Build a dynamic pricing system.

This question is essentially asking you to design a system with pricing that adjusts or 'dynamics' based on certain conditions or metrics. Dynamic pricing is a strategy where businesses set flexible prices for products or services based on current market demands, and it's frequently used in various industries like airlines, rideshare services like Uber, and e-commerce platforms where demand and supply can fluctuate significantly in short periods.

When explaining your design, remember to consider the following key points:

1. Inputs and Outputs: Identify what are the data and metrics, which will drive the dynamic pricing system, and what will be the resultant output.
2. Algorithm: Describe the algorithm or the business logic you'll use to dynamically adjust prices based on the input parameters.
3. Scalability: Consider how this system can scale under high load, as pricing requests could come in large volumes during peak hours.
4. Reliability and consistency: Ensure that each pricing request is processed accurately and the result is reliably consistent.
5. Real-time performance: Given that dynamic pricing often response to rapid changes in supply and demand, your system needs to respond and adjust prices in real time.

A dynamic pricing system starts with data collection. This could be demand data, customer behavior data, competitive landscape data, time, location etc. All these data points are vital to give a comprehensive view of market conditions.

Based on these input parameters, we can design an algorithm that generates a dynamic price. For example, we can implement a machine learning model that predicts demand based on historical data and make price adjustments based on forecasted data.

For scalability, I would propose building a distributed system where each server instance can handle a specific number of pricing requests. The system can add or remove servers dynamically based on the volume of requests. This way, the system can scale horizontally and maintain a high level of performance even during peak demand.

The data would be stored in a highly consistent yet scalable database, ensuring that we always have the most accurate and consistent data to base our pricing on.

Finally, to ensure real-time performance, we can use in-memory databases like Redis for storing data, which allow for very fast read and write speeds, enabling our system to calculate and adjust prices in real time.

Dynamic pricing system requires careful consideration of several factors, including market demand, competitor pricing, and trends. Initial data can be sourced from the company's own product sales, and supplemented with external data like competitors' prices and general market trends.

The algorithm for this system would be based on these factors. For instance, if demand is high and competitor prices are rising, we could optimise profit by increasing our prices. Conversely, if demand is low and competitors are decreasing prices, we might need to do the same to stay competitive. The algorithm could also consider time, implementing higher prices during peak times and lower prices during off-peak.

For scaling, a microservices architecture can be leveraged. Each service can be independently scaled based on the demand it experiences. Additionally, we can use load balancers to distribute traffic evenly across all available servers.

We might use a strong consistency model to ensure that all instances of the data across the system are same at any given instant, ensuring that we give a consistent price to all customers, no matter which server handles their request.

The system should be inherently fast and capable of real-time performance. To handle this, we can use technologies like Apache Kafka for processing real-time data streams and storing them in a NoSQL database for quick access and modification.