Azure AI Search (formerly Azure Cognitive Search) is enterprise search as a service, from Microsoft.
$0.10
Per Hour
Searchspring
Score 9.0 out of 10
N/A
Searchspring headquartered in Denver offers intelligent site search for customer facing web pages and ecommerce, providing product discvoery tools, navigation viacategory page, and other features to improve site navigation.
In February 2020, Searchspring merged with Nextopia to expand its product capabilities, and customer base. Nextopia customers will continue to receive the same services, under the SearchSpring brand.
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.
Search Spring offers strong options for search customizations: synonyms, redirects, query replacements, spell corrections, etc. We enjoy the ability to boost and unique product display options. We were 4Tell customers prior to the Search Spring acquisition and we're looking forward to both being part of one console. Search Spring is a really solid, stable search/merch platform that I would recommend for any mid-market business.
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.
Developing 'cocktails' of different ranking criteria. At the moment we can only serve results based on either 'relevancy' or 'sales performance'. It would be great to not only have the ability to blend these two options (by search term), but also add additional facets into the mix, such as stock quantity, margin, sponsorship factor etc...
Provide financing reporting on results - so we know how much revenue/conversion has been driven from specific search terms. For example, "Baby Milk" drove 50 searches, 6 direct conversions (customers that searched went on to buy an item(s) that were recommended), 16 indirect conversions (customers that searched went on to buy other item(s) not severed).
I want to improve their product and also want to learn Azure AI Search like a professional and use it with full feature but their price is too high, so now I use the free plan as of now, but it takes a very large amount of data, type is few minutes, and give result that I want.
We have a monthly phone call with our account manager, and she is available for calls in between as well. She has always been accessible. Working with her has been easy and she has provided training where needed. She is proactive in making sure we have everything we need and feel comfortable with the platform.
Nextopia’s features were on par or better than consideration set at a lower cost and with an easier implementation. Contract terms were also more favorable.
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.
