Azure AI Search (formerly Azure Cognitive Search) is enterprise search as a service, from Microsoft.
$0.10
Per Hour
IBM Watson Content Analytics
Score 9.0 out of 10
N/A
IBM Watson Content Analytics is an enterprise search option. This supersedes IBM's older offerings, IBM Omnifind and IBM Content Analytics and 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.
If one is looking for a content crawling search engine (think "Google" but on your own private system), IBM Watson does a great job. It is also very good for locating duplicate files/folders and lost items. If document organization and searching is the goal, IBM Watson hits the nail on the head.
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.
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.
IBM Watson is not quite in the same category as Worldox or NetDocuments as both are full-fledged document management. However, both vendors provide a similar searching and indexing product. Worldox provides searching and indexing but the Indexer is somewhat prone to issues. IBM Watson does not have the stability/consistency issues. NetDocuments is cloud-hosted document management and its index does not seem to have issues. That being said, there is a large premium as the data is all stored in a cloud container with the management system.
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.
