Alternatives to SimilarWeb logo

Alternatives to SimilarWeb

Google Analytics, Alexa, Ahrefs, SEMrush, and App Annie are the most popular alternatives and competitors to SimilarWeb.
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What is SimilarWeb and what are its top alternatives?

SimilarWeb is a web analytics tool that provides insights into website traffic, engagement metrics, and other online performance indicators. It offers features such as traffic analysis, audience insights, keyword research, and competitive analysis. However, SimilarWeb has limitations in terms of accuracy and real-time data availability.

  1. SEMrush: SEMrush is an all-in-one marketing toolkit for digital marketing professionals. It offers features such as keyword research, backlink analysis, site audits, and much more. Pros include comprehensive analytics and data, while cons include a higher learning curve for beginners compared to SimilarWeb.
  2. Ahrefs: Ahrefs is a popular SEO tool that provides data on backlinks, organic search keywords, and competitor analysis. Key features include link analysis, keyword tracking, and content research. Pros include a user-friendly interface and comprehensive data, while cons include a higher price point than SimilarWeb.
  3. Moz: Moz offers tools for SEO, link building, keyword research, site audits, and more. It is known for its Domain Authority metric and keyword difficulty score. Pros include actionable insights and a strong community, while cons include limited data compared to SimilarWeb.
  4. SpyFu: SpyFu specializes in competitive intelligence for online advertising, PPC, and SEO. It offers features such as keyword research, competitor analysis, and ad history. Pros include comprehensive ad insights, while cons include limited data on organic search compared to SimilarWeb.
  5. SE Ranking: SE Ranking is an all-in-one SEO platform that offers tools for keyword rank tracking, website audit, backlink monitoring, and more. Pros include customizable reports and user-friendly interface, while cons include limited social media analytics compared to SimilarWeb.
  6. Serpstat: Serpstat is a growth hacking tool for SEO, PPC, and content marketing. It provides features such as keyword research, site audit, backlink analysis, and more. Pros include affordable pricing and detailed reports, while cons include a somewhat limited keyword database compared to SimilarWeb.
  7. Majestic: Majestic is a tool for backlink analysis and website trust flow metrics. It offers features such as Site Explorer, Link Context, and Campaigns. Pros include comprehensive backlink data, while cons include a focus on backlinks rather than overall website performance like SimilarWeb.
  8. Crazy Egg: Crazy Egg is a heat mapping and user behavior analytics tool for website optimization. It provides insights into visitor behavior, conversion rates, and usability. Pros include visual data representation and easy-to-use interface, while cons include limited focus on traffic and SEO like SimilarWeb.
  9. SerpWatch: SerpWatch is a rank tracker tool that helps monitor keyword positions in search engine results. It offers features such as daily ranking updates, competitor tracking, and advanced reporting. Pros include real-time ranking data, while cons include a narrower focus compared to the broad insights of SimilarWeb.
  10. Rank Ranger: Rank Ranger is an SEO and marketing platform that offers rank tracking, site audits, backlink monitoring, and more. It provides comprehensive data on keyword rankings, local SEO, and competitor analysis. Pros include advanced reporting features, while cons include a steeper learning curve compared to the user-friendly interface of SimilarWeb.

Top Alternatives to SimilarWeb

  • Google Analytics
    Google Analytics

    Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications. ...

  • Alexa
    Alexa

    It is a cloud-based voice service and the brain behind tens of millions of devices including the Echo family of devices, FireTV, Fire Tablet, and third-party devices. You can build voice experiences, or skills, that make everyday tasks faster, easier, and more delightful for customers. ...

  • Ahrefs
    Ahrefs

    Tools to grow your search traffic, research your competitors and monitor your niche. It helps you learn why your competitors rank so high and what you need to do to outrank them. ...

  • SEMrush
    SEMrush

    SEMrush is a powerful and versatile competitive intelligence suite for online marketing, from SEO and PPC to social media and video advertising research. ...

  • App Annie
    App Annie

    Annie takes care of all the Math Behind The App Stores keeping you up-to-date with your own app's metrics and the latest app store trends. Annie provides three fabulous products for her fans: Analytics, Store Stats, Intelligence. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

  • GitHub
    GitHub

    GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. ...

SimilarWeb alternatives & related posts

Google Analytics logo

Google Analytics

126.2K
48.5K
5K
Enterprise-class web analytics.
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48.5K
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PROS OF GOOGLE ANALYTICS
  • 1.5K
    Free
  • 926
    Easy setup
  • 890
    Data visualization
  • 698
    Real-time stats
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    Comprehensive feature set
  • 181
    Goals tracking
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    Powerful funnel conversion reporting
  • 138
    Customizable reports
  • 83
    Custom events try
  • 53
    Elastic api
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    Updated regulary
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    Interactive Documentation
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    Google play
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    Industry Standard
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    Irina
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    Financial Management Challenges -2015h
  • 1
    Lifesaver
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    Easy to integrate
CONS OF GOOGLE ANALYTICS
  • 11
    Confusing UX/UI
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    Super complex
  • 6
    Very hard to build out funnels
  • 4
    Poor web performance metrics
  • 3
    Very easy to confuse the user of the analytics
  • 2
    Time spent on page isn't accurate out of the box

related Google Analytics posts

Alex Step

We used to use Google Analytics to get audience insights while running a startup and we are constantly doing experiments to lear our users. We are a small team and we have a lack of time to keep up with trends. Here is the list of problems we are experiencing: - Analytics takes too much time - We have enough time to regularly monitor analytics - Google Analytics interface is too advanced and complicated - It's difficult to detect anomalies and trends in GA

We considered other solutions on a market, but found 2 main issues: - The solution created for analytic experts - The solution is pretty expensive and non-automated

After learning this fact we decided to create AI-powered Slack bot to analyze Google Analytics and share trends. The bot is currently working and highlights trends for us.

We are thinking about publishing this solution as a SaaS. If you are interested in automating Google Analytics analysis, drop a comment and you'll get an early access.

We will implement this solution only if we have 20+ early adaptors. Leave a message with your thought. I appreciate any feedback.

See more
Tim Specht
‎Co-Founder and CTO at Dubsmash · | 14 upvotes · 959.1K views

In order to accurately measure & track user behaviour on our platform we moved over quickly from the initial solution using Google Analytics to a custom-built one due to resource & pricing concerns we had.

While this does sound complicated, it’s as easy as clients sending JSON blobs of events to Amazon Kinesis from where we use AWS Lambda & Amazon SQS to batch and process incoming events and then ingest them into Google BigQuery. Once events are stored in BigQuery (which usually only takes a second from the time the client sends the data until it’s available), we can use almost-standard-SQL to simply query for data while Google makes sure that, even with terabytes of data being scanned, query times stay in the range of seconds rather than hours. Before ingesting their data into the pipeline, our mobile clients are aggregating events internally and, once a certain threshold is reached or the app is going to the background, sending the events as a JSON blob into the stream.

In the past we had workers running that continuously read from the stream and would validate and post-process the data and then enqueue them for other workers to write them to BigQuery. We went ahead and implemented the Lambda-based approach in such a way that Lambda functions would automatically be triggered for incoming records, pre-aggregate events, and write them back to SQS, from which we then read them, and persist the events to BigQuery. While this approach had a couple of bumps on the road, like re-triggering functions asynchronously to keep up with the stream and proper batch sizes, we finally managed to get it running in a reliable way and are very happy with this solution today.

#ServerlessTaskProcessing #GeneralAnalytics #RealTimeDataProcessing #BigDataAsAService

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Alexa logo

Alexa

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A cloud-based voice service
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      related Alexa posts

      Arthur Boghossian
      DevOps Engineer at PlayAsYouGo · | 3 upvotes · 142.6K views

      For our Compute services, we decided to use AWS Lambda as it is perfect for quick executions (perfect for a bot), is serverless, and is required by Amazon Lex, which we will use as the framework for our bot. We chose Amazon Lex as it integrates well with other #AWS services and uses the same technology as Alexa. This will give customers the ability to purchase licenses through their Alexa device. We chose Amazon DynamoDB to store customer information as it is a noSQL database, has high performance, and highly available. If we decide to train our own models for license recommendation we will either use Amazon SageMaker or Amazon EC2 with AWS Elastic Load Balancing (ELB) and AWS ASG as they are ideal for model training and inference.

      See more
      Ahrefs logo

      Ahrefs

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      SEO Tools & Resources To Grow Your Search Traffic
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          related Ahrefs posts

          SEMrush logo

          SEMrush

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              related SEMrush posts

              App Annie logo

              App Annie

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              The leader in app store analytics, app rankings, and market intelligence
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                  related App Annie posts

                  Shared insights
                  on
                  SimilarWebSimilarWebApp AnnieApp Annie

                  Hello everyone, hope you're doing well.

                  I currently use SimilarWeb to collect data (e.g. downloads, dau, engagement) of some Brazilian apps, to do market research with them (estimate market share of some industry, for instance)

                  I wonder if App Annie offers any significant upside vs SimilarWeb to reach this goal.

                  Also, in your opinion, how do the cost-benefit ratios of the 2 solutions compare?

                  See more
                  JavaScript logo

                  JavaScript

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                  • 286
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                    Ubiquitousness
                  • 191
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                    Its everywhere
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                    Because I love functions
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                    JavaScript is the New PHP
                  • 10
                    Like it or not, JS is part of the web standard
                  • 9
                    Expansive community
                  • 9
                    Everyone use it
                  • 9
                    Can be used in backend, frontend and DB
                  • 9
                    Easy
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                    Most Popular Language in the World
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                    Can be used both as frontend and backend as well
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                    Can be used on frontend/backend/Mobile/create PRO Ui
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                    1.6K Can be used on frontend/backend
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                    Subskill #4
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                    Hard 彤
                  CONS OF JAVASCRIPT
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                    A constant moving target, too much churn
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                    Horribly inconsistent
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                    Javascript is the New PHP
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                    Shows Zero output in case of ANY error
                  • 7
                    Thinks strange results are better than errors
                  • 6
                    Can be ugly
                  • 3
                    No GitHub
                  • 2
                    Slow

                  related JavaScript posts

                  Zach Holman

                  Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

                  But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

                  But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

                  Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

                  See more
                  Conor Myhrvold
                  Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.9M views

                  How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

                  Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

                  Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

                  https://eng.uber.com/distributed-tracing/

                  (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

                  Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

                  See more
                  Git logo

                  Git

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                    Distributed version control system
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                  • 2
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                  • 2
                    Possible to lose history and commits
                  • 1
                    Rebase supported natively; reflog; access to plumbing
                  • 1
                    Light
                  • 1
                    Team Integration
                  • 1
                    Fast, scalable, distributed revision control system
                  • 1
                    Easy
                  • 1
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                  • 1
                    CLI is great, but the GUI tools are awesome
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                  • 0
                    Phinx
                  CONS OF GIT
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                    Inconsistent command line interface
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                  • 7
                    Worst documentation ever possibly made
                  • 5
                    Awful merge handling
                  • 3
                    Unexistent preventive security flows
                  • 3
                    Rebase hell
                  • 2
                    When --force is disabled, cannot rebase
                  • 2
                    Ironically even die-hard supporters screw up badly
                  • 1
                    Doesn't scale for big data

                  related Git posts

                  Simon Reymann
                  Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M views

                  Our whole DevOps stack consists of the following tools:

                  • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
                  • Respectively Git as revision control system
                  • SourceTree as Git GUI
                  • Visual Studio Code as IDE
                  • CircleCI for continuous integration (automatize development process)
                  • Prettier / TSLint / ESLint as code linter
                  • SonarQube as quality gate
                  • Docker as container management (incl. Docker Compose for multi-container application management)
                  • VirtualBox for operating system simulation tests
                  • Kubernetes as cluster management for docker containers
                  • Heroku for deploying in test environments
                  • nginx as web server (preferably used as facade server in production environment)
                  • SSLMate (using OpenSSL) for certificate management
                  • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
                  • PostgreSQL as preferred database system
                  • Redis as preferred in-memory database/store (great for caching)

                  The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

                  • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
                  • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
                  • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
                  • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
                  • Scalability: All-in-one framework for distributed systems.
                  • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
                  See more
                  Tymoteusz Paul
                  Devops guy at X20X Development LTD · | 23 upvotes · 8.7M views

                  Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

                  It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

                  I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

                  We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

                  If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

                  The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

                  Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

                  See more
                  GitHub logo

                  GitHub

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                  • 43
                    Organizations
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                  • 22
                    Community SDK involvement
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                    Learn from others source code
                  • 16
                    Because: Git
                  • 14
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                    Newsfeed
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                    CI Integration
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                  • 4
                    Uses GIT
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                    Version Control
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                    Security options
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                    Ci
                  • 3
                    IAM
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                  • 2
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                    Leads the copycats
                  • 2
                    All in one development service
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                    Free private repos
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                    Free HTML hostings
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                    Easy source control and everything is backed up
                  • 2
                    IAM integration
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                    Very Easy to Use
                  • 2
                    Good tools support
                  • 2
                    Issues tracker
                  • 2
                    Never dethroned
                  • 2
                    Self Hosted
                  • 1
                    Dasf
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                    Profound
                  CONS OF GITHUB
                  • 54
                    Owned by micrcosoft
                  • 38
                    Expensive for lone developers that want private repos
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                    Relatively slow product/feature release cadence
                  • 10
                    API scoping could be better
                  • 9
                    Only 3 collaborators for private repos
                  • 4
                    Limited featureset for issue management
                  • 3
                    Does not have a graph for showing history like git lens
                  • 2
                    GitHub Packages does not support SNAPSHOT versions
                  • 1
                    No multilingual interface
                  • 1
                    Takes a long time to commit
                  • 1
                    Expensive

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