Is Your Cloud Architecture Rigid? Build With Limitless Flexibility.

Note: This is a use case example demonstrating how Rediacc can solve this problem. As a startup, these scenarios represent potential applications rather than completed case studies.

Crisis Scenario: AI training times extended 2-3 times, causing project delays. Engineers experienced significant productivity loss while waiting for resources, threatening the company’s competitive advantage.

The Problem

The company’s software engineers are experiencing performance issues with on-premise servers used for AI model training:

  • During office hours (08:00-17:00), server requests reach 99% capacity
  • Training requiring high processing power causes the hardware to be insufficient

Search for Solution:

  • Server upgrade cost is not considered suitable due to 6-7 hours of daily use
  • Although cloud migration is considered, data transfer cost and synchronization difficulties are obstacles

Crisis Impact

  • AI training times extend 2-3 times, projects are delayed
  • Engineers experience productivity loss while waiting for resources
  • The company faces the risk of gradually losing its competitive advantage

Rediacc Solution

System engineer Yüksel develops a hybrid model with Rediacc:

Hybrid Cloud Scaling

1. Instant Cloud Migration

  • During office hours, on-premise services are cloned to the cloud with all data and configurations
  • 100 TB of data is synchronized in 9 minutes by transferring only the changed parts thanks to Rediacc

2. Dynamic Scaling

  • Servers in the cloud environment are rented as much as needed for AI training
  • Processing power can be increased 10 times according to demand

3. Night Synchronization

  • At the end of the workday, all changes in the cloud are automatically pulled to the on-premise environment
  • Engineers working at night continue their operations with up-to-date data

Result

Cost Advantage:

  • By renting cloud resources hourly, monthly cost was reduced by 60%
  • The need to upgrade on-premise servers was eliminated

Performance Increase:

  • AI training times were reduced from 8 hours to 1.5 hours
  • Engineer productivity increased by 40%

Flexible Working:

  • Data consistency between cloud and on-premise environments was ensured seamlessly
  • Teams on the night shift had instant access to up-to-date data