We used the semantic search capabilities of Azure AI Search, but we haven't gotten good results in the semantic search. So we are exploring with ChromaDB, and Cosmos is having the capability of doing the semantic search as well. We are exploring that. A few queries we use analytics search, which works and is good. Analytics search is good. We are trying the ML capabilities of the product since we are using Databricks and other tools for building the models, MLflow, and related items. We are still working on proof of concepts, which could be better with ChromaDB or Cosmos or vector search or inbuilt Databricks vector stores. Language processing is not about user intention; it's about the context. If there is a document and you want to know the context of a particular section, then we would use vector search. Instead of traversing through the whole document, while chunking it into the vector, we'll categorize and chunk, and then we'll look only at those chunks to do a semantic search. When comparing Azure AI Search, I'm doing a proof of concept because with ChromaDB I can create instances using LangChain anywhere. For per session, I can create one ChromaDB and can remove it, which is really useful for proof of concepts. Instead of creating an Azure AI Search instance and doing that there, that is one advantage I'm seeing for the proof of concept alone, not for the entire product. I hope it should support all the embedding providers as well. Is there a viewer or tool similar to Storage Explorer? We are basically SQL-centric people, so we used to find Cosmos DB very quick for us when we search something and create indexes. I guess there is some limitation in Azure AI Search. I couldn't remember now, such as querying limitations. I'm not remembering that part. View full review »

The pricing is room for improvement. However, I assume they will once the cloud providers start making money.

Moreover, from a performance perspective, there is some room for improvement.

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The solution's search patterns are a bit intuitive. It would be good if the site found a better way to filter things based on subscription. Also, it can work on giving a good prebuilt view, like what we get during the live stage.

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I would definitely say that the user interface could be improved, for sure. Azure Search could stand to be more user-friendly in general from the initial setup on.  

The documentation for the setup should be simplified and have more exacting detail. I do not think it is well executed and as helpful as it should be.  

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The after-hour services are slow. It could be better.

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The solution's stability and support could be better.

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They should add an API for third-party vendors, like a security operating center or reporting system, that would be a big improvement.

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As the product continues to be developed, there are several areas where improvements can be realized. For SDKs, Azure Search currently offers solutions for .NET and Python. Additional platforms would be welcomed, especially native iOS and Android solutions for mobile development. For availability, expanding its use to all Azure datacenters would be helpful in increasing awareness and usage of the product.

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• Adding items to Azure Search using its .NET APIs sometimes throws exceptions.
• I have encountered few exceptions while adding items using a console application. I do not know the actual reason behind them.
• That console app used to insert data in Cosmos DB and the Search service. I did not face any issue with Cosmos DB though.

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