Algolia vs Elasticsearch: What are the differences?
Developers describe Algolia as "Developer-friendly API and complete set of tools for building search". Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard. On the other hand, Elasticsearch is detailed as "Open Source, Distributed, RESTful Search Engine". Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
Algolia and Elasticsearch can be categorized as "Search as a Service" tools.
Some of the features offered by Algolia are:
- Database search
- Search as you type
On the other hand, Elasticsearch provides the following key features:
- Distributed and Highly Available Search Engine.
- Multi Tenant with Multi Types.
- Various set of APIs including RESTful
"Ultra fast", "Super easy to implement" and "Easy setup, fast and relevant" are the key factors why developers consider Algolia; whereas "Powerful api", "Great search engine" and "Open source" are the primary reasons why Elasticsearch is favored.
Elasticsearch is an open source tool with 42.4K GitHub stars and 14.2K GitHub forks. Here's a link to Elasticsearch's open source repository on GitHub.
Uber Technologies, Instacart, and Slack are some of the popular companies that use Elasticsearch, whereas Algolia is used by Medium, StackShare, and Product Hunt. Elasticsearch has a broader approval, being mentioned in 2000 company stacks & 976 developers stacks; compared to Algolia, which is listed in 258 company stacks and 54 developer stacks.
What is Algolia?
What is Elasticsearch?
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Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.
Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.
Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.