Data Scientist at a tech services company with 1,001-5,000 employees
Real User
Top 5
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
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.
Open Source Databases are essential for businesses seeking customizable database solutions. They offer flexibility, security, and active community support, making them a popular choice for a wide range of applications and industries.Known for their adaptability, Open Source Databases enable organizations to tailor database management systems to their specific requirements. With the freedom to modify code, users can optimize performance and security in ways that proprietary databases might not...
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.