Apache Solr vs. Inbenta Enterprise Search

Very effective for end-user searching applications and for generating search results. Also very well suited to those looking for high reliability and performance. If [you're doing] fuzzy searching or if you are working on a smaller end-user application or an internal application that does not require high performance and flexible/adapting searching then it may not be necessary to use Solr.

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

• Faceted navigation and field collapsing/grouping : filtering and quick results were what we needed for our websites. Our customers needed to have this functionalities for good and efficient results.
• We tested them with our customers' registered searches (they received all new goods matching with their registered searches by emails and/or mobile push). Results were incredible by comparison with our old system (old MySQL requests).
• Note : we didn't put all our data in Solr. Just what we need for searching uses. Other data stayed in our MySQL database.
• Auto-suggest : our old auto-suggest wasn't performing well. With Apache Solr, our new one was worked really well ! The suggestions came quickly and suggestions were good.
• We also extended auto-suggestion with geo-spatial data and it worked well.
• Hit highlighting : we used this functionality and we didn't have problem and nasty surprise.
• Keep all data status during data upgrading (see next details for improvements)

• 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

• Ease of use -- this is not always the easier platform to configure.
• Developer Community -- There are not a whole lot of folks out there that understand this stuff so finding talent is difficult.
• Drupal 8 -- We've been frustrated with slow development on APIs for Drupal 8 integration.

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

It takes some time to deploy and currectly maintein it. And also, to learn how to use and integrate in the enviroment as well. Once you get theses steps done, it usability is very simple, and almost of the time it don't require no further attention on it. Even for maintence, if you deploy it on a cluster mode, it is very reliable and easy to take one host down.

Incentivized

We switched from search indexes stored in MySQL to soar and it's made a world of difference for our growing businesses. The relational databases are very poor for handling the complex data searches require and Solr delivered all the tools we need to get the performance our end users are demanding.

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

• It's enabled us to deliver fast, relevant search results on our new website. The site is still in beta and being actively developed so our complete ROI is still unknown.
• It integrates very well with Drupal so it has saved us from having to develop a custom solution.

Incentivized

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

Incentivized