We use Azure AI Search in our organization to bring relevant chunk text from our database postgres pgadmin vector db for our chatbot based model. Based on the user query, by using the retrieval mechanism connect with Azure AI Search ranking and relevant text will be retrieved from the database to answer the user query from our trained document.
Pros
Relevant query text finding the answer from the chunk text
Accurate output for the chatbot it works well
Faster response it works well
Cons
If the data is larger or heavy there it have to work
If the data is larger accuracy should be improved
Large data handling time also important thing to work faster
Likelihood to Recommend
It is a document based query for the pdf, docs, docx files, manifest based links which are the knowledge base will be trained and stored in db there from that user will ask a question and our chatbot ai agent have to answer fastly and accurately there AI Azure Search play a vital role to bring accurate and fast output.
Azure AI Search is heavily being used by our file management system to search and retrieve items based on search token provided. The Azure AI Search is very versatile in working with multiple sources of data and ability to integrate with Chat Bot is very handy. The implementation is very easy and seamless.
Pros
The Azure AI Search has all state-of-the-art LLM models ready to use for our enterprise grade apps.
Azure AI Search seamlessly integrates with our Azure infrastructure and provide most advance security for our sensitive data.
It's hybrid search ability makes it much faster than the conventional search algorithm which would require us to index the database.
Cons
Azure AI Search formerly known as Cognitive search has very limited development SKU model for getting started which makes it costly while just getting started.
We had an issue with limited file size capped at 16Mb, which turned into a system wide limitation for our product since many of our blobs exceeded the 16Mb mark.
For larger systems the REST rate limits for the query API can become a headache if you don't plan for possible influx of peak requests.
Likelihood to Recommend
It's very useful when used with large file systems, once the models index the files good enough, the suggestions are very impressive and produce grounded answers. Since it can natively work with blob storage the requirement for pre-processing the data is eliminated i.e. the data can be searched in its raw form, this makes Azure AI Search a very powerful tool when used with Azure Stack.
I can search for any type of data, such as images, audio, and multilingual text, as well as large, complex datasets. It saved my time, and it is really a fully intelligent AI that has more problem-solving skills than other AI models, and I get the exact answer to any data
Pros
Takes very complex data.
Get any type of data that you need at right time.
Filters are very good to search for data.
Cons
The free plan storage is very low for anyone who want to lean azure AI search.
The plan is too high for me.
They need to intricate ai search is every Microsoft ecosystem.
Likelihood to Recommend
From a beginner's point of view, I need to take a comprehensive course to gain hands-on experience with Azure AI and unlock its full potential. The price is too high compared to competitors, and the storage limit in the free plan is too low for my use.
Azure AI Search is increasingly being utilized in the TV broadcast industry which address various challenges and enhance content delivery and management. We uses it for content creation and disribution. This helps us in managing workflows, quick dicision making , timely delivery of projects. This helps us to understand audience preferences , based on this content is being created considering individuals mood and interest.
Pros
Content recommendations, AI recommends shows and content as per viewer preferences , which enhances user experience.
This helps in managing workflows which makes decision making easier and time delivery of project.
with the help of Generative AI, it increased scalability of all AI applications.
Cons
Cross platform compatibility to integrate with various OS
Optimizing latency.
Nothing better than work on price, create more flexible options.
Likelihood to Recommend
Advanced analytics and machine learning algorithms provide insights into audience trends and engagement patterns, aiding in targeted content creation and distribution strategies, it enhance user experience and create more users.
VU
Verified User
Manager in Information Technology (501-1000 employees)
Azure Search is an incredibly robust component of Microsoft's Azure Cloud, which we use at our company for web hosting. We've integrated Azure Search into our website for the purpose of search queries and auto-complete suggestions when checking out via our e-commerce platform. Reducing friction to purchase on a checkout page is of paramount importance. When a user is logged in, we provide auto-complete suggestions based on their previous checkout information. This way, the user can easily tab through their ship-to/bill-to/billing information for minimum purchase friction.
Pros
Incredibly robust back-end infrastructure.
Streamlined integration into Microsoft's Azure Cloud.
From a user standpoint, it lets the customer easily access their data and provide useful search tips.
Cons
It's an enterprise level product so you need to have the budget for it.
Challenging-to-impossible for a non-technical administrator to implement.
It further locks you into Microsoft's ecosystem and doesn't play well with non-Microsoft software. Depending on your point of view, this can be a pro or a con.
Likelihood to Recommend
Incredibly robust software for an enterprise organization to plug into their application. If you have a full development resource team at your disposal, this is great software and I highly recommend it. Largely, however, you won't be able to use this prior to the enterprise level. It's just too complicated and cumbersome of a product.
Azure Search provides an intelligent, AI-powered discovery engine for content in LiveTiles Cloud - an Intelligence Experience Platform (IXP) that allows anyone to build a website for their organization.
Pros
Azure Search provides a fully-managed service for loading, indexing, and querying content.
Azure Search has an easy C# SDK that allows you to implement loading and retrieving data from the service very easy. Any developer with some Microsoft experience should feel immediate familiarity.
Azure Search has a robust set of abilities around slicing and presenting the data during a search, such as narrowing by geospatial data and providing an auto-complete capabilities via "Suggesters".
Azure Search has one-of-a-kind "Cognitive Search" capabilities that enable running AI algorithms over data to enrich it before it is stored into the service. For example, one could automatically do a sentiment analysis when ingesting the data and store that as one of the searchable fields on the content.
Cons
Like virtually all Azure services, it has first-class treatment for .Net as the developer platform of choice, but largely ignores other options. While there is a first-party Python SDK, there are only community packages for other languages like Ruby and Node. Might be a game of roulette for those to be kept up-to-date. This might make it a non-starter for some teams that don't want to do the work to integrate with the REST API directly.
In my opinion, partitions inside of Azure Search don't count as data segregation for customers in a multi-tenant app, so any application where you have many customers with high-security concerns, Azure Search is probably a non-starter.
To elaborate on the multi-tenant issue: Azure Search's approach to pricing is pretty steep. While there is a free tier for small applications (50MB of content or less) the first paid tier is about 14x more expensive than the first SQL Database tier that supports full-text search. For many applications, it makes a lot more economic sense to just run some LIKE or CONTAINS queries on columns in a table rather than going with Azure Search.
Likelihood to Recommend
If you have a medium amount of data (2GB - 2.4TB), high-security concerns, and search is a key requirement in your single-tenant application then Azure Search likely has you covered. If you have a small amount of data per tenant (EG, about 2GB), have low-security concerns, and a multi-tenant application where search is a key requirement, then Azure Search would likely be a good choice - though you would need to implement your own concept of sharding and managing across potentially multiple Azure Search instances.
If you can reflect your would-be indexes in Azure Search by depositing the data in columns in a SQL table and just index it for full-text search - and that still fits your requirements - it's probably better to start with SQL Database then scale up to Azure Search when you need the advanced features like ranking or cognitive abilities.
