Algolia vs Elasticsearch

• Algolia is lightning quick -- the fastest we found
• Algolia has great tech support, tons of documentation, and many high quality ready-made plugins
• Algolia allows for a high degree of customization

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• Indexing. Elasticsearch can index thousands of documents per second.
• Searching. Elasticsearch provides plenty of options for querying your data to get just the right information back.
• Scalability. Elasticsearch has built-in features for replicating data and distributing load, so you don't have to invest a ton of time and effort into third-party or customized clustering and/or sharding solutions.
• Backup. Elasticsearch has built-in options for backing up your data. If you're dealing with a large cluster, backing things up can get rather interesting from a storage perspective, but Elasticsearch has worked very well for us thus far.
• Recovery. If part of your cluster goes offline, Elasticsearch generally does a decent job of staying online and recovering from the outage. Occasionally you'll lose nodes that house all copies of a given set of shards (which isn't fun), but Elasticsearch still handles that situation as well as can be expected.

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• It is very expensive compared to elastic search which is relatively free.
• Algolia does not support iPhone and Android, unlike Sharefile and Diqify
• Algolia lacks the activity tracking and email integration
• Algolia does not have any video tutorials.

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• The online documentation is very difficult to use, both as a teaching tool and as a quick reference. The search syntax is arcane and not particularly "human friendly" and examples from the documentation are often insufficiently detailed to apply directly.
• ElasticSearch is touted as "schemaless" when in fact mappings (aka schemas) are required for all but the most basic use cases.

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• Algolia is very fast but also very expensive. However, the size of our organization is very huge, so cost is not much of a problem.
• Algolia does not support iPhone and Android. This is a major issue, which is why we were considering moving to swiftype.
• Since it has integration issues, we could not fully leverage the software.

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• (Negative) Expense: Just Time. Early on, I had issues getting it installed in an exotic distribution, so labor/hours invested.
• (Positive) Configuration and Modularity: You don't *have* to implement a full ELK stack. In fact, you could just run one, two or three components. However, marrying up ES with something like Syslog-ng, you combine two very powerful, feature-rich software packages in their own right, into an amazingly powerful data collection and gathering tool.
• (Positive) Shallow learning curve: if you can write your own Unix configuration files, you will be able to maintain and develop on Elasticsearch.

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