Megaladata Pricing Plans

Desktop Editions

Standalone applications for personal use, installable on local computers running Windows or Linux.
Community
For non-commercial use
  • No limitation on the amount of data
  • No limitation on the use of algorithms
  • Windows, Linux
 Free
Download
Personal
For commercial use
  • No limitation on the amount of data
  • No limitation on the use of algorithms
  • Technical support
  • Windows, Linux
 $990
Buy

Server Editions

On-premises installation on the customer's equipment allows for collaborative work via a web browser. It can also be seamlessly integrated into business processes, with batch processing capabilities available.
Team
For small teams of 5-10 people
  • Up to 6 processor cores
  • Up to 12 GB RAM
  • Job scheduler
  • Technical support
  • Windows
Starts at:
$15 900
Request a presentation
Standard
For small organizations of 5-20 people
  • Up to 12 processor cores
  • Up to 24 GB RAM
  • Job scheduler
  • SOAP requests
  • 1 free environment (Dev/Test/Prod)
  • Technical support
  • Windows, Linux
Starts at:
$45 800
Request a presentation
Enterprise
For large companies and complex projects
  • Unlimited number of users
  • Unlimited core usage
  • Unlimited RAM usage
  • Fault-tolerant cluster
  • Job scheduler
  • LDAP authentication
  • OpenID authentication
  • Kafka support
  • 3 free environments (Dev/Test/Prod)
  • Technical support
  • Windows, Linux
 
On request
Request a presentation

Cloud Edition

Rent a cloud-based Megaladata for hypothesis testing, or implement a production-ready solution within a fault-tolerant cloud infrastructure.
Cloud
Scalable fault-tolerant cloud service
 
On request
Starts at:
$0.99/hour
Request a presentation

Free Server Trial

We offer a comprehensive 3-month trial with no limitations so you can experience the platform's robust capabilities in a server environment.
Request Server Trial

About Megaladata

Megaladata is a low code platform for advanced analytics

A solution for a wide range of business problems that require processing large volumes of data, implementing complex logic, and applying machine learning methods.
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