Alternatives to Obviously AI logo

Alternatives to Obviously AI

Google Analytics, Google Tag Manager, Mixpanel, Mixpanel, and Optimizely are the most popular alternatives and competitors to Obviously AI.
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What is Obviously AI and what are its top alternatives?

Obviously AI is a no-code AI platform that allows users to build, train, and deploy machine learning models without requiring any coding knowledge. It offers features such as autoML, data visualization, and model predictions. However, it has limitations in terms of customization options and advanced model tuning capabilities.

  1. RapidMiner: RapidMiner is a data science platform that offers advanced analytics, machine learning, and data preparation tools. It provides a drag-and-drop interface for building models and has a wide range of data connectors. Pros: Extensive feature set, user-friendly interface. Cons: Steeper learning curve compared to Obviously AI.
  2. DataRobot: DataRobot is an automated machine learning platform that helps businesses build and deploy machine learning models. It offers features such as model deployment, collaboration tools, and model explainability. Pros: Automatic model building, model interpretability. Cons: Higher pricing compared to Obviously AI.
  3. H2O.ai: H2O.ai is an open-source machine learning platform that offers distributed machine learning algorithms and tools for building custom models. It supports deep learning, gradient boosting, and other advanced techniques. Pros: Open-source, scalable. Cons: Requires more technical expertise compared to Obviously AI.
  4. KNIME: KNIME is a data analytics platform that allows users to create visual workflows for data manipulation, analysis, and machine learning. It offers a wide range of extensions and integrations with other tools. Pros: Extensive library of nodes, flexibility in workflow design. Cons: Steeper learning curve for beginners.
  5. Dataiku: Dataiku is a collaborative data science platform that provides tools for data preparation, model building, and deployment. It offers visual recipes for data wrangling and machine learning tasks. Pros: Collaboration features, end-to-end data science workflow. Cons: Pricing may be a barrier for small businesses.
  6. BigML: BigML is a machine learning platform that offers automated machine learning, model evaluation, and model visualization tools. It supports ensemble learning and anomaly detection techniques. Pros: Automation capabilities, user-friendly interface. Cons: Limited customization options compared to Obviously AI.
  7. Microsoft Azure Machine Learning: Azure Machine Learning is a cloud-based machine learning service that offers tools for building, training, and deploying machine learning models. It supports Python and R programming languages. Pros: Integration with other Azure services, scalability. Cons: Requires familiarity with Azure ecosystem.
  8. Google Cloud AutoML: Google Cloud AutoML is a machine learning service that allows users to build custom machine learning models without requiring coding skills. It supports image, text, and tabular data for model training. Pros: User-friendly interface, integration with Google Cloud services. Cons: Pricing based on usage can be complex.
  9. AWS Sagemaker: AWS Sagemaker is a fully managed machine learning service that allows users to build, train, and deploy machine learning models at scale. It supports a wide range of machine learning frameworks and algorithms. Pros: Scalability, integration with AWS services. Cons: Complex pricing structure.
  10. IBM Watson Studio: IBM Watson Studio is a data science platform that provides tools for data exploration, model development, and model deployment. It offers a collaborative environment for data science teams to work together. Pros: Collaboration features, integration with IBM Cloud services. Cons: Limited support for deep learning models compared to Obviously AI.

Top Alternatives to Obviously AI

  • 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. ...

  • Google Tag Manager
    Google Tag Manager

    Tag Manager gives you the ability to add and update your own tags for conversion tracking, site analytics, remarketing, and more. There are nearly endless ways to track user behavior across your sites and apps, and the intuitive design lets you change tags whenever you want. ...

  • Mixpanel
    Mixpanel

    Mixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience. ...

  • Mixpanel
    Mixpanel

    Mixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience. ...

  • Optimizely
    Optimizely

    Optimizely is the market leader in digital experience optimization, helping digital leaders and Fortune 100 companies alike optimize their digital products, commerce, and campaigns with a fully featured experimentation platform. ...

  • Segment
    Segment

    Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch. ...

  • Crazy Egg
    Crazy Egg

    Crazy Egg gives you the competitive advantage to improve your website in a heartbeat without the high costs. ...

  • Quantcast
    Quantcast

    It is a digital marketing company that provides free audience demographics measurement and delivers real-time advertising. ...

Obviously AI alternatives & related posts

Google Analytics logo

Google Analytics

127.1K
49.2K
5.1K
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PROS OF GOOGLE ANALYTICS
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  • 927
    Easy setup
  • 891
    Data visualization
  • 698
    Real-time stats
  • 406
    Comprehensive feature set
  • 182
    Goals tracking
  • 155
    Powerful funnel conversion reporting
  • 139
    Customizable reports
  • 83
    Custom events try
  • 53
    Elastic api
  • 15
    Updated regulary
  • 8
    Interactive Documentation
  • 4
    Google play
  • 3
    Walkman music video playlist
  • 3
    Industry Standard
  • 3
    Advanced ecommerce
  • 2
    Irina
  • 2
    Easy to integrate
  • 2
    Financial Management Challenges -2015h
  • 2
    Medium / Channel data split
  • 2
    Lifesaver
CONS OF GOOGLE ANALYTICS
  • 11
    Confusing UX/UI
  • 8
    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

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Tassanai Singprom

This is my stack in Application & Data

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Max Musing
Founder & CEO at BaseDash · | 8 upvotes · 361.2K views

Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

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Google Tag Manager logo

Google Tag Manager

63.6K
7.2K
0
Quickly and easily update tags and code snippets on your website or mobile app
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      Iva Obrovac
      Product Marketing Manager at Martian & Machine · | 8 upvotes · 82.8K views

      Hi,

      This is a question for best practice regarding Segment and Google Tag Manager. I would love to use Segment and GTM together when we need to implement a lot of additional tools, such as Amplitude, Appsfyler, or any other engagement tool since we can send event data without additional SDK implementation, etc.

      So, my question is, if you use Segment and Google Tag Manager, how did you define what you will push through Segment and what will you push through Google Tag Manager? For example, when implementing a Facebook Pixel or any other 3rd party marketing tag?

      From my point of view, implementing marketing pixels should stay in GTM because of the tag/trigger control.

      If you are using Segment and GTM together, I would love to learn more about your best practice.

      Thanks!

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      Mixpanel logo

      Mixpanel

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      • 78
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      • 58
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      • 22
        A wide range of tools
      • 15
        Powerful Graph Search
      • 11
        Responsive Customer Support
      • 2
        Nice reporting
      CONS OF MIXPANEL
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        Messaging (notification, email) features are weak
      • 2
        Paid plans can get expensive
      • 1
        Limited dashboard capabilities

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      Max Musing
      Founder & CEO at BaseDash · | 8 upvotes · 361.2K views

      Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

      Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

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      Yasmine de Aranda
      Chief Growth Officer at Huddol · | 7 upvotes · 380.6K views

      Hi there, we are a seed-stage startup in the personal development space. I am looking at building the marketing stack tool to have an accurate view of the user experience from acquisition through to adoption and retention for our upcoming React Native Mobile app. We qualify for the startup program of Segment and Mixpanel, which seems like a good option to get rolling and scale for free to learn how our current 60K free members will interact in the new subscription-based platform. I was considering AppsFlyer for attribution, and I am now looking at an affordable yet scalable Mobile Marketing tool vs. building in-house. Braze looks great, so does Leanplum, but the price points are 30K to start, which we can't do. I looked at OneSignal, but it doesn't have user flow visualization. I am now looking into Urban Airship and Iterable. Any advice would be much appreciated!

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      Mixpanel logo

      Mixpanel

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      • 108
        Easy integration
      • 78
        Great funnel funcionality
      • 58
        Free
      • 22
        A wide range of tools
      • 15
        Powerful Graph Search
      • 11
        Responsive Customer Support
      • 2
        Nice reporting
      CONS OF MIXPANEL
      • 2
        Messaging (notification, email) features are weak
      • 2
        Paid plans can get expensive
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      Max Musing
      Founder & CEO at BaseDash · | 8 upvotes · 361.2K views

      Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

      Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

      See more
      Yasmine de Aranda
      Chief Growth Officer at Huddol · | 7 upvotes · 380.6K views

      Hi there, we are a seed-stage startup in the personal development space. I am looking at building the marketing stack tool to have an accurate view of the user experience from acquisition through to adoption and retention for our upcoming React Native Mobile app. We qualify for the startup program of Segment and Mixpanel, which seems like a good option to get rolling and scale for free to learn how our current 60K free members will interact in the new subscription-based platform. I was considering AppsFlyer for attribution, and I am now looking at an affordable yet scalable Mobile Marketing tool vs. building in-house. Braze looks great, so does Leanplum, but the price points are 30K to start, which we can't do. I looked at OneSignal, but it doesn't have user flow visualization. I am now looking into Urban Airship and Iterable. Any advice would be much appreciated!

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      Optimizely logo

      Optimizely

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        SegmentSegmentOptimizelyOptimizely

        Hey all, I'm managing the implementation of a customer data platform and headless CMS for a digital consumer content publisher. We're weighing up the pros and cons of implementing an OTB activation platform like Optimizely Recommendations or Dynamic Yield vs developing a bespoke solution for personalising content recommendations. Use Case is CDP will house customers and personas, and headless CMS will contain the individual content assets. The intermediary solution will activate data between the two for personalisation of news content feeds. I saw GCP has some potentially applicable personalisation solutions such as recommendations AI, which seem to be targeted at retail, but would probably be relevant to this use case for all intents and purposes. The CDP is Segment and the CMS is Contentstack. Has anyone implemented an activation platform or personalisation solution under similar circumstances? Any advice or direction would be appreciated! Thank you

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        Segment logo

        Segment

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          Simple
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          Multiple integrations
        • 19
          Cleanest API
        • 10
          Easy
        • 9
          Free
        • 8
          Mixpanel Integration
        • 7
          Segment SQL
        • 6
          Flexible
        • 4
          Google Analytics Integration
        • 2
          Salesforce Integration
        • 2
          SQL Access
        • 2
          Clean Integration with Application
        • 1
          Own all your tracking data
        • 1
          Quick setup
        • 1
          Clearbit integration
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          Beautiful UI
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          Integrates with Apptimize
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          Escort
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        CONS OF SEGMENT
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          Not clear which events/options are integration-specific
        • 1
          Limitations with integration-specific configurations
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          Client-side events are separated from server-side

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        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.

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        Robert Zuber

        Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

        We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

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