Azure AI Search vs. Inbenta Enterprise Search

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.

Incentivized

If you are looking for an enterprise search platform that offers an easy to use interface and if high performance is not really your priority, then I believe Inbenta Enterprise Search would fit your use case. For most other situations, I would recommend using a tried and tested search platform like Apache Solr.

Incentivized

• 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.

Incentivized

• Fairly easy to use interface.
• Quick to onboard new users, both in terms of access as well as training.
• The NLP-based semantic search feature is quite impressive.

Incentivized

• 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.

Incentivized

• It is a fairly new tool in the market, so not much market feedback exists for this. This made us apprehensive in using it.
• The documentation and online forums are bare minimum.
• Quite buggy when working with multiple sources.

Incentivized

I give 10 rating because by using this endpoint and api key only we able to build that chatbot product in a timeline given by our client and also creating the endpoint and keys from the portal is also very easy for Azure AI Search and it doesn't take much time and also scalability is good.

It is good for me, and I want to rate this product 9/10. I hope they continue to improve and also offer a free plan with more benefits to learn Azure AI Search.

Incentivized

Apache Solr to Inbenta is like Java to Python. Apache Solr is powerful and handles complex search platforms well. Inbenta, on the other hand, is meant to be easy to use and does not lay too much emphasis on performance. If you are a small or medium business, Inbenta may suit your needs better. However, if you wish to manage a large and complex search platform, Solr is the way to go.

Incentivized

• When integrated with our existing file system the Azure AI Search helped users tremendously by reducing search times and improve efficacy of intended result.
• Since Azure AI Search is a PaaS solution, we had very short ideation to go-live timespan, which ended up reflecting in our product performance.
• A rare but not negligible occurrence was correctness of search being questionable when new data was added to the system. The search returns false positive results.

Incentivized

• Enabled our developers to quickly deploy changes to the search model.
• NLP-powered semantic search feedback provides good actionable insights.

Incentivized