Everyone who has written on-chain smart contracts understands that feeling—finishing code at 3 a.m., the testnet runs smoothly, only to be awakened by Gas fees when deploying on the mainnet. The moment you see that string of numbers, from full of ideas to feeling completely discouraged, separated only by a wallet confirmation button.

This is the harsh reality of current smart contract development: your technical skills and innovative ideas often get stuck at the cost fluctuation hurdle. Every on-chain interaction, especially those involving external data calls, incurs two types of fees—network transaction costs and data service fees. Both are unpredictable: one fluctuates with network congestion, and the other depends on the pricing set by different service providers.

The good news is that some industry players are actively tackling this problem. Certain data service solutions are exploring a new approach: considering cost certainty at the architectural level. For example, adopting a pull-based model instead of continuous push, which reduces unnecessary on-chain operations from the source. Furthermore, some providers are experimenting with dynamic cost adjustment mechanisms—flexibly adjusting service fees based on real-time Gas prices on Ethereum or other public chains, even offering discounts during network congestion periods.

This is not just marketing hype but a genuine system-level cost design. Imagine: when Ethereum is congested due to a popular NFT minting event and Gas prices spike to 200 Gwei, your data call costs could benefit from a temporary discount factor. The logic is clear—since the network itself is expensive, service providers raising prices is just a secondary harvest; it’s better to share the cost burden with developers, thereby maintaining user stickiness and ecosystem vitality.

The core idea of this approach is to turn the uncertainty of development costs into a predictable and optimizable parameter. For on-chain applications, it means not having to gamble on how high Gas or data costs might go each time; for the ecosystem, rationalized cost pricing can attract more developers to experiment. After all, reducing financial risks associated with innovation directly fosters more innovation.