Pyth excels at delivering ultra-low-latency, high-frequency price updates for major assets because it leverages a network of over 90 first-party data providers (like CBOE and Binance) publishing directly on-chain. This native on-chain aggregation minimizes the data-to-execution path, resulting in sub-second updates. For protocols like Solana's MarginFi or Jupiter, this speed is critical for capital-efficient liquidations where milliseconds and gas costs on high-throughput chains matter.
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Comparisons
Pyth vs RedStone: Liquidation Speed
A technical comparison of Pyth's push-based and RedStone's pull-based oracle models, focusing on latency, cost, and architectural trade-offs critical for high-frequency DeFi applications like liquidations.
Chainscore © 2026
introduction
THE ANALYSIS
Introduction: The Latency Imperative for DeFi Liquidations
A direct comparison of Pyth and RedStone's architectural approaches to delivering the low-latency price feeds critical for profitable and safe DeFi liquidations.
RedStone takes a different approach by utilizing a pull-based, data-availability-centric model. Prices are stored off-chain in data availability layers (like Arweave) and delivered on-demand via a signed data package, which a relayer or the dApp itself pushes on-chain only when needed. This results in a significant trade-off: dramatically lower operational costs and multi-chain simplicity, but introduces a variable latency overhead for the final on-chain write, which can be a critical factor in fast-moving markets.
The key trade-off: If your priority is deterministic, minimal-latency updates for high-value, high-frequency assets on a single, performant chain (e.g., Solana, Sui, Aptos), choose Pyth. If you prioritize extreme cost-efficiency, uniform data across dozens of EVM and non-EVM chains, and can tolerate a slightly higher latency floor, choose RedStone.
tldr-summary
Pyth vs RedStone: Liquidation Speed
TL;DR: Core Differentiators
Key architectural strengths and trade-offs for high-frequency DeFi operations at a glance.
01
Pyth: Sub-Second Finality
Pull-based, on-demand updates: Pythnet validators push price updates to a dedicated Solana-based Pythnet, which are then pulled by client protocols. This enables ~400ms update speeds for major assets. This matters for perps and money markets on Solana, Aptos, and Sui where sub-second liquidations are critical.
~400ms
Update Speed
02
Pyth: High-Value Asset Focus
Curated, high-liquidity feeds: Pyth's permissioned publisher network (Jump Trading, Two Sigma) provides deep liquidity data for ~500 assets, primarily large-cap crypto, equities, and forex. This matters for protocols like MarginFi and Drift that require maximum confidence in price accuracy for large, volatile positions.
~500
Primary Assets
03
RedStone: Modular & Gas-Optimized
Push-based, data availability layer: RedStone stores signed price data off-chain (Arweave) and delivers it via a signed data package in the user's transaction, minimizing on-chain storage and reducing gas costs by up to 90%. This matters for high-frequency, low-margin strategies on EVM L2s (Arbitrum, Base) where gas is a primary cost.
~90%
Gas Reduction
04
RedStone: High Throughput & Breadth
Massive data throughput with 1000+ feeds: Aggregates data from 50+ sources (CEXs, DEXs) for a vast universe of long-tail crypto assets. Supports 1-second updates for all feeds. This matters for exotic perp markets, prediction platforms, and RWA protocols on Polygon or Avalanche that need diverse, frequently updated data.
1000+
Total Feeds
1s
Update Cadence
LIQUIDATION SPEED COMPARISON
Head-to-Head Feature Matrix: Pyth vs RedStone
Direct comparison of oracle performance metrics critical for high-frequency DeFi and liquidation engines.
| Metric | Pyth | RedStone |
|---|---|---|
Update Latency (On-Chain) | 400-500 ms | < 100 ms |
Price Feed Update Frequency | ~400 ms | ~100 ms |
Data Providers per Feed | 80+ | 50+ |
On-Demand Pull Oracle | ||
Gas Cost per Update (Solana) | ~0.00001 SOL | ~0.000005 SOL |
Supported Blockchains | 50+ | 40+ |
PYTH VS REDSTONE: LIQUIDATION SPEED
Latency & Performance Benchmarks
Direct comparison of key metrics for high-frequency DeFi applications.
| Metric | Pyth | RedStone |
|---|---|---|
Update Latency (Publish to On-Chain) | 400-500 ms | 1-2 seconds |
Price Update Frequency | 400 ms | 10 seconds (Standard), 1 second (Turbo) |
On-Chain Data Delivery | Push Model (Pythnet) | Pull Model (Data Feeds) |
Gas Cost per Update (Solana) | ~0.0001 SOL | ~0.00005 SOL |
Supported Blockchains | 50+ | 60+ |
Data Providers (Sources) | 90+ | 50+ |
pros-cons-a
ORACLE INFRASTRUCTURE SHOWDOWN
Pyth Network vs RedStone: Liquidation Speed
For high-frequency DeFi protocols, the speed of price updates directly impacts liquidation efficiency and capital safety. Here's how the leading oracles compare.
01
Pyth's Edge: Sub-Second On-Chain Latency
Specific advantage: Pyth's Pull Oracle model with Pythnet delivers updates in ~400-800ms. This matters for high-frequency perps and money markets where stale prices cause missed liquidations. Protocols like MarginFi and Drift leverage this for real-time risk management.
< 1 sec
Avg. On-Chain Latency
02
RedStone's Edge: Modular Data Feeds
Specific advantage: RedStone's Streamr-based data layer pushes prices off-chain with ~100ms latency, allowing protocols to pull only needed assets on-demand. This matters for gas-sensitive L2s and custom baskets where you pay only for the data you consume, as seen with Sommelier Finance and Morpho Blue integrations.
~100ms
Off-Chain Data Latency
03
Pyth's Trade-off: Higher Baseline Cost
Specific disadvantage: Pyth's ultra-fast updates require continuous on-chain price publishing, leading to higher baseline gas costs for the network. This matters for budget-conscious protocols or those with less volatile assets where sub-second updates aren't critical for all pairs.
04
RedStone's Trade-off: Pull-Induced Latency Spike
Specific disadvantage: While off-chain data is fast, the final on-chain update depends on the protocol's pull transaction, introducing variable latency (1s to 1 block). This matters for peak congestion periods where delayed pulls can create a dangerous gap between off-chain and on-chain state.
pros-cons-b
PROS AND CONS
Pyth vs RedStone: Liquidation Speed
Key architectural trade-offs that directly impact liquidation engine performance and safety.
01
Pyth: Ultra-Low Latency
Pull-based, on-demand updates with sub-second finality on Pythnet. This enables < 400ms price updates on Solana and other supported chains. For liquidations, this minimizes the risk of stale data causing missed opportunities or bad debt.
< 400ms
Update Latency
02
Pyth: High-Cost Resilience
Publisher staking and slashing creates strong crypto-economic security. Data providers post substantial bond, which can be slashed for malfeasance. This is critical for high-value liquidation systems where oracle manipulation could lead to catastrophic losses.
03
RedStone: Extreme Cost Efficiency
Gas-optimized data placement stores price data in a data availability layer (like Arweave or Celestia) and pushes only a cryptographic attestation on-chain. This reduces gas costs for price updates by >90% vs. traditional push oracles, enabling high-frequency updates even on expensive L1s.
>90%
Gas Savings
04
RedStone: Flexible Update Triggers
Pull-or-Push model allows protocols to trigger price updates on-demand (for liquidations) or subscribe to automatic pushes. This flexibility lets liquidation bots control timing and cost, optimizing for market volatility without paying for unnecessary on-chain writes.
CHOOSE YOUR PRIORITY
Decision Framework: When to Use Which
Pyth for Speed
Verdict: The benchmark for ultra-low-latency, high-frequency applications. Strengths: Pyth's pull-based model delivers price updates directly on-chain in <400ms. This deterministic, sub-second latency is critical for perpetual DEXs (e.g., Hyperliquid) and options protocols where liquidation engines must react to micro-fluctuations. Its Solana-native architecture and dedicated Pythnet provide the fastest path from data source to on-chain state. Trade-off: This speed requires higher gas costs for frequent updates and relies on a curated, permissioned set of first-party data providers.
RedStone for Speed
Verdict: Highly competitive for cost-effective, frequent updates on EVM chains. Strengths: RedStone's data availability layer (Arweave) with on-demand push via relayers enables fast, cheap updates. Protocols can trigger price pulls at the exact moment needed (e.g., at transaction execution), avoiding constant gas expenditure. This is optimal for EVM-based money markets (like Aave) and leveraged yield strategies that need fresh prices at liquidation checks without maintaining a live feed. Trade-off: The final latency includes the relayer's response time and block inclusion, making it slightly less deterministic than Pyth's pure pull model for sub-second needs.
verdict
THE ANALYSIS
Final Verdict and Strategic Recommendation
Choosing between Pyth and RedStone for liquidation speed hinges on your protocol's tolerance for latency versus cost and decentralization.
Pyth excels at delivering ultra-low-latency, high-frequency price updates directly on-chain, a critical advantage for liquidation engines. Its push-based oracle model, where publishers push data to the Pythnet before it's relayed to supported chains like Solana and Sui, achieves sub-second update speeds. For example, on Solana, Pyth's Pythnet architecture enables updates every 400ms, providing a significant edge for protocols like Drift and Marginfi that require near-instantaneous price validation to manage risk in volatile markets.
RedStone takes a different approach by prioritizing cost-efficiency and multi-chain flexibility through its pull-based, data-availability-centric model. Prices are stored in a decentralized data layer (like Arweave) and only pulled on-demand by dApps via a signed data package, minimizing gas costs. This results in a trade-off: while updates can be triggered as frequently as needed, the final speed is gated by the underlying blockchain's block time and the dApp's own invocation logic, making it typically slower than a direct push for the same on-chain frequency.
The key trade-off: If your absolute priority is minimizing liquidation risk with the fastest possible on-chain price updates, and you operate on a supported high-throughput chain like Solana, Aptos, or Sui, choose Pyth. Its architecture is built for this specific high-performance use case. If you prioritize gas cost reduction, deployment across dozens of EVM and non-EVM chains, and are comfortable with a design where your protocol controls the final update timing, choose RedStone. Its model offers unparalleled flexibility and cost savings, especially for less time-sensitive positions or on chains with higher base fees.
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