Pyth excels at leveraging high-fidelity data from a permissioned, institutional network of over 90 first-party publishers like Jane Street and CBOE. This results in ultra-low latency and high-frequency updates (e.g., 400ms updates for major assets) with robust data quality. Its pull-based model, where data is aggregated on-chain only when a user request triggers it, optimizes for cost-efficiency and integration with high-throughput chains like Solana and Sui.
LABS
Comparisons
Pyth vs Supra: Oracle Decentralization
A technical comparison of Pyth Network and Supra Oracles, focusing on their decentralization models, data delivery mechanisms, and trade-offs for CTOs and protocol architects.
Chainscore © 2026
introduction
THE ANALYSIS
Introduction: The Decentralization Dilemma in Oracle Design
A data-driven comparison of Pyth and Supra's contrasting approaches to decentralizing price feeds.
Supra takes a radically different approach with its Distributed Oracle Agreement (DORA) consensus, which requires a supermajority of its decentralized node network to attest to data before it's pushed on-chain. This Byzantine Fault Tolerant (BFT) process, combined with a pull-push hybrid model, prioritizes cryptographic security and liveness guarantees, aiming for sub-2-second finality. The trade-off is a more complex infrastructure that may have different gas cost implications compared to pure pull oracles.
The key trade-off: If your priority is minimizing latency and cost with institutional-grade data for DeFi applications on fast L1/L2s, choose Pyth. If you prioritize maximizing cryptographic security and consensus-based liveness for applications where oracle manipulation resistance is paramount, choose Supra.
tldr-summary
Pyth vs Supra: Oracle Decentralization
TL;DR: Key Differentiators at a Glance
A data-driven comparison of two leading oracle networks, highlighting their architectural trade-offs and ideal deployment scenarios.
01
Pyth: Unmatched Data Provider Network
Specific advantage: Integrates 100+ first-party data providers (e.g., Jane Street, CBOE) publishing directly to the network. This matters for protocols requiring institutional-grade price feeds with deep liquidity and verifiable provenance, such as perpetual DEXs like Hyperliquid or structured products.
02
Pyth: Cross-Chain Dominance
Specific advantage: 50+ supported blockchains via its Pull Oracle model. This matters for multi-chain DeFi applications (e.g., lending on Solana, trading on Arbitrum) that need consistent, low-latency data across a fragmented ecosystem without managing multiple oracle contracts.
03
Supra: Novel Consensus for Speed & Finality
Specific advantage: DORA consensus and Moonshot consensus achieve 3-5 second finality with cryptographic proofs. This matters for high-frequency trading, gaming, and options protocols where sub-second price updates and guaranteed finality are critical for user experience and security.
04
Supra: Integrated Data Feeds & VRF
Specific advantage: Native Verifiable Random Function (VRF) and intra-block updates bundled with its oracle service. This matters for NFT gaming, lottery dApps, and prediction markets that need both reliable randomness and frequent price updates within the same trusted network, simplifying tech stack.
05
Choose Pyth If...
Your priority is maximum data source credibility and broad chain coverage. Ideal for:
- Institutional DeFi and derivatives platforms.
- Cross-chain protocols that cannot be tied to a single L1/L2.
- Projects where the brand reputation of data publishers (e.g., exchanges, market makers) is a key trust signal.
06
Choose Supra If...
Your priority is ultra-low latency finality and integrated randomness. Ideal for:
- High-speed Perps DEXs and options protocols on a single high-performance chain.
- Web3 Gaming and NFT minting requiring both price oracles and a secure VRF.
- Applications where 3-5 second data finality is a non-negotiable requirement for logic execution.
ORACLE DECENTRALIZATION COMPARISON
Head-to-Head Feature Comparison: Pyth vs Supra
Direct comparison of key decentralization, performance, and data metrics for oracle networks.
| Metric | Pyth | Supra |
|---|---|---|
Data Publishers (Node Operators) | 100+ | ~50 |
Consensus Mechanism | Wormhole + P2P | Moonshot Consensus |
Data Update Frequency | ~400ms | ~500-1000ms |
Supported Blockchains | 60+ | 25+ |
On-Chain Data Feeds | 400+ | 800+ |
Native Token for Staking/Security |
PYTH VS SUPRA
Technical Deep Dive: Pull vs Push and Consensus Models
This analysis dissects the core architectural differences between Pyth Network and Supra Oracles, focusing on their data delivery mechanisms and the consensus models that underpin their security and decentralization claims.
Pyth uses a pull model, while Supra uses a push model. In Pyth's pull architecture, applications (like Solana or EVM dApps) must actively request on-chain price updates, paying the gas fee. Supra's push model automatically broadcasts price updates to all subscribed chains at predefined intervals, with Supra covering the gas costs. This makes Pyth more gas-efficient for infrequent updates but places update initiation on the dApp. Supra simplifies integration but requires a robust cross-chain infrastructure to manage costs.
pros-cons-a
PROS AND CONS
Pyth Network vs Supra: Oracle Decentralization
A data-driven comparison of decentralization models, data sources, and network architecture for CTOs evaluating oracle dependencies.
01
Pyth's Data Provider Network
Specific advantage: 90+ first-party data providers (e.g., CBOE, Binance, Jane Street). This matters for protocols requiring institutional-grade, auditable price feeds with direct source accountability. The model reduces reliance on aggregated third-party APIs.
02
Supra's Distributed Oracle Committees
Specific advantage: Uses a randomized, multi-round consensus (DORA) among node committees for finality. This matters for achieving high-throughput, low-latency updates (targeting < 500ms) with Byzantine fault tolerance, prioritizing performance in finality.
03
Pyth's Pull vs. Push Model
Trade-off: Pyth uses a pull-based model where consumers request on-demand price updates. This optimizes for cost-efficiency (pay per update) but introduces latency (2-5 seconds) versus constant push streams. Best for less time-sensitive applications.
04
Supra's Cross-Chain Native Design
Specific advantage: Built from the ground up for interoperability with a proprietary cross-chain consensus. This matters for protocols deploying on 10+ heterogeneous L1/L2s (Solana, Sui, Aptos) seeking a single, consistent oracle solution across all chains.
05
Pyth's Solana & SVM Primacy
Trade-off: Deeply optimized for the Solana Virtual Machine (SVM) ecosystem, offering sub-second updates and low fees on Solana. This creates a performance asymmetry; while multi-chain, it is the dominant choice for high-throughput Solana DeFi (e.g., Jupiter, Drift).
06
Supra's Incentive & Slashing Mechanism
Trade-off: Employs a cryptoeconomic security model with staking, rewards, and slashing for node operators. This aims to strengthen data integrity and liveness guarantees but introduces complexity and different risk vectors compared to provider reputation-based models.
pros-cons-b
Pyth vs Supra: Oracle Decentralization
Supra Oracles: Strengths and Trade-offs
A data-driven comparison of two leading oracle networks, focusing on their architectural approaches to decentralization, performance, and security.
01
Pyth's Strength: Institutional Data Depth
First-party data from 90+ major institutions including Jane Street, CBOE, and Binance. This provides a high-fidelity, low-latency feed for institutional-grade assets like equities, forex, and commodities. This matters for TradFi DeFi applications requiring regulated market data.
90+
First-Party Publishers
350+
Price Feeds
02
Pyth's Trade-off: Permissioned Core
Publisher network is permissioned and curated. While the data aggregation and consensus (Pythnet) are decentralized, the initial data sources are vetted entities. This creates a potential centralization vector at the source layer. This matters for protocols prioritizing maximal censorship resistance and permissionless participation at all levels.
03
Supra's Strength: Multi-Round Consensus
Proprietary DORA consensus with multiple validation rounds (intra- and inter-cluster). This design aims for Byzantine Fault Tolerance (BFT) with fast finality (< 3-4 seconds) and robust liveness guarantees. This matters for high-frequency trading (HFT) dApps and derivatives where speed and reliability are non-negotiable.
< 4s
Data Finality
04
Supra's Trade-off: Novel, Less Battle-Tested
Relatively new mainnet launch (2024) compared to Pyth's multi-year production history. While its technical design is ambitious, it lacks the extensive real-world economic security and proven resilience under extreme market conditions of more established oracles. This matters for mission-critical, high-TVL protocols where proven track records are paramount.
05
Choose Pyth For:
- Institutional asset coverage (stocks, ETFs, forex).
- Integration with Solana and SVM ecosystems as a native oracle.
- Protocols that prioritize data source reputation and institutional trust.
06
Choose Supra For:
- Cross-chain applications needing fast, consistent data across 50+ blockchains.
- Latency-sensitive use cases like on-chain gaming or options trading.
- Protocols valuing a novel, multi-layered consensus approach to decentralization.
CHOOSE YOUR PRIORITY
Decision Framework: When to Choose Pyth vs Supra
Pyth for DeFi
Verdict: The established standard for high-value, cross-chain DeFi. Strengths: Unmatched TVL integration with protocols like Synthetix, Venus, and MarginFi. Battle-tested with over $100B in secured value. Superior data coverage for niche assets (e.g., FX, commodities) crucial for perps and structured products. The Pythnet architecture provides high-frequency updates ideal for low-latency trading venues. Trade-off: Higher cost per price update and more complex pull-based integration.
Supra for DeFi
Verdict: A high-performance challenger for cost-sensitive, high-throughput applications. Strengths: Lower latency and cost via its push oracle model and optimized consensus (Moonshot). Excellent for high-frequency DEXs and money markets requiring sub-second updates without prohibitive fees. Native support for Volatility (DIVA) and VWAP oracles offers advanced data products. Trade-off: Smaller established footprint compared to Pyth's ecosystem; data set is growing but currently narrower.
verdict
THE ANALYSIS
Final Verdict: Choosing Your Oracle Foundation
A data-driven breakdown of the decentralization and performance trade-offs between Pyth and Supra to inform your protocol's oracle selection.
Pyth excels at providing ultra-low-latency, high-frequency price data for capital markets because of its unique pull-based model and network of over 90 first-party data publishers. For example, its data is updated on-chain every 400ms on Solana, enabling sub-second oracle updates critical for perpetuals DEXs like Drift Protocol and perpetuals options platforms like Zeta Markets. Its massive $2.5B+ Total Value Secured (TVS) demonstrates deep integration with high-throughput DeFi.
Supra takes a different approach by prioritizing a highly decentralized, consensus-driven architecture with its novel Distributed Oracle Agreement (DORA) and Moonshot consensus. This results in a trade-off of slightly higher latency (2-5 second finality) for enhanced cryptographic security and liveness guarantees, making it resilient to data manipulation attacks. Its design is optimized for applications where security and censorship resistance are paramount over millisecond updates.
The key trade-off: If your priority is sub-second latency for derivatives, spot trading, or liquid staking on high-performance L1/L2s like Solana, Sui, or Aptos, choose Pyth. If you prioritize maximized decentralization and Byzantine fault tolerance for cross-chain smart contracts, reserve-backed stablecoins, or insurance protocols where data integrity is non-negotiable, choose Supra.
ENQUIRY
Get In Touch
today.
Our experts will offer a free quote and a 30min call to discuss your project.
NDA Protected
Confidentiality24h Response
Time to ValueDirectly to Engineering Team
No Sales Fluff10+
Protocols Shipped
$20M+
TVL Overall