Balancing data consistency and system performance requires understanding the specific needs of the application. First, assess the nature of the system. If the application demands real-time accuracy, such as in financial transactions or healthcare systems, prioritize data consistency to avoid errors or data corruption. In contrast, if performance and speed are critical, like in content delivery networks or social media feeds, system performance might take precedence, with eventual consistency being acceptable. Ultimately, the decision depends on the use case, user expectations, and risk tolerance, and may involve finding a middle ground where both consistency and performance are sufficiently balanced for the business needs.

It all depends on the data in consideration and boils down to answering this simple question. Is it okay for system to have inconsistent data for a while? If Yes, we should go with eventual consistency and prioritise system performance.

Identify and prioritize the consistency of crucial data that is essential for your operations, while allowing less critical data to be eventually consistent. Use performance metrics and monitoring tools to track system load and identify when to scale resources, ensuring smooth and responsive operations. Employ smart caching mechanisms to reduce database load, thereby improving performance without significantly sacrificing data accuracy. Use databases that offer tunable consistency levels, allowing you to adjust the balance between consistency and performance based on specific use cases. Design and optimize database queries to be efficient, reducing latency and resource consumption while maintaining necessary consistency levels.

IMO, Can the system tolerate eventual consistency or slight data lags? → consider performance Is every transaction or operation required to be immediately accurate? → consider consistency

Understand your non-functional requirements. Do we meet them? Measure (invest in knowing observability tools) and see the shape of system's load, be able to monitor system's the performance. If you have the luxury of being able to scale the system - know your options, have scaling plan and a relevant working procedure. Know your craft when it comes to db queries analysis and optimization - or hire an excellent DB engineer. Understand your data and db usage patterns. See which usecases require transactioanl processing and full consistency, and which type of data is not crucial, or may be delivered out of order, partially or with higher latency.