IBM Elastic Storage Server vs. QxStor

Reliable data storage and access is a big challenge in AI applications and IBM ESS is a service you need to solve the problem. If you are building an AI service and you already use the IBM ecosystem for AI compute requirements, ESS can serve as an excellent software based storage solution. Integration with other platforms such as EMR/Databricks is still a challenge and should be improved.

Incentivized

• Powerful processing power, literally support unlimited scale out storage capabilities
• well integrated and supported with multi flavor environment and file systems.
• Huge capacity with in a very reduced form factor of disks/enclosures

• Integration and configuration need a bit more technical expertise which is I guess expected.
• There can be some improvement in integration with Legacy applications.

Incentivized

IBM ESS is optimized for AI and Big Data usecases while S3 is a general purpose storage solutions. EMR and Databricks have lakehouse/data warehousing solutions for distributed computing but are more optimized for just the big data pipelining solutions and not essentially for AI usecases, especially for inference, when you need to load model artifacts really quickly.

Incentivized

• When the cost and price dynamics comes into account, we have replaced age old traditional arrays with almost negligible cost with ESS
• able to extend the capabilities of global teams to work and share their experiences and knowledge with other teams.