I'm a freelance developer working on a multimodal video recommendation system for a startup. Zilliz's support for billion-scale vector search was impressive—deploying their managed service meant I didn't need to worry about scaling issues. The multi-cloud options allowed us to host across different environments to optimize latency. However, I found the setup documentation somewhat sparse, especially around fine-tuning indexing parameters for multimodal data. Also, integrating the embedding pipelines was straightforward, but I wish there were more built-in tools for advanced governance and data privacy. Still, for large-scale projects, Zilliz offers a robust backbone.

As a data scientist at a mid-sized tech firm, I needed a reliable way to handle billion-scale image embeddings for our visual search app. Zilliz's fully managed Milvus service simplified deployment tremendously—within hours, I had a robust, scalable setup without the hassle of managing infrastructure. Its high-performance vector search made retrieval speeds lightning-fast, which was crucial for user experience. The built-in embedding pipelines streamlined our AI workflow, especially during model updates. My only wish is that multi-cloud deployment could offer more seamless migrations between providers, but overall, it's been a game changer for our project.