Algolia聽vs聽Found Elasticsearch

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Algolia vs Found Elasticsearch: What are the differences?

What is Algolia? 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.

What is Found Elasticsearch? Hosted Elasticsearch. Create your own fully managed and hosted Elasticsearch cluster. You get a dedicated cluster with reserved memory, giving you predictable performance. There are no arbitrary limits on how many indexes or documents you can store. Scale your clusters as and when needed, without any downtime.

Algolia and Found Elasticsearch can be categorized as "Search as a Service" tools.

Some of the features offered by Algolia are:

  • Database search
  • Multi-attributes
  • Search as you type

On the other hand, Found Elasticsearch provides the following key features:

  • Hosted and managed: You get your own fully hosted and managed Elasticsearch cluster. No need to host and maintain your own costly search infrastructure.
  • Reserved Memory and storage: Your clusters get reserved memory and storage. No shared clusters and no arbitrary limits on how many indexes or documents you can store.
  • Scalable and flexible: Start small, grow big. You can scale your cluster as and when needed, without any downtime. There are several Elasticsearch versions to choose from, and upgrading is easier than ever.
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What is Algolia?

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.

What is Found Elasticsearch?

Create your own fully managed and hosted Elasticsearch cluster. You get a dedicated cluster with reserved memory, giving you predictable performance. There are no arbitrary limits on how many indexes or documents you can store. Scale your clusters as and when needed, without any downtime.
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Why do developers choose Algolia?
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      What tools integrate with Algolia?
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        What are some alternatives to Algolia and Found Elasticsearch?
        Elasticsearch
        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).
        Solr
        Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.
        Swiftype
        Swiftype is the easiest way to add great search to your website or mobile application.
        Azure Search
        Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios.
        Klevu
        It is an intelligent site search solution designed to help eCommerce businesses increase onsite sales and improve the customer online shopping experience.
        See all alternatives
        Decisions about Algolia and Found Elasticsearch
        Tim Specht
        Tim Specht
        鈥嶤o-Founder and CTO at Dubsmash | 16 upvotes 124.9K views
        atDubsmashDubsmash
        Elasticsearch
        Elasticsearch
        Algolia
        Algolia
        Memcached
        Memcached
        #SearchAsAService

        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.

        #SearchAsAService

        See more
        Julien DeFrance
        Julien DeFrance
        Principal Software Engineer at Tophatter | 16 upvotes 859.2K views
        atSmartZipSmartZip
        Rails
        Rails
        Rails API
        Rails API
        AWS Elastic Beanstalk
        AWS Elastic Beanstalk
        Capistrano
        Capistrano
        Docker
        Docker
        Amazon S3
        Amazon S3
        Amazon RDS
        Amazon RDS
        MySQL
        MySQL
        Amazon RDS for Aurora
        Amazon RDS for Aurora
        Amazon ElastiCache
        Amazon ElastiCache
        Memcached
        Memcached
        Amazon CloudFront
        Amazon CloudFront
        Segment
        Segment
        Zapier
        Zapier
        Amazon Redshift
        Amazon Redshift
        Amazon Quicksight
        Amazon Quicksight
        Superset
        Superset
        Elasticsearch
        Elasticsearch
        Amazon Elasticsearch Service
        Amazon Elasticsearch Service
        New Relic
        New Relic
        AWS Lambda
        AWS Lambda
        Node.js
        Node.js
        Ruby
        Ruby
        Amazon DynamoDB
        Amazon DynamoDB
        Algolia
        Algolia

        Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

        I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

        For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

        Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

        Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

        Future improvements / technology decisions included:

        Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

        As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

        One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

        See more
        Interest over time
        Reviews of Algolia and Found Elasticsearch
        Review ofFound ElasticsearchFound Elasticsearch

        We use found.no to host our elasticsearch index for ninya.io[1].

        When we were looking for an elasticsearch provider we compared a couple of different services. One thing that we considered as a big advantage over other elasticsearch provider is the simple pricing. Most other elasticsearch provider use document limits whereas found.no uses storage quota which makes more sense to us.

        found.no supports all elasticsearch features and has been very reliable for us. They also have an amazing customer support that helped us to resole issues (on our end) quickly when we didn't know how to help ourselves.

        Getting started with found.no was a piece of cake. There's everything you need right at your dashboard. They even generate examples for exactly your account which is really helpful to get newcomers started.

        For everything else there is great documentation right on there website[2]. I'd also like to recommend their blog[3] that is full of technical articles not necessarily scoped to their service but also elasticsearch in general.

        Review ofAlgoliaAlgolia

        Maxime is a big supporter of Product Hunt, recognizing the continual request that I add search to the product from others in the community. Having seen many frustrating search implementations on other sites, I assumed it would be hard to integrate and provide something useful. Algolia proved me wrong (see the results here: http://producthunt.co/search).

        I'm impressed with the speed and amazing support from the Algolia team. The dashboard analytics and management are incredibly useful, providing insight into how people are using the product and ability to act on those learning without changing a line of code. I would highly recommend it.

        Review ofAlgoliaAlgolia

        Having a great search engine is extremely important for our app store. We find that users love to search, not only when they know what they are looking for but also to discover content around different themes.

        In a very rushed period with lots of things to do in parallel, we found that Algolia offered a high quality solution that perfectly solved our problem and we had a first version working great in less than a day.

        We also enjoyed getting their feedbacks and ideas to help us improve our search and we are now using Algolia in our internal tools as well. We strongly recommend them!

        Review ofAlgoliaAlgolia

        We were looking for a better search solution for GrowthHackers.com for a couple months. All the options we looked at were either too complicated to setup, didn't have the features we needed, or were too expensive. Algolia hit the right balance for us. It's super fast and easy to customize, and the documentation and examples for getting started are great. Most importantly though, their support rocks. It's always a pleasure talking with their team.

        Review ofAlgoliaAlgolia

        I'm Antonio, TVShow Time's CEO, a startup that has more than 100k+ active users.

        Before Algolia, we were using Elastic Search that was costing a lot (hosted on 2 big EC2 instances) and with results that weren't that relevant.

        Then we switched to Algolia, in 1 hour. We were blown away by how easy the integration was for such a good relevance in results and high performance.

        Review ofAlgoliaAlgolia

        We tried a lot of services at Socialcam to handle our massive user base. All of them couldn't handle that number of users.

        Algolia handles it without any problem but on top of that, it does it at full speed: we get results back in under a few milliseconds. Last but not least, it does it with error handling, which is great as typos are very frequent on mobile...

        How developers use Algolia and Found Elasticsearch
        Avatar of Packet
        Packet uses AlgoliaAlgolia

        Algolia helps us search across disparate pieces of information in our staff portal, and allows customers to easily jump around the portal between devices, support conversations, and documentation.

        Avatar of kako.pk
        kako.pk uses AlgoliaAlgolia

        This is the bedrock of kako.pk - it not only serves the JSON data, it doubles as a (very fast) web-server if you connect to the client JS widget libraries

        Avatar of Shoes of Prey
        Shoes of Prey uses AlgoliaAlgolia

        We use algolia to power our product search / filtering (https://www.shoesofprey.com/shoes).

        Avatar of DevTube
        DevTube uses AlgoliaAlgolia

        We started with Algolia, but switched to our home-backed full-text search solution. It's serverless, based on Lunr.js

        Avatar of Pharmatolia
        Pharmatolia uses AlgoliaAlgolia

        For detailed searching listing and products posted by customers

        How much does Algolia cost?
        How much does Found Elasticsearch cost?
        News about Found Elasticsearch
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