Push vs Pull Oracles: 2026

Proactive, Low-Latency Delivery: Data is pushed to contracts automatically upon update. This enables sub-second finality for price feeds, critical for high-frequency DeFi (e.g., perpetuals on dYdX, GMX).

• Best for: Real-time applications requiring instant state updates.

Higher On-Chain Cost & Complexity: Every data push incurs a gas fee, paid by the oracle or protocol. This can be inefficient for infrequently accessed data. Managing many feeds requires robust subscription models and relayers.

Cost-Efficient & On-Demand: Contracts request data only when needed, paying gas per request. Ideal for event-driven logic (e.g., insurance payouts, NFT rarity checks) or data used less than once per block.

• Best for: Optimizing gas spend for non-real-time data.

Higher Latency & Requester Complexity: The calling contract must initiate and handle the request-response cycle, adding 1-3 block delay. This makes it unsuitable for liquidations or swaps. Developers must manage callback logic and potential request timeouts.

Proactive data delivery: Data is broadcast to subscribers as soon as it's updated (e.g., Chainlink Data Streams). This ensures sub-second latency for critical on-chain actions like liquidations or options settlements. This matters for high-frequency DeFi and perpetual futures protocols where stale data means immediate losses.

Continuous gas expenditure: The oracle pays gas to push data, regardless of whether it's used, leading to higher operational costs that are passed to consumers. This is inefficient for data with low query frequency (e.g., weekly settlement prices). This matters for budget-conscious protocols or niche data feeds with few subscribers.

Pay-per-query model: Contracts request data only when needed (e.g., traditional Chainlink Price Feeds). This minimizes gas costs for both oracle and dApp, making it ideal for low-frequency events like end-of-epoch rewards or insurance claim verification. This matters for optimizing operational budgets and infrequently accessed data.

Deterministic data freshness: The requesting contract defines the maximum allowable data age (e.g., maxDelay). This provides granular control over data quality and protects against stale data in volatile markets. This matters for protocols with specific SLA requirements and risk-engineering teams building robust systems.

Introduces request latency: The full cycle (request → off-chain fetch → response) adds 1-3 block confirmations, making it unsuitable for sub-second needs. It also requires dApps to manage the request lifecycle. This matters for real-time trading apps and teams wanting to minimize smart contract logic.

Guaranteed, Low-Latency Updates: Data is pushed on-chain automatically at predefined intervals (e.g., Chainlink's Heartbeat). This ensures sub-5-second finality for critical data like price feeds, which is non-negotiable for high-frequency DeFi (DEXs, lending).

Higher Baseline Gas Costs: Continuous on-chain updates incur fees regardless of usage. For less active data feeds or niche assets, this creates a significant, recurring cost burden for the data provider or the protocol treasury.

User-Pays Gas Model: Data is fetched on-demand by the end-user's transaction (e.g., Tellor, API3 dAPIs). This enables gas-efficient access to thousands of data feeds, as costs are only incurred when needed. Ideal for insurance, gaming, or any low-frequency data call.

Higher Per-Call Latency & Complexity: The requesting contract must handle the asynchronous data retrieval, adding 1-2 block confirmations of delay. This introduces front-running risks and more complex smart contract logic compared to a simple push oracle read.