Pull-based oracles (like Chainlink's Low-Level Data Feeds) excel at cost-efficiency and on-demand freshness because they only query and pay for data when a user transaction explicitly requests it. This model eliminates the gas overhead of constant updates, making it ideal for applications with sporadic, user-initiated data needs. For example, a prediction market settling a weekly event can use a single on-chain call to fetch the final price, avoiding thousands of dollars in unnecessary update fees.
LABS
Comparisons
Pull-Based Oracles vs Push Feeds
A technical analysis comparing on-demand (pull) and continuous (push) oracle models. We examine latency, cost, security guarantees, and ideal use cases for protocols like Chainlink, Pyth, and API3.
Chainscore © 2026
introduction
THE ANALYSIS
Introduction: The Core Architectural Divide in Oracle Design
The fundamental choice between pull-based oracles and push feeds dictates your application's performance, cost, and security model.
Push feed oracles (like Chainlink Data Feeds or Pyth Network) take a different approach by continuously broadcasting data to the blockchain via a decentralized network of nodes. This results in sub-second latency and high reliability for critical, high-frequency data—such as spot prices for perpetual swaps—but requires a persistent gas subsidy. The trade-off is a higher, predictable operational cost for superior data availability and speed, which is non-negotiable for DeFi lending protocols that need real-time collateral valuations to prevent liquidations.
The key trade-off: If your priority is minimizing operational cost for non-continuous data needs (e.g., insurance, gaming outcomes), choose a pull-based model. If you prioritize ultra-low latency and guaranteed data availability for always-on financial logic (e.g., DEX spot prices, money markets), a push feed is mandatory. Your architecture decision fundamentally locks in your application's cost structure and performance ceiling.
tldr-summary
Push vs Pull: Architectural Trade-offs
TL;DR: Key Differentiators at a Glance
Push feeds (e.g., Chainlink Data Feeds) proactively deliver data, while pull oracles (e.g., Pyth Network, API3) require on-demand requests. The core trade-off is between cost predictability and data freshness.
01
Push Feed: Predictable Cost & Latency
Guaranteed On-Chain Updates: Data is continuously pushed to a smart contract, ensuring availability with sub-10 second latency (e.g., Chainlink ETH/USD). This matters for perpetuals, lending protocols, and any application requiring constant, low-latency price data without manual triggers.
<10 sec
Typical Update Latency
02
Push Feed: Simpler Integration
Minimal On-Chain Logic: Contracts read from a single, updated data point (e.g., AggregatorV3Interface). This reduces gas costs for users and simplifies development, making it ideal for high-frequency DeFi primitives like AMMs, liquidations, and options pricing.
03
Pull Oracle: Cost Efficiency
Pay-Per-Use Model: Data is fetched on-demand, so you only pay for updates you consume. This eliminates gas waste from unnecessary pushes. This matters for event-driven or low-frequency applications like insurance payouts, prediction market resolutions, or NFT floor price checks.
>70%
Potential Gas Savings
04
Pull Oracle: Data Freshness & Customization
On-Demand Freshness: Each request fetches the latest data directly from the source (e.g., Pyth's pull oracle). Supports custom data types beyond price feeds (sports, weather, IoT). This matters for settlement processes, cross-chain bridges, and bespoke data applications where staleness is unacceptable.
05
Push Feed: Higher Baseline Cost
Continuous On-Chain Gas Overhead: The network pays to update data feeds constantly, regardless of usage. This can be inefficient for niche assets or sidechains. This matters for budget-conscious projects or chains with limited block space where every byte counts.
06
Pull Oracle: Higher Per-Call Complexity
Requires Off-Chain Trigger: An external entity (keeper, user, bot) must initiate and pay for the data pull, adding a layer of operational complexity and potential failure points. This matters for fully decentralized applications that cannot rely on a centralized relayer to trigger updates.
PULL-BASED ORACLES VS PUSH FEEDS
Head-to-Head Feature Comparison
Direct comparison of key architectural and operational metrics for on-chain data delivery.
| Metric | Pull-Based Oracles (e.g., Chainlink) | Push Feeds (e.g., Pyth Network) |
|---|---|---|
Data Update Latency | User-triggered, variable (seconds to minutes) | Continuous, ~400ms per price update |
On-Chain Gas Cost for Update | Paid by end-user per request | Subsidized by protocol, ~$0.001 per update |
Primary Architecture | Decentralized Data Fetch (DONs) | Publisher Network + Pull Oracle |
Real-World TPS for Price Feeds | ~1-2 updates/sec per feed | ~100+ updates/sec aggregate |
Data Provider Slashing | ||
Supports Custom Data Feeds | ||
Time to First Mainnet Data | 2019 | 2021 |
PULL-BASED ORACLES VS PUSH FEEDS
Performance & Cost Benchmarks
Direct comparison of on-chain performance, cost, and operational characteristics for oracle data delivery models.
| Metric | Pull-Based Oracles (e.g., Chainlink) | Push Feeds (e.g., Pyth) |
|---|---|---|
Data Update Latency | On-demand (user-triggered) | Continuous (protocol-triggered) |
Avg. User Cost per Update | $0.50 - $5.00+ | $0.001 - $0.01 |
Primary Cost Bearer | End User (Gas) | Protocol/Publisher (Subsidy) |
Data Freshness Guarantee | ||
Real-World TPS (Oracle) | ~1,000 | ~1,000+ |
Time to On-Chain Availability | ~1 block after request | < 400ms (pre-committed) |
Main Architectural Model | Decentralized Data Fetch | Decentralized Publish/Subscribe |
pros-cons-a
ARCHITECTURE COMPARISON
Pull-Based Oracles vs Push Feeds
Key strengths, trade-offs, and ideal use cases for on-demand vs. continuous data delivery.
01
Pull-Based Oracle (e.g., Chainlink VRF, API3 dAPIs)
On-Demand Cost Efficiency: Contracts pay only for data they explicitly request. This is critical for low-frequency, event-driven applications like NFT minting (VRF) or insurance claim verification, where paying for a constant feed is wasteful.
02
Pull-Based Oracle (e.g., Chainlink VRF, API3 dAPIs)
Guaranteed Freshness on Request: Data is fetched at the exact moment of execution, ensuring deterministic finality. This is non-negotiable for DeFi settlement (e.g., closing a perp position) or gaming logic where the state must be current at the transaction's precise moment.
03
Push-Based Feed (e.g., Chainlink Data Feeds, Pyth Network)
Ultra-Low Latency for High-Frequency Apps: Data is continuously updated on-chain (e.g., every 400ms for Pyth, 1-5 min for Chainlink). This is essential for perpetual DEXs, money markets, and liquid staking where sub-second price updates are required to prevent arbitrage and protect liquidity.
04
Push-Based Feed (e.g., Chainlink Data Feeds, Pyth Network)
Predictable Cost & Simpler Integration: Protocols pay a fixed subscription or rely on subsidized feeds, simplifying budgeting. Contracts read from a pre-populated on-chain storage slot, reducing gas overhead per query. Ideal for high-TVL DeFi primitives like Aave or Compound that need constant price access.
05
Choose Pull-Based For:
- Verifiable Randomness (VRF) for NFTs & Gaming
- One-Time Verification (KYC, proof-of-reserves)
- Low-Volume, High-Stakes Events (parametric insurance payouts)
- Budget-Constrained Prototypes where cost predictability is key.
06
Choose Push-Based For:
- High-Frequency Trading & Lending (Perpetuals on dYdX, MarginFi)
- Liquid Staking Derivatives (stETH, jitoSOL) needing real-time validator stats
- Stablecoin & Synthetics Protocols (DAI, SNX) requiring constant peg monitoring
- Any dApp where gas cost of a pull is prohibitive vs. pre-paid updates.
pros-cons-b
ARCHITECTURE COMPARISON
Pull-Based Oracles vs Push Feeds
Key architectural trade-offs and performance characteristics for oracle design. Use this matrix to align your protocol's needs with the right data delivery model.
01
Pull-Based Oracle (e.g., Chainlink)
On-Demand Data Fetch: Contracts explicitly request data via requestRandomness or requestPriceData. This matters for event-driven applications where data is needed sporadically, like insurance payouts or NFT minting.
Proven Security Model: Relies on decentralized oracle networks (DONs) with off-chain computation and on-chain aggregation. This provides tamper-proof data for high-value DeFi protocols like Aave and Synthetix.
Trade-off: Introduces higher latency (2-3 block confirmations) and gas costs per request, making it less ideal for high-frequency updates.
2-3 Blocks
Typical Latency
$50B+
Secured TVL
02
Push Feed Oracle (e.g., Pyth, Chainlink Data Streams)
Continuous Data Streaming: Oracles push pre-verified data to an on-chain cache (e.g., Pyth's Price Feed contract) at high frequency. This matters for low-latency trading on perpetual DEXs like Hyperliquid or Drift Protocol.
Sub-Second Updates: Enables < 1-second price updates with minimal on-chain computation. This is critical for derivatives and money markets requiring real-time liquidation checks.
Trade-off: Requires continuous off-chain infrastructure and gas expenditure by the oracle provider, potentially leading to higher operational costs and centralization pressures for the data source.
< 1 sec
Update Speed
300+
Feeds
03
Choose Pull for Event-Driven Logic
Best for: Protocols where data requests are infrequent and unpredictable.
- Insurance: Requesting flight status for parametric insurance (e.g., Etherisc).
- Gaming & NFTs: Requesting verifiable randomness for loot boxes or attributes.
- Sporadic Settlements: Executing a derivatives contract only at expiry.
Key Tools: Chainlink VRF, Any API, and custom external adapters.
04
Choose Push for High-Frequency Trading
Best for: Applications requiring real-time, continuous data.
- Perpetual Futures DEXs: Needing sub-second price feeds for accurate mark prices and liquidations.
- Money Markets: Requiring up-to-the-second collateral valuation (e.g., Solend on Solana uses Pyth).
- Algorithmic Stablecoins: Needing constant FX or commodity price feeds for rebalancing.
Key Tools: Pyth Network, Chainlink Data Streams, API3 dAPIs (with push capabilities).
05
Cost Structure: Upfront vs. Recurring
Pull Model (User-Pays): Gas + oracle service fee paid per request. Predictable for low volume, but costs scale linearly with usage. Example: Chainlink VRF costs ~0.0025 LINK per request.
Push Model (Provider-Pays): Oracle provider covers the continuous gas cost to update the on-chain feed. Cost is often socialized or subsidized, leading to zero direct cost for dApp users reading the data. This favors high-throughput applications.
06
Security & Decentralization Trade-off
Pull-Based (Decentralized Verification): Employs multiple independent nodes with on-chain aggregation (e.g., Chainlink's OCR). Strong censorship resistance but higher latency.
Push-Based (Centralized Speed): Often relies on fewer, highly performant nodes or first-party data providers (e.g., Pyth's publishers) to achieve speed. This can create a trust assumption in the data publisher's integrity, though cryptographic proofs (e.g., Wormhole attestations) are used.
Hybrid Approach: Emerging solutions like Chainlink Data Streams aim to combine push speed with decentralized node networks.
CHOOSE YOUR PRIORITY
Decision Framework: When to Use Which Oracle Model
Push Feeds for DeFi
Verdict: The Standard Choice. Strengths: Continuous, low-latency data delivery is critical for liquidations, stablecoin pegs, and perpetual futures. Protocols like Chainlink Data Feeds provide high-frequency updates (e.g., price updates every block on many chains) with decentralized node operators securing billions in TVL. This model is battle-tested for Aave, Compound, and Synthetix. The push model ensures the on-chain state is always current without user action.
Pull-Based Oracles for DeFi
Verdict: Niche for Cost-Sensitive or Custom Data. Strengths: Superior for protocols with infrequent price checks or highly specialized data needs. A Pyth Network pull update, where the relayer pays the fee, can be more economical for low-volume derivatives or insurance products. It's also the model for custom data aggregation where a protocol like UMA's Optimistic Oracle pulls a verified data point on-demand for a specific event or dispute.
verdict
THE ANALYSIS
Final Verdict and Strategic Recommendation
Choosing between pull-based oracles and push feeds is a foundational architectural decision that dictates your protocol's cost profile, latency tolerance, and operational complexity.
Pull-Based Oracles (e.g., Chainlink's LatestRoundData, Pyth's PriceFeed) excel at cost efficiency for low-frequency, user-initiated updates. The on-demand model shifts gas costs to the end-user, making it ideal for applications like NFT marketplaces or governance votes where price checks are sporadic. For example, a protocol with 10,000 daily transactions could save over 90% on oracle gas fees compared to a continuous push model, as validated by gas usage analysis on networks like Arbitrum and Polygon.
Push Feeds (e.g., Chainlink Data Streams, Pythnet) take a different approach by pre-writing data on-chain at high frequency (e.g., 100-400ms). This results in sub-second finality for the data consumer but introduces a constant, protocol-borne cost for block space and relayers. The trade-off is higher baseline operational expense for guaranteed low-latency access, a non-negotiable requirement for perpetual DEXs like dYdX or GMX where liquidation engines must react in real-time.
The key trade-off is between cost predictability and latency. If your priority is minimizing protocol-run gas overhead and your logic tolerates a 1-2 block delay for fresh data, choose a pull-based model. If you prioritize sub-second data finality and can absorb the fixed cost of continuous updates—critical for high-frequency trading, options pricing, or real-time gaming—choose a push feed system. For many protocols, a hybrid approach using push feeds for core liquidity pairs and pull oracles for ancillary assets is the most strategic deployment.
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