Data Scientist at a tech services company with 1,001-5,000 employees
Real User
Top 10
2024-04-29T11:52:00Z
Apr 29, 2024
Milvus works well for various use cases and is quite flexible in terms of deployment. For on-premises deployment, you can use the open-source version with Docker. The system requirements are relatively modest; around 16 GB of RAM and some disk space are recommended. This setup is sufficient for initial trials or proof-of-concept projects. If you prefer, you can use a cloud instance for Milvus. Cloud instances have limitations but are ideal for initial testing and quick setup without needing to install everything locally. This allows you to experiment with Milvus without significant upfront investment in infrastructure. Milvus provides options for both on-premises and cloud deployment, so you can choose based on your needs. The documentation is comprehensive, and while some initial setup may require assistance, the process is straightforward once you get the hang of it. You can configure Milvus with different databases and customize it to fit your requirements. Milvus excels at calculating the distance between your queries and the data, which is central to its functionality. The tool is designed to be easy to use and configure, with options to filter and view your data effectively. If you encounter issues, the UI and documentation provide support to help you troubleshoot and resolve problems. Overall, I rate the solution a seven out of ten.
Leader, Data Science Practice at a computer software company with 5,001-10,000 employees
MSP
Top 5
2024-04-22T14:04:29Z
Apr 22, 2024
I work in a services company that works with different customers. Often, customers decide to use Milvus rather than us. I would recommend Milvus to experienced software developers. Local infrastructure or on-premises will use Mistral AI for Large Language Models (LLMs) and Chroma DB for vector DB. We usually use the BGE, LangChain, or LlamaIndex embedding models. Overall, I rate the solution a six out of ten.
PeerSpot users agreed that functionality is of utmost importance to a quality Open Source Database (OSD). The specifications will change depending on the task you are trying to accomplish, but any Open Source Database needs to be solidly functional or there is nothing to work with. On an individual basis, scalability, metrics, and security are important features to look for. Users were clear that the efficiency of the medium which will connect the OSD with the application running it is...
I would definitely recommend it. For all the things it provides, it is a good solution. Overall, I would rate the solution a nine out of ten.
Milvus works well for various use cases and is quite flexible in terms of deployment. For on-premises deployment, you can use the open-source version with Docker. The system requirements are relatively modest; around 16 GB of RAM and some disk space are recommended. This setup is sufficient for initial trials or proof-of-concept projects. If you prefer, you can use a cloud instance for Milvus. Cloud instances have limitations but are ideal for initial testing and quick setup without needing to install everything locally. This allows you to experiment with Milvus without significant upfront investment in infrastructure. Milvus provides options for both on-premises and cloud deployment, so you can choose based on your needs. The documentation is comprehensive, and while some initial setup may require assistance, the process is straightforward once you get the hang of it. You can configure Milvus with different databases and customize it to fit your requirements. Milvus excels at calculating the distance between your queries and the data, which is central to its functionality. The tool is designed to be easy to use and configure, with options to filter and view your data effectively. If you encounter issues, the UI and documentation provide support to help you troubleshoot and resolve problems. Overall, I rate the solution a seven out of ten.
I work in a services company that works with different customers. Often, customers decide to use Milvus rather than us. I would recommend Milvus to experienced software developers. Local infrastructure or on-premises will use Mistral AI for Large Language Models (LLMs) and Chroma DB for vector DB. We usually use the BGE, LangChain, or LlamaIndex embedding models. Overall, I rate the solution a six out of ten.
Milvus is deployed on-cloud in our organization. Overall, I rate Milvus a seven out of ten.