We performed a comparison between Algolia, Amazon AWS CloudSearch, and Elastic Search based on real PeerSpot user reviews.
Find out what your peers are saying about Elastic, Amazon, Microsoft and others in Search as a Service."The Algolia solution really helped us to improve our conversion rate and click through rate."
"It's the best solution for any company. It has a hosting ERP system for any task. AWS is stable. AWS is more flexible and its elastic concept is a new concept. AWS is also very secure. It has many layers of security, like hardware security and software security. This is a big issue."
"Document indexing, text-based search API, and Geospatial searches are all good features."
"The quality of the solution is good."
"The best feature is its scalability in that Cloud is always on the fly."
"I've found the solution to be very scalable."
"It will remain alive in the market. The solution will be stable in the market."
"AWS CloudSearch's best features are good performance under high CPU and memory use, and ease of deployment and scaling."
"The most valuable feature of Amazon AWS CloudSearch is its ability to receive data quickly. You can access your data easily in a short time."
"Dashboard is very customizable."
"The initial setup is very easy for small environments."
"The most valuable feature of Elastic Enterprise Search is the Discovery option for the visualization of logs on a GPU instead of on the server."
"The most valuable features are the detection and correlation features."
"Implementing the main requirements regarding my support portal."
"A good use case is saving metadata of your systems for data cataloging. Various systems, like those opened in metadata and similar applications, use Elasticsearch to store their text data."
"The solution has good security features. I have been happy with the dashboards and interface."
"I appreciate that Elastic Enterprise Search is easy to use and that we have people on our team who are able to manage it effectively."
"I think they could improve the analytics view."
"The price of the solution can be expensive."
"Index cleanup is sometimes painful. No easy way to clean indexes or a bulk of documents. Full indexing or regeneration of entire indexes sometimes gets stuck. In one instance, we had to delete the entire index and re-create it."
"Security is a concern but they're working on it."
"The solution should improve the recovery aspects that it has on offer."
"Latlon data type only supports single value per document. All other types support multiple values. We faced issues with this because we had scenarios where, for each document, we needed to store multiple latlon values for different geographical locations."
"I would say that it needs to keep its cost competitive in the market, especially in comparison to other clouds."
"AWS CloudSearch's documentation isn't very clear. Also, the on-premise version of the solution is less stable than the cloud version."
"We'd like to see more database features."
"Machine learning on search needs improvement."
"There is an index issue in which the data starts to crash as it increases."
"Better dashboards or a better configuration system would be very good."
"I would rate the stability a seven out of ten. We faced a few issues."
"The solution has quite a steep learning curve. The usability and general user-friendliness could be improved. However, that is kind of typical with products that have a lot of flexibility, or a lot of capabilities. Sometimes having more choices makes things more complex. It makes it difficult to configure it, though. It's kind of a bitter pill that you have to swallow in the beginning and you really have to get through it."
"Something that could be improved is better integrations with Cortex and QRadar, for example."
"The UI point of view is not very powerful because it is dependent on Kibana."
"The price could be better. Kibana has some limitations in terms of the tablet to view event logs. I also have a high volume of data. On the initialization part, if you chose Kibana, you'll have some limitations. Kibana was primarily proposed as a log data reviewer to build applications to the viewer log data using Kibana. Then it became a virtualization tool, but it still has limitations from a developer's point of view."