Gas fees have become a headache for many projects. Every on-chain data update costs money. Especially for high-frequency trading and asset management protocols, maintaining data synchronization alone can incur significant costs.



But recently, some projects have quietly changed their approach. They haven't cut features; instead, they've made calls more flexible and responses faster—while reducing costs at the same time.

The turning point is here: they abandoned the "mindless push" approach and switched to "pull when needed."

Imagine the original oracle model. It's like having a helpful friend who sends you market quotes every minute, whether you need them or not—each message costs you delivery fees. No matter how frequent the updates, you have to accept them all.

Using a data pull mechanism is different. The core logic is: "Don't proactively push; respond passively; appear when needed, stay silent when not."

**In practical terms:**

At the moment a user executes a trade or a contract needs to settle—data is immediately available. Other times? The system remains silent, burning not a single Gas.

High-frequency strategies can pull data at millisecond intervals, while low-frequency scenarios can trigger events as needed. You're no longer bound by continuous data streams but hold the initiative—when to need data, when to call.

**Security remains uncompromised.** Each data pull still requires verification by decentralized nodes, ensuring source traceability and full trustworthiness throughout the process. Cost savings never mean lowering security standards.

The beauty of this approach is that it transforms data costs from fixed expenses into on-demand resource consumption. Already, algorithmic stablecoin projects are using it to optimize minting and redemption processes, and derivatives protocols are employing it for millisecond-level precise calculations.

Their shared realization is—no longer agonizing over "on-chain costs," but learning to "save smartly and use precisely."

When data can truly be as instant and accessible as water and electricity, your protocol design gains cost flexibility. Projects that once saw on-chain data fees as a ceiling are now re-evaluating their architecture.

Think about how much Gas your project spends daily on those "unnecessary" data updates. Share your scenarios in the comments—perhaps "pull on demand" is exactly the breakthrough you're looking for.
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gas_fee_therapyvip
· 3h ago
Finally someone is talking about this. I'm exhausted by gas fees every day. How is this logic so refreshing? Pulling on demand is really awesome. The on-demand pulling system sounds a bit too idealistic, right? Damn, isn't this exactly the problem I've been wanting to complain about? But is security really not compromised? I'm a bit worried. Pulling in seconds sounds great, but could the actual latency become a new problem? My project’s gas fees in a month could buy a car. I need to give it a try.
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PerpetualLongervip
· 3h ago
Another opportunity I didn't take advantage of... This plan sounds impressive, but how many can actually be implemented? I've seen too many "revolutionary plans" end up just talk. But on the other hand, if they can really cut Gas fees like that, wouldn't my full position in derivative protocols take off? I need to reassess my holdings this time; I feel like I missed the golden opportunity again...
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Layer2Observervip
· 3h ago
On-demand pulling indeed solves an old problem, but it depends on the specific implementation. Merely changing the architecture is not enough—the key is whether the node verification process is truly decentralized. Talking with project teams every day about this, most are still being lazy, using "on-demand" as a pretext to continue centralized pushing. That's not a real breakthrough. An interesting discovery should be—at the source code level, how much can the pulling mechanism reduce gas costs, rather than just broadly saying "costs have decreased." Where's the data? Further verification is needed, especially for projects claiming millisecond-level precision. Their actual throughput and latency performance are still unclear.
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