agent Analytics: Stream and Analyze Agent Data

📊 Google introduces BigQuery Agent Analytics, an ADK plugin that streams agent interaction events into BigQuery to capture, analyze, and visualize performance, usage, and cost. The plugin provides a predefined schema and uses the BigQuery Storage Write API for low-latency, high-throughput streaming of requests, responses, and tool calls. Developers can filter and preprocess events (for example, redaction) and build dashboards in Looker Studio or Grafana while leveraging vector search and generative AI functions for deeper analysis.

Microsoft Databases and Fabric: Unified AI Data Estate

🧠 Microsoft details a broad expansion of its database portfolio and deeper integration with Microsoft Fabric to simplify data architectures and accelerate AI. Key launches include general availability of SQL Server 2025, GA of Azure DocumentDB (MongoDB-compatible), the preview of Azure HorizonDB, and Fabric-hosted SaaS databases for SQL and Cosmos DB. OneLake mirroring, Fabric IQ semantic modeling, expanded agent capabilities, and partner integrations (SAP, Salesforce, Databricks, Snowflake, dbt) are positioned to deliver zero-ETL analytics and operational AI at scale.

Using BigQuery ML to Solve Lookalike Audiences at Zeotap

🔍 Zeotap and Google Cloud describe a SQL-first approach to building scalable lookalike audiences entirely within BigQuery. They convert low-cardinality categorical features into one-hot and multi-hot vectors, use Jaccard similarity reframed via dot-product and Manhattan norms, and index vectors with BigQuery’s VECTOR_SEARCH. By combining pre-filtering on discriminative features and batching queries, the workflow reduces compute, latency, and cost while avoiding a separate vector database.

Amazon DocumentDB 8.0 Adds MongoDB 8.0 Compatibility

⚡ Amazon DocumentDB (with MongoDB compatibility) version 8.0 adds support for MongoDB API drivers 6.0, 7.0, and 8.0 while delivering up to 7x improved query latency and up to 5x better compression. The release introduces Planner Version3, new aggregation stages and operators, dictionary-based Zstandard compression, text index v2, and parallel vector index builds. Upgrades from 5.0 instance-based clusters are supported via AWS Database Migration Service, and DocumentDB 8.0 is available in all Regions where the service is offered.

AlloyDB AI: Auto Vector Embeddings and Indexing Capabilities

🔍 AlloyDB AI launches two preview features—Auto Vector Embeddings and Auto Vector Index—that let teams convert operational databases into AI-native stores using simple SQL. Auto Vector Embeddings generates and incrementally refreshes vectors in-database, batching calls to Vertex AI and running as a background process. The Auto Vector Index (ScaNN) self-configures, self-tunes, and maintains vector indexes to accelerate filtered semantic search and reduce ETL and tuning overhead for production workloads.

Agent Factory Recap: Securing AI Agents in Production

🛡️ This recap of the Agent Factory episode explains practical strategies for securing production AI agents, demonstrating attacks like prompt injection, invisible Unicode exploits, and vector DB context poisoning. It highlights Model Armor for pre- and post-inference filtering, sandboxed execution, network isolation, observability, and tool safeguards via the Agent Development Kit (ADK). The team demonstrates a secured DevOps assistant that blocks data-exfiltration attempts while preserving intended functionality and provides operational guidance on multi-agent authentication, least-privilege IAM, and compliance-ready logging.

Moloco and Google Cloud Power AI Vector Search in Retail

🔎 Moloco’s AI-native retail media platform, integrated with Vertex AI Vector Search on Google Cloud, delivers semantic, real-time ad retrieval and personalized recommendations. The joint architecture uses TPUs and GPUs for model training and scoring while vector search runs efficiently on CPUs, enabling outcomes-based bidding at scale. Internal benchmarks report ~10x capacity, up to ~25% lower p95 latency, and a ~4% revenue uplift. The managed service reduces operational overhead and accelerates time-to-value for retailers.

Scaling Customer Experience with AI on Google Cloud

🤖 LiveX AI outlines a Google Cloud blueprint to scale conversational customer experiences across chat, voice, and avatar interfaces. The post details how Cloud Run hosts elastic front-end microservices while GKE provides GPU-backed AI inference, and how AgentFlow orchestrates conversational state, knowledge retrieval, and human escalation. Reported customer outcomes include a >90% self-service rate for Wyze and a 3× conversion uplift for Pictory. The design emphasizes cost efficiency, sub-second latency, multilingual support, and secure integrations with platforms such as Stripe, Zendesk, and Salesforce.

Amazon ElastiCache Adds Vector Search with Valkey 8.2

🚀 Amazon ElastiCache now offers vector search generally available with Valkey 8.2, enabling indexing, searching, and updating billions of high-dimensional embeddings from providers such as Amazon Bedrock, Amazon SageMaker, Anthropic, and OpenAI with microsecond latency and up to 99% recall. Key use cases include semantic caching for LLMs, multi-turn conversational agents, and RAG-enabled agentic systems to reduce latency and cost. Vector search runs on node-based clusters in all AWS Regions at no additional cost, and existing Valkey or Redis OSS clusters can be upgraded to Valkey 8.2 with no downtime.

Amazon CloudWatch Adds Generative AI Observability

🔍 Amazon CloudWatch is generally available with Generative AI Observability, providing end-to-end telemetry for AI applications and AgentCore-managed agents. It expands monitoring beyond model runtime to include Built-in Tools, Gateways, Memory, and Identity, surfacing latency, token usage, errors, and performance across components. The capability integrates with orchestration frameworks like LangChain, LangGraph, and Strands Agents, and works with existing CloudWatch features and pricing for underlying telemetry.

Amazon Neptune Analytics Launched in Two New Regions

🚀 Amazon has made Neptune Analytics available in the AWS Canada (Central) and Australia (Sydney) Regions, enabling local creation and management of analytics graphs. Neptune Analytics is a memory‑optimized graph engine that supports fast, in‑memory processing, a library of optimized analytic algorithms, low‑latency graph queries, and vector similarity search within traversals. You can ingest data from an Amazon Neptune Database, snapshots, or Amazon S3, and start via the AWS Console or CLI; consult the Neptune pricing page and AWS Region Table for costs and availability.

AI in Today's Cybersecurity: Detection, Hunting, Response

🤖 Artificial intelligence is reshaping how organizations detect, investigate, and respond to cyber threats. The article explains how AI reduces alert noise, prioritizes vulnerabilities, and supports behavioral analysis, UEBA, and NLP-driven phishing detection. It highlights Wazuh's integrations with models such as Claude 3.5, Llama 3, and ChatGPT to provide conversational insights, automated hunting, and contextual remediation guidance.