Algolia vs Amazon Machine Learning

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Algolia

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Amazon Machine Learning

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Algolia vs Amazon Machine Learning: What are the differences?

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; Amazon Machine Learning: Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn complex ML algorithms & technology. This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.

Algolia can be classified as a tool in the "Search as a Service" category, while Amazon Machine Learning is grouped under "Machine Learning as a Service".

Some of the features offered by Algolia are:

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

On the other hand, Amazon Machine Learning provides the following key features:

  • Easily Create Machine Learning Models
  • From Models to Predictions in Seconds
  • Scalable, High Performance Prediction Generation Service

Medium, StackShare, and Product Hunt are some of the popular companies that use Algolia, whereas Amazon Machine Learning is used by Apli, Cymatic Security, and FetchyFox. Algolia has a broader approval, being mentioned in 258 company stacks & 54 developers stacks; compared to Amazon Machine Learning, which is listed in 9 company stacks and 10 developer stacks.

Advice on Algolia and Amazon Machine Learning
Rana Usman Shahid
Chief Technology Officer at TechAvanza · | 5 upvotes · 152.2K views
Needs advice
on
Firebase
Elasticsearch
and
Algolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

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Replies (2)
Josh Dzielak
Co-Founder & CTO at Orbit · | 7 upvotes · 115.5K views
Recommends
Algolia

Hi Rana, good question! From my Firebase experience, 3 million records is not too big at all, as long as the cost is within reason for you. With Firebase you will be able to access the data from anywhere, including an android app, and implement fine-grained security with JSON rules. The real-time-ness works perfectly. As a fully managed database, Firebase really takes care of everything. The only thing to watch out for is if you need complex query patterns - Firestore (also in the Firebase family) can be a better fit there.

To answer question 2: the right answer will depend on what's most important to you. Algolia is like Firebase is that it is fully-managed, very easy to set up, and has great SDKs for Android. Algolia is really a full-stack search solution in this case, and it is easy to connect with your Firebase data. Bear in mind that Algolia does cost money, so you'll want to make sure the cost is okay for you, but you will save a lot of engineering time and never have to worry about scale. The search-as-you-type performance with Algolia is flawless, as that is a primary aspect of its design. Elasticsearch can store tons of data and has all the flexibility, is hosted for cheap by many cloud services, and has many users. If you haven't done a lot with search before, the learning curve is higher than Algolia for getting the results ranked properly, and there is another learning curve if you want to do the DevOps part yourself. Both are very good platforms for search, Algolia shines when buliding your app is the most important and you don't want to spend many engineering hours, Elasticsearch shines when you have a lot of data and don't mind learning how to run and optimize it.

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Mike Endale
Recommends
Cloud Firestore

Rana - we use Cloud Firestore at our startup. It handles many million records without any issues. It provides you the same set of features that the Firebase Realtime Database provides on top of the indexing and security trims. The only thing to watch out for is to make sure your Cloud Functions have proper exception handling and there are no infinite loop in the code. This will be too costly if not caught quickly.

For search; Algolia is a great option, but cost is a real consideration. Indexing large number of records can be cost prohibitive for most projects. Elasticsearch is a solid alternative, but requires a little additional work to configure and maintain if you want to self-host.

Hope this helps.

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Pros of Algolia
Pros of Amazon Machine Learning
  • 125
    Ultra fast
  • 95
    Super easy to implement
  • 73
    Modern search engine
  • 71
    Excellent support
  • 70
    Easy setup, fast and relevant
  • 46
    Typos handling
  • 40
    Search analytics
  • 31
    Designed to search records, not pages
  • 30
    Multiple datacenters
  • 30
    Distributed Search Network
  • 10
    Smart Highlighting
  • 9
    Search as you type
  • 8
    Instantsearch.js
  • 8
    Multi-attributes
  • 6
    Super fast, easy to set up
  • 5
    Amazing uptime
  • 5
    Database search
  • 4
    Realtime
  • 4
    Great documentation
  • 4
    Highly customizable
  • 4
    Github-awesome-autocomple
  • 3
    Powerful Search
  • 3
    Beautiful UI
  • 3
    Places.js
  • 2
    Integrates with just about everything
  • 2
    Awesome aanltiycs and typos hnadling
  • 1
    Fast response time
  • 1
    Smooth platform
  • 1
    Github integration
  • 1
    Developer-friendly frontend libraries
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    Cons of Algolia
    Cons of Amazon Machine Learning
    • 10
      Expensive
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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 Amazon Machine Learning?

      This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use Algolia?
      What companies use Amazon Machine Learning?
      See which teams inside your own company are using Algolia or Amazon Machine Learning.
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      What tools integrate with Algolia?
      What tools integrate with Amazon Machine Learning?
        No integrations found

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        What are some alternatives to Algolia and Amazon Machine Learning?
        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