Azure AI Search (formerly Azure Cognitive Search) is enterprise search as a service, from Microsoft.
$0.10
Per Hour
Coveo Relevance Cloud
Score 7.7 out of 10
N/A
Coveo is an enterprise search technology which can index data on disparate cloud systems making it easier to retrieve. It has integrated plug-ins for Salesforce.com, Sitecore CEP, and Microsoft Outlook and SharePoint.
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.
Coveo Relevance Cloud is a great solution to implement into Salesforce to provide Knowledge-Centered Support, Enhancements to a Customer Community, to provide sales aids, or to complement your customized app in Salesforce.
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.
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.
It would be great if Coveo 6 allowed you to rebuild indexes from a certain subtree instead of needing to rebuild the entire tree to see changes. This functionality was added in Coveo 7 and is very useful.
In Coveo 6, integration with Sitecore is more difficult than one would expect. This integration is much improved in Coveo 7.
I have seen cases where an exception thrown when crawling a specific document will cause the indexing to stop completely. I believe this only happens in implementations using custom faceting but it could be handled more efficiently if the trouble document was skipped and the indexing could continue.
Relevancy ranking editor is good but not as powerful as GSA. GSA offers a self-learning scorer which automatically analyzes user behavior and the specific links that users click on for specific queries to fine tune relevance and scoring.
We've ran into issues on multiple clients with Sitecore items being indexed multiple times in Sitecore 7 and Coveo 7. The fix Coveo suggested was to upgrade our Sitecore version and Coveo but unfortunately this didn't resolve our issue. After months of testing we were finally able to resolve this by implementing our own CoveoItemCrawler to get around the issue (based on https://developers.coveo.com/display/public/SC201404/Items+in+the+Same+Language+Gets+Indexed+Multiple+Times;jsessionid=3C1A2AE33540E0A0B8BB52BA3A64AF70).
Integration with RabbitMQ in Coveo 7 seems error prone. We often see the error "The AMQP operation was interrupted" and on occasion, need to restart the Coveo service to get this operating again. In some extreme cases, we have also had to restart the server because of issues when attempting to restart the Coveo service.
As I've mentioned, the biggest competitor to Azure Search is actually Azure SQL Database. It doesn't have as many features, but it's more economical and most .Net applications will have one already. As long as you can arrive at a schema and ranking strategy, it's a "good enough" solution. There are a variety of search technologies (Lucene, Solr, Elasticsearch) that implement a search service. Some of them are even open source, though I would only say "free" if you do not value your time. They most likely need to be hosted via Container (or VM if you're old school), so you're incurring DevOps costs to not only set them up but monitor and maintain them yourself.
If you're already on AWS, there is almost no reason to use Azure Search. Unless you're already multi-cloud, desperately need the cognitive abilities, and don't mind a potential performance hit from looking across datacenters (hey, it could happen), you should probably just use Amazon CloudSearch.
Quick to find things in a massive database when needed.
Results need to be more concise - sometimes we spend more time looking for the right file than if we were to just search amongst our own networks instead.
Coveo is not always the most useful but does its job when general information is needed.
