Update vector-search-ai.md

Author: wmwxwa

articles/cosmos-db/mongodb/vcore/vector-search-ai.md

@@ -19,9 +19,9 @@ When developers select vector databases, the open-source options provide numerou
 
 Another advantage of open-source vector databases is the strong community support they enjoy. Active user communities often contribute to the development of these databases, provide support, and share best practices, promoting innovation.
 
-Some individuals also opt for open-source vector databases because they are "free," meaning there is no cost to acquire or use the software. However, for this purpose users can also use the free benefits through the lifetime free tiers offered by managed vector database services. These managed services provide not only cost-free access up to a certain usage limit but also simplify the operational burden by handling maintenance, updates, and scalability. Therefore, users who seek the economic advantages of open-source solutions can achieve similar cost savings using the free tier of managed vector database services, which provide the added benefit of reduced management overhead. This approach offers a balance between cost efficiency and operational convenience, allowing users to focus more on their core activities rather than on database administration.
+Some individuals opt for open-source vector databases because they are "free," meaning there's no cost to acquire or use the software. An alternative is using the free tiers offered by managed vector database services. These managed services provide not only cost-free access up to a certain usage limit but also simplify the operational burden by handling maintenance, updates, and scalability. Therefore, by using the free tier of managed vector database services, users can achieve cost savings while reducing management overhead. This approach allows users to focus more on their core activities rather than on database administration.
 
-## Working Mechanism of Open-source Vector Databases
+## Working mechanism of open-source vector databases
 
 Open-source vector databases are designed to store and manage vector embeddings, which are mathematical representations of data in a high-dimensional space. In this space, each dimension corresponds to a feature of the data, and tens of thousands of dimensions might be used to represent sophisticated data. A vector's position in this space represents its characteristics. Words, phrases, or entire documents, and images, audio, and other types of data can all be vectorized. These vector embeddings are used in similarity search, multi-modal search, recommendations engines, large languages models (LLMs), etc.
 
@@ -40,33 +40,32 @@ Vector databases are used in numerous domains and situations across analytical a
 - implement persistent memory for AI agents
 - enable retrieval-augmented generation (RAG)
 
-## Selecting the Appropriate Open-source Vector Database
+## Selecting the best open-source vector database
 
-Choosing the best open-source vector database requires considering several factors. Performance and scalability of the database are crucial, as they impact whether the database can handle your specific workload requirements. Databases with efficient indexing and querying capabilities usually offer optimal performance.
-
-Another factor is the community support and documentation available for the database. A robust community and ample documentation can provide valuable assistance. Comparing different open-source vector database options based on features, supported data types, and compatibility with existing tools and frameworks is critical to finding the best fit for your needs. Ease of installation, configuration, and maintenance should also be considered to ensure smooth integration into your workflow.
-
-Here are some popular open-source vector databases:
+Choosing the best open-source vector database requires considering several factors. Performance and scalability of the database are crucial, as they impact whether the database can handle your specific workload requirements. Databases with efficient indexing and querying capabilities usually offer optimal performance. Another factor is the community support and documentation available for the database. A robust community and ample documentation can provide valuable assistance. Here are some popular open-source vector databases:
 
 - Chroma
 - Milvus
 - Qdrant
 - Weaviate
 
-## Challenges with Open-source Vector Databases
+>[!NOTE]
+>The most popular option may not be the best option for you. To find the best fit for your needs, you should compare different options based on features, supported data types, compatibility with existing tools and frameworks you use. Ease of installation, configuration, and maintenance should also be considered to ensure smooth integration into your workflow. 
+
+## Challenges with open-source vector databases
 
 Open-source vector databases pose challenges that are typical of open-source software:
 
 - Setup: Users need in-depth knowledge to install, configure, and operate, especially for complex deployments. Optimizing resources and configuration while scaling up operation requires close monitoring and adjustments.
-- Maintenance: Users must manage their own updates, patches, and maintenance. Thus, ML expertise would not suffice; users must also have extensive experience in database administration.
+- Maintenance: Users must manage their own updates, patches, and maintenance. Thus, ML expertise wouldn't suffice; users must also have extensive experience in database administration.
 - Support: Official support can be limited compared to managed services, relying more on community assistance.
 
 Therefore, while free initially, open-source vector databases incur significant costs when scaling up. Expanding operations necessitates more hardware, skilled IT staff, and advanced infrastructure management, leading to higher expenses in hardware, personnel, and operational costs. Scaling open-source vector databases can be financially demanding despite the lack of licensing fees.
 
-## Addressing the Challenges with Open-Source Vector Databases
+## Addressing the challenges
 
 A fully managed database service helps developers avoid the hassles from setting up, maintaining, and relying on community assistance for an open-source vector database. The Integrated Vector Database in Azure Cosmos DB for MongoDB vCore offers a life-time free tier. It allows developers to enjoy the same financial benefit associated with open-source vector databases, while the service provider handles maintenance, updates, and scalability. When it’s time to scale up operations, upgrading is quick and easy while keeping a low [total cost of ownership (TCO)](introduction.md#low-total-cost-of-ownership-tco).
 
 ## Next steps
 > [!div class="nextstepaction"]
-> [Create a lifetime free-tier vCore cluster for Azure Cosmos DB for MongoDB]
+> [Create a lifetime free-tier vCore cluster for Azure Cosmos DB for MongoDB](free-tier.md)