Update vector-search-ai.md

Author: wmwxwa

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

@@ -54,25 +54,25 @@ Choosing the best open-source vector database requires considering several facto
 
 ## Challenges of open-source vector databases
 
-Open-source vector databases pose challenges that are typical of open-source software as well as ones that are unique to many vector databases.
+Open-source vector databases pose challenges that are typical of open-source software and ones that are specific to many vector databases.
 
-Challenges with open source: 
+### Challenges with open source: 
 
 - 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 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.
 
-Challenges that are unique to vector databases:
+### Challenges that are specific to vector databases:
 
-Most open-source vector databases nowadays are pure vector databases. In other words, they are designed to store and manage vector embeddings, along with a small amount of metadata; it is separate from the data source from which the embeddings are derived. Thus, using pure vector databases requires sending your data between service integrations, which adds additional cost, complexity, and bottlenecks for your production workloads.
+Most open-source vector databases nowadays are pure vector databases. In other words, they are designed to store and manage vector embeddings, along with a small amount of metadata; it is separate from the data source from which the embeddings are derived. Thus, using pure vector databases requires sending your data between service integrations, which adds extra cost, complexity, and bottlenecks for your production workloads.
 
 ## Addressing the challenges of open-source vector databases
 
 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; moreover, some managed vector database services offer a life-time free tier.
 
-The additional cost and complexity of pure vector databases can be avoided by using a vector database that is integrated in a highly performant NoSQL or relational database, which stores, indexes, and queries embeddings alongside the corresponding original data. This approach eliminates the extra cost of replicating data in a separate pure vector database. Moreover, keeping the vector embeddings and original data together better facilitates multi-modal data operations, and enables greater data consistency, scale, and performance.
+The extra cost and complexity of pure vector databases can be avoided by using a vector database that is integrated in a highly performant NoSQL or relational database, which stores, indexes, and queries embeddings alongside the corresponding original data. This approach eliminates the extra cost of replicating data in a separate pure vector database. Moreover, keeping the vector embeddings and original data together better facilitates multi-modal data operations, and enables greater data consistency, scale, and performance.
 
 An example is the Integrated Vector Database in Azure Cosmos DB for MongoDB. 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).