Pyth excels at delivering ultra-low-latency, high-frequency price data for institutional-grade DeFi because it leverages a first-party data model where publishers like Jane Street and Jump Crypto push signed prices directly to its on-chain Pythnet. This results in sub-second update latencies and a massive data set, securing over $2.5 billion in total value across chains like Solana, Sui, and Aptos. Its pull mechanism allows protocols to fetch the latest verified price on-demand, minimizing gas costs for applications that don't require constant updates.
LABS
Comparisons
Pyth vs RedStone: Pull Oracle Models
A technical comparison for CTOs and protocol architects evaluating pull-based oracles. Analyzes data delivery, cost structure, security models, and ideal use cases for Pyth and RedStone.
Chainscore © 2026
introduction
THE ANALYSIS
Introduction: The Pull Oracle Paradigm
A head-to-head comparison of Pyth and RedStone, the two leading pull-based oracles, focusing on their architectural trade-offs for high-performance applications.
RedStone takes a different approach by decoupling data delivery from storage, using Arweave as a permanent data availability layer and a network of decentralized nodes. This results in a trade-off: while offering unparalleled data breadth (over 1,200+ assets) and seamless multi-chain deployment via its unique RedStonePayload design, the initial data fetch can involve slightly higher latency as data is pulled from the Arweave gateway and validated on-chain. Its model is optimized for cost-efficiency and scalability for protocols on EVM, L2s, and beyond.
The key trade-off: If your priority is minimizing latency for high-frequency trading or perpetuals on a supported chain, choose Pyth. Its first-party data and tight integration provide the speed and reliability demanded by top protocols. If you prioritize maximizing asset coverage, deploying across many ecosystems with a single integration, or minimizing long-term data storage costs, choose RedStone. Its modular, data-availability-first design offers exceptional flexibility and breadth.
tldr-summary
Pyth vs RedStone: Pull Oracle Models
TL;DR: Core Differentiators
Key strengths and trade-offs at a glance.
01
Pyth: Institutional Data Integrity
Direct Publisher Model: Data sourced from 90+ first-party providers (e.g., CBOE, Jane Street). This matters for high-value DeFi protocols like Synthetix and Venus, where data provenance and minimal manipulation risk are non-negotiable.
90+
First-Party Publishers
400+
Price Feeds
02
Pyth: Low-Latency Push Updates
Proactive Data Propagation: Publishers push price updates on-chain (e.g., Solana, Sui) with sub-second finality. This matters for perpetuals and spot DEXs like Drift and Helium, where stale data directly leads to liquidations and arbitrage losses.
< 500ms
Update Latency (Solana)
03
RedStone: Modular & Cost-Efficient
Pull-Optimized Design: Data is stored off-chain (Arweave) and pulled on-demand via signed data packages. This matters for L2s and new chains like Arbitrum and zkSync, where minimizing persistent on-chain storage costs is critical for bootstrapping.
1,000+
Assets Supported
PULL ORACLE MODELS
Feature Comparison: Pyth vs RedStone
Direct comparison of key technical and operational metrics for pull-based oracles.
| Metric | Pyth | RedStone |
|---|---|---|
Data Update Latency (On-Chain) | ~400ms | < 1 sec |
Data Sources per Feed | 80+ | 50+ |
Supported Blockchains | 50+ | 40+ |
On-Chain Data Delivery | Push & Pull | Pull-Only |
Native Token for Staking | PYTH | |
Free Public Data Feeds | ||
Avg. Update Frequency | ~400ms | ~1-5 min |
pros-cons-a
Technical Deep Dive
Pyth vs RedStone: Pull Oracle Models
A side-by-side analysis of the leading pull-based oracle solutions. Pyth leverages a publisher network for high-frequency data, while RedStone uses Arweave for cost-effective, modular delivery.
02
Pyth: Trade-off - Cost & Complexity
Higher on-chain costs and integration complexity: Each price update requires an on-chain verification of multiple publisher signatures, leading to higher gas fees on L1s. This matters for high-volume, low-margin DeFi applications on Ethereum Mainnet where gas optimization is critical. Simpler push oracles like Chainlink can be more cost-effective for less frequent updates.
04
RedStone: Trade-off - Latency & Finality
Potential latency from off-chain data availability: Reliance on Arweave's block time (~2 min) and the pull mechanism can introduce latency vs. purely on-chain verification. This matters for high-frequency trading (HFT) strategies or liquidation engines where a few seconds of lag is unacceptable. Pyth's on-chain aggregation offers stronger real-time guarantees.
pros-cons-b
Pyth vs RedStone: Pull Oracle Models
RedStone: Strengths and Trade-offs
A data-driven comparison of two leading pull-based oracle solutions, highlighting their architectural trade-offs and ideal use cases.
01
RedStone's Core Strength: Modular Data Feeds
Protocol-agnostic data sourcing: RedStone aggregates data from CEXs (Binance, Coinbase), DEXs (Uniswap, Curve), and other providers, allowing dApps to customize their feed composition. This matters for long-tail assets and emerging L2s where Pyth's native coverage may be limited. Supports 1,000+ price feeds.
02
RedStone's Trade-off: On-Chain Verification Overhead
Deferred trust model: Data is pushed to a decentralized cache (like Arweave) and pulled on-demand with a signed data package. The smart contract must verify the signature, adding ~50k-100k extra gas per price fetch compared to Pyth's pre-verified on-chain storage. This matters for high-frequency, low-margin protocols where gas optimization is critical.
03
Pyth's Core Strength: Low-Latency, On-Chain Primitive
Sub-second price updates: Data is continuously pushed and aggregated on a dedicated Pythnet before being published to supported chains via Wormhole. This provides < 1-second latency for top assets. This matters for perpetuals protocols (Drift, Hyperliquid) and options markets where price staleness directly impacts liquidations and PnL.
04
Pyth's Trade-off: Curated Publisher Network & Cost
Permissioned data providers: Relies on a vetted network of ~90 major institutions (Jump Trading, CBOE). While high-quality, this limits feed diversity and makes the system less permissionless for new data sources. Update fees are also borne by publishers, which can limit update frequency for less popular assets. This matters for protocols needing exotic data pairs or maximum decentralization.
GAS AND FEE STRUCTURES
Pyth vs RedStone: Pull Oracle Cost Analysis
Direct comparison of operational costs and fee models for pull-based oracle solutions.
| Metric | Pyth Network | RedStone Oracles |
|---|---|---|
On-Chain Update Cost (Solana) | $0.0001 - $0.001 | $0.001 - $0.01 |
Primary Fee Model | Data Consumer Pays (per update) | Data Provider Pays (subscription) |
Cross-Chain Gas Abstraction | ||
Free Data Feeds Available | ||
Avg. Update Frequency (Solana) | 400ms | User-defined |
Supported Price Feeds | 400+ | 1,200+ |
Native Token Required for Fees |
CHOOSE YOUR PRIORITY
Decision Framework: When to Use Which
Pyth for DeFi
Verdict: The default for high-value, low-latency applications. Strengths: Pyth's pull-based model delivers 400+ price feeds with sub-second latency and on-chain verification. This is critical for perpetuals, options, and money markets where stale data means immediate losses. Its Solana-native design and support for EVM, Sui, Aptos, and Cosmos make it a battle-tested choice for protocols like MarginFi, Drift, and Jupiter. Use Pyth when your primary concern is data freshness and security for large positions.
RedStone for DeFi
Verdict: The flexible, cost-effective alternative for multi-chain and experimental DeFi. Strengths: RedStone's modular design separates data availability (stored on Arweave or Celestia) from delivery, enabling gas-efficient updates and 1,000+ data feeds. Its on-demand fetching via signed data packages is ideal for lending protocols, yield aggregators, and cross-chain applications where batch updates reduce costs. Use RedStone when you prioritize low operational costs, extensive asset coverage, and deployment flexibility across EVM, Starknet, and other L2s.
verdict
THE ANALYSIS
Final Verdict and Recommendation
A data-driven breakdown of the core trade-offs between Pyth's push and RedStone's pull models to guide your oracle selection.
Pyth excels at providing high-frequency, low-latency price data for high-value DeFi applications because of its push-based, on-chain aggregation model. For example, its network of over 90 first-party publishers delivers price updates for assets like SOL and BTC with sub-second latency directly to the Pythnet, securing over $2 billion in total value. This makes it the default choice for perpetuals protocols (e.g., Drift, Hyperliquid) and money markets where stale data equates to immediate risk.
RedStone takes a radically different approach by utilizing a pull-based, data-availability-centric model. This strategy results in a powerful trade-off: it enables support for thousands of assets (including long-tail tokens and real-world assets) at a fraction of the on-chain gas cost, but introduces a slight latency overhead as data must be fetched and verified per transaction. Its use of Arweave for data anchoring and modular design is ideal for scaling novel ecosystems like L2s and app-chains.
The key trade-off is between immediate, guaranteed data freshness and extensive, cost-effective coverage. If your priority is ultra-low latency for mainstream crypto assets in a high-stakes trading environment, choose Pyth. If you prioritize supporting a vast, custom asset universe, optimizing for gas efficiency on L2s, or building on non-EVM chains like Starknet or Fuel, choose RedStone. Your protocol's asset mix, tolerance for update latency, and deployment chain are the ultimate deciders.
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