Zilliz Cloud Reviews

DebugTool Reviews

1. Ernesto Lasso

   · Design project manager at Sriservices · about 4 years ago
   Save time and deliver projects faster

   We work in an agency that develops websites mainly in WordPress, and regularly based on our experience over several years of working with multiple clients, we have discovered that one of the main problems between agency-client is communication, which often sometimes is tedious and fruitless, but now with the visual debugger is easy to point in the screen what needs to be changed and do it quickly. Also, we had a problem calculating how much our will cost our services for the customers according to the time spent on their projects but now with the "Time tracking" function, we can know exactly how much time was dedicated to the project and based on that invoice the client with greater certainty.

   I think this is a great solution for web designers that needs to clear all visual bugs of a website fast to deliver in less time and get more jobs quickly.

   🏁 Competitors: Webvizio, Atarim

   👍 Pros:    Easy to install|Very good money/value|Saves a ton of time

   👎 Cons:    New

Zilliz Cloud mentions (5)

• Vector Graph RAG: Multi-Hop RAG Without a Graph Database

  By default, it uses Milvus Lite with a local .db file — no server needed. For production, switch to Milvus standalone/cluster or Zilliz Cloud. - Source: dev.to / 4 months ago

• 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 / 12 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 / about 1 year 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 / about 1 year 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 / over 1 year ago

DebugTool mentions (0)