Calculate the price of your online store on Magento 2.1

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Quick Overview

Descriptive price of an online store on Magento, including:

  1. Price for standard solutions.

  2. The cost of exclusive development and average price for solution integration.

  3. Periodic costs (monthly expenses) for licenses, hosting and maintenance ( technical support, customization, improvements).

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The price of online store on Magento.

Standard Magento online store price calculator.

Choose the product options to have a descriptive view of how much it takes to create an online store on the most popular platform in the world - Magento (according to data from Alexa Rating, Aheadworks Rating, Google Trends). Why Magento? - learn more.

This calculator is not a Commercial Proposal and it only shows the price of standard integrations, popular requests and regular expenses for an online store.

Magento 2.1 Community Edition - is a totally free CMS. The service package you will see further allows you to understand how much it actually takes to own and develop your own standard online store on Magento.

What should be included in a ready-to-go online store (good online store).

A ready-to-go good online store should include:

  1. Varnish cache (out of the box in Magento 2).
  2. Smart search (Elastic).
  3. Unique design and responsive theme.
  4. Full integration with your CRM and ERP systems.
  5. PIM for product information management (we recommend Akeneo PIM).
  6. Multichannel
  7. Checkout address autocomplete.
  8. Social network connection.
  9. A sepcific service to generate transactional emails and complex recommendations.

For big eCommerce projects you can also add heavy load features.


What can be defined as a Smart online store.

A smart online store include the feature of machine learning:

  1. Identification of user segment and catalogue generation according to the segment preferences.
  2. Record on purchased products.
  3. The store inventory is formed with assortment matrix forecasting the demand - a necessary product is always available during pick demands without any holes in the size range.
  4. There are lookbooks managed manually. Machine learning forecasts the compatibility of products based on existing lookbooks and associated sales history.
  5. Complete eCommerce analytics in Google.Analytics (information by sku, sales, returns).
  6. Forecast of user segment by his behaviour analysis (category view, products, navigation speed).
  7. Reccomendation service with big data like RetailRocket.

Additional Information

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