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Exceptional Support and Scalability for Vector Similarity Search
What do you like best about the product?
Profesional technical support, great documentation and the most scalable and reliable solution for vector similarity search
What do you dislike about the product?
Nothing to dislike. Everything is going great so far
What problems is the product solving and how is that benefiting you?
Zilliz addresses the challenge of searching through embeddings created from social media creators' content on a large scale. It effectively manages the complexity involved in handling and retrieving relevant information from vast amounts of such data.
Stable Performance and Excellent Support from Zilliz
What do you like best about the product?
Zilliz has been pretty stable in the last year. The team was very helpful resolving issue in the initial integration period. We currently store about 50 million vectors.
What do you dislike about the product?
The cost can be somewhat high when we stored like 1 billion vectors.
What problems is the product solving and how is that benefiting you?
We use Zilliz as our RAG
Fast, Affordable, and Effortless to Use
What do you like best about the product?
This product is quick, affordable, straightforward, and gets the job done. It's user-friendly and can easily scale to meet growing needs.
What do you dislike about the product?
Modifying collection schemas involves a migration process, which can be quite complex and often takes a significant amount of time to manage.
What problems is the product solving and how is that benefiting you?
Zilliz serves as the primary knowledge base for our AI agent. It drives the RAG functionality for a customer support and shopping assistant AI agent that currently assists millions of e-commerce shoppers.
Fast and Capable Vector Database
What do you like best about the product?
Extremely fast, low-latency vector search, even at a massive scale. The cloud platform is intuitive, and the SDKs (like PyMilvus) are straightforward to integrate.
What do you dislike about the product?
The pricing for the cloud service can be a bit high for smaller projects or individual developers.
What problems is the product solving and how is that benefiting you?
We use Zilliz to power our recommendation system. It solves the critical problem of storing and searching millions of image embeddings at high speed. This benefits us by enabling real-time, accurate visual recommendations and 'similar item' search for our users, which significantly improves user engagement.
Lightning-Fast Retrieval with Robust Support
What do you like best about the product?
I truly love using Zilliz for building our video retrieval tool as it has greatly enhanced our ability to convert each frame of a video into vectors and store them effectively in Zilliz Cloud. This functionality allows for accurate and efficient searches for corresponding images or videos, whether making searches from text to images or vice versa. The support from Zilliz is impeccable, with a very active community and extremely reliable database systems, which gives us a great deal of confidence. The most impressive aspect is the retrieval speed; it is exceptionally fast, allowing us to search through millions of vectors in just 200 milliseconds, which is critical for our operations. Additionally, the ability of Zilliz to handle a large capacity of data further boosts its value, making our data management and retrieval processes smooth and efficient.
What do you dislike about the product?
I think the number of vector columns supported in a single collection is a bit low, it seems to be 4 or 8. We would like to be able to search across more dimensions.
What problems is the product solving and how is that benefiting you?
I use Zilliz to convert video frames into vectors stored in Zilliz Cloud, facilitating accurate image or video retrieval. The retrieval speed is extremely fast, processing millions of vectors in 200 milliseconds, and the capacity is very large.
Convenient Hybrid Retrieval with Room for Documentation Improvement
What do you like best about the product?
I really appreciate how Zilliz supports hybrid retrieval, combining both vector and keyword searches. This feature is incredibly beneficial as it provides me with more accurate, context-aware results and ensures that I do not miss important exact matches. It’s very convenient to use, making my workflow more efficient. Additionally, the initial setup was a breeze thanks to Docker Compose and the clear documentation which guided me quickly through getting started.
What do you dislike about the product?
I find that sometimes the documentation could be more detailed, especially for advanced configurations and performance tuning. They could include more real-world configuration examples, detailed parameter explanations, and best practices for optimizing performance with large-scale hybrid retrieval workloads.
What problems is the product solving and how is that benefiting you?
I use Zilliz for RAG, enhancing search accuracy and document storage with similarity search and hybrid retrieval, combining vector and keyword search for context-aware results.
Outstanding Performance and Robust Features for Large Datasets
What do you like best about the product?
Excellent performance, Automatic updates,Comprehensive functionality,Supports extremely large datasets
What do you dislike about the product?
Support for scalar fields is not perfect.
The price is relatively expensive.
The price is relatively expensive.
What problems is the product solving and how is that benefiting you?
It serves as a vector data storage solution for RAG data retrieval in AI scenarios.
It is also the primary database for semantic similarity matching calculations.
It is also the primary database for semantic similarity matching calculations.
Efficient Zilliz for Entity Alignment, Disambiguation, and RAG Recall in Knowledge Graphs
What do you like best about the product?
I love how Zilliz excels at scalability and high-performance vector search, making it incredibly efficient for handling massive datasets in AI-driven tasks. In my experience, its hybrid retrieval capabilities—combining vector embeddings with knowledge graph integrations—stand out, especially for complex queries involving semantic understanding. It's easy to integrate with tools like LangChain and OpenAI embeddings, and the cloud-native features in Zilliz Cloud simplify management without sacrificing speed or accuracy.
What do you dislike about the product?
For self-hosted Milvus setups, the configuration might feel a bit involved compared to fully managed options, but Zilliz Cloud largely resolves that with its user-friendly UI and elastic scaling.
What problems is the product solving and how is that benefiting you?
Zilliz is tackling challenges in entity alignment and disambiguation within my knowledge graph project by leveraging vector embeddings and graph-based retrieval to link and resolve entities across diverse datasets, reducing ambiguities (like distinguishing similar terms in different contexts) and enabling precise multi-hop reasoning. This has streamlined entity matching, cutting down on manual corrections and improving overall graph accuracy. In the recall step of my RAG pipeline, it enhances retrieval of relevant documents through hybrid searches (vector similarity plus relational structures), minimizing hallucinations in LLM outputs and providing more complete, contextually rich responses. Overall, it's saved me significant time on data processing, boosted the reliability of my AI applications, and allowed for handling larger-scale data without performance drops—ultimately making my projects more efficient and effective.
Why I Love Zilliz: Fast Queries, Flexible Indexes, and Easy Choices for My Business
What do you like best about the product?
Zilliz is the commercial version of Milvus, and it fits different business needs really well. First, it’s super fast when it comes to queries—super impressive. Then, for features, it supports both vector indexing and keyword indexing, so you’ve got way more flexible options there. And when picking the right type, it has capacity-focused, performance-focused, and tiered storage models. You can easily match what you need, whether that’s "fast response times" or "keeping costs down." Overall, it’s really practical and easy to use.
What do you dislike about the product?
Currently, it does not support slow query-related features.
Audit logs only support the Coordinated Universal Time Zero (UTC+0) time zone and cannot be adapted to other local time zones.
Audit logs only support the Coordinated Universal Time Zero (UTC+0) time zone and cannot be adapted to other local time zones.
What problems is the product solving and how is that benefiting you?
Zilliz addresses three main challenges in unstructured data processing: it enables vector search for question topics, supports Retrieval-Augmented Generation (RAG) for large language models, and provides vector search capabilities for images.
Effortless Vector Retrieval, Seamless Setup
What do you like best about the product?
I am truly impressed by Zilliz's ability to handle a vast amount of data efficiently. Its capacity to store hundreds of millions of vector data points is remarkable. I find the vector retrieval process to be incredibly fast, characterized by its low latency, which is vital for my image similarity retrieval and content moderation needs. The platform's support for high-concurrency requests is another feature I value, as it ensures smooth operation even when handling multiple requests simultaneously. Additionally, the fact that Zilliz supports more than ten vector index types and offers rich functionality is a considerable advantage, simplifying operation and maintenance for my team. This richness in features also makes using Zilliz cost-effective. The initial setup was straightforward, simple, and clear, making it easier for us to integrate it with general-purpose embedding models. These aspects collectively make Zilliz an invaluable tool for our data storage and retrieval endeavors.
What do you dislike about the product?
There is a need for more comprehensive learning materials for Zilliz.
What problems is the product solving and how is that benefiting you?
I use Zilliz for efficient image similarity retrieval and content moderation, storing and retrieving vector data with low latency, supporting high concurrency, simplifying operations, and reducing costs.
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