Not every exchange behaves the same way when network activity spikes. During quiet periods, almost all of them operate similarly, but the differences become apparent precisely when the load increases. The network itself also plays a role; for example, what is considered a stress test for the BTC or ETH networks is standard operating procedure for the $TON network.

This is quite evident in the example of STONfi on the $TON network. The first point is the stability of the exchanges themselves. Even as the number of users and transactions increases, swaps continue to process without delays or significant price deviations.

The second point is price behavior during a transaction. Under high load, slippage becomes more noticeable on many platforms, especially when the transaction amount is large. If the exchange handles the load well, the result remains close to what is expected, even as volumes grow.

The third point is the overall user experience. When you don’t have to double-check every action, there are no doubts before confirmation, and everything proceeds predictably, this is already an indicator that the system can handle the user flow.

The way liquidity is managed plays a distinct role here. Thanks to internal mechanisms, including aggregation via Omniston, the load is distributed more evenly. This helps avoid situations where a single pool simply cannot handle the volume.