CloseVector mentions (0)

Zilliz Cloud mentions (4)

• Building Production-Grade Vector Search: Performance Insights from Zilliz Cloud on AWS

  As an engineer designing real-time RAG pipelines, I consistently face the challenge of selecting infrastructure capable of handling massive vector datasets without compromising latency or reliability. My recent evaluation of Zilliz Cloud deployed on AWS revealed several architecturally significant patterns worth sharing. - Source: dev.to / 2 months ago

• Monitoring Vector Database Performance: Setting Up Prometheus for Zilliz Cloud in Production

  As an engineer managing AI workloads, I’ve learned that observability isn’t optional—it’s survival gear. When my team adopted Zilliz Cloud for vector search in our RAG pipeline, we needed granular visibility into latency, memory, and throughput. Prometheus emerged as the logical choice, but integration reveals subtle pitfalls. Here’s what I discovered deploying this stack. - Source: dev.to / 4 months ago

• Monitoring Vector Search Operations in Production: How I Integrated Zilliz Cloud with Datadog

  As an engineer scaling semantic search systems, I’ve learned that observability separates functional prototypes from production-grade AI. Last quarter, I hit critical bottlenecks in our retrieval-augmented generation pipeline when QPS spiked unexpectedly. The core issue? Our monitoring couldn’t correlate Milvus-based vector search latency with downstream LLM inference. That’s when I integrated Zilliz Cloud’s... - Source: dev.to / 4 months ago

• Build RAG Chatbot with LangChain, Milvus, GPT-4o mini, and text-embedding-3-large

  Retrieval-Augmented Generation (RAG) is a game-changer for GenAI applications, especially in conversational AI. It combines the power of pre-trained large language models (LLMs) like OpenAI’s GPT with external knowledge sources stored in vector databases such as Milvus and Zilliz Cloud, allowing for more accurate, contextually relevant, and up-to-date response generation. - Source: dev.to / 8 months ago