What are Oracles?

Blockchains in and of themselves are useful already, for trustless and permissionless transactions without censorship. No trust or verification from the user is required because it is stored on a decentralised ledger with global consensus. What if certain transactions require reliable and real-time data from external sources that do not necessarily have a global consensus or can be stored on the same ledger? For example:

• Products that rely on price feeds of assets from other blockchains or real-world markets: Many decentralized finance (DeFi) applications, like decentralized exchanges or lending platforms, need accurate and timely information about asset prices (e.g., stocks, cryptocurrencies, commodities). Since these prices are continuously changing in real-world markets, blockchains need a way to securely access this off-chain data.
• Products that require verifiable and secure random numbers: Randomness is crucial for a variety of blockchain use cases, such as lotteries, gaming, and even secure cryptographic protocols. However, generating truly random numbers on-chain is challenging without introducing bias or predictability. Off-chain randomness, when provided by a reliable source, is often needed.
• Products dependent on historical price data: Some DeFi platforms and financial products might need access to archived price data for risk assessment, backtesting trading strategies, or offering historical analysis. Since blockchains primarily focus on storing current state information, they need external sources to provide this historical data efficiently.

To address these challenges, Oracles were introduced. Oracles serve as bridges between blockchains and the external world, providing smart contracts with access to off-chain data. They connect external data providers—such as market data owners, web APIs, or IoT devices—to decentralized applications across multiple blockchains. Oracles enable these applications to securely and reliably obtain real-time data, execute transactions based on external events, and interact with data that cannot be directly stored on-chain.

Why can this data be trusted? Oracles provide a robust mechanism for ensuring the integrity and reliability of off-chain data before it is used on the blockchain. An oracle network verifies the:

• Authenticity: To ensure that the data is genuine and comes from a legitimate source, oracle networks source data from multiple trusted providers or verifiable APIs. This process reduces the risk of malicious or false information being introduced into smart contracts.
• Accuracy: Accurate data is crucial for smart contracts to function correctly. Oracles achieve this by aggregating data from several independent sources. Instead of relying on a single provider, an oracle network will query multiple data sources and compare their responses.
• Reliability: Oracle networks enhance reliability by using decentralized nodes, which increases resilience against failures or malicious activity. If one data source or node fails or provides incorrect information, the other nodes in the network can continue to operate and provide valid data.

The demand for accurate and reliable off-chain data is growing as the number of real-world use-cases and adoption of blockchain increases. Users of applications are more than willing to pay for an oracle service that is accurate and reliable and covers a large variety of use-cases.

Pyth Network versus Other Oracles

Read the blog post of Battle of the Oracles to learn more about the different oracles solutions. To recap, Pyth Network is a high-frequency oracle leveraging Solana's technology, offering a robust solution for off-chain data sharing for primarily decentralized finance applications (DeFi). It provides services like real-time price feeds and benchmarks, accessible to a wide range of financial service providers. PYTH is the governance token and utility token of the Pyth Network. Supply and demand for the PYTH token is directly related to level of usage and total demand of Pyth’s services and Pyth Network’s Tokenomics.

Total Value Secured by Oracles

While Chainlink holds the lion’s share of the total value secured by oracles, Pyth has shown by far the largest growth in terms of TVS, number of protocols supported and number of DApps. Pyth is expanding rapidly, across different networks and protocols, supporting more DApps, data providers and integration partners every day. In the same time frame, Chainlink’s marketshare has decreased. Comparing the main metrics of MCAP/TVS ratio and MCAP/TTV ratio, we notice that based on market capitalization (circulating supply), Pyth is undervalued whereas the TVS ratio based on fully diluted value paints a different picture. This is because only 37% of PYTH tokens are unlocked, the next significant PYTH token unlock takes place in May of 2025 and happens yearly thereafter on the same date until the full amount of tokens has been unlocked by 2027.

Use-cases Enabled by Pyth

Products and Services:

• Price Feeds: real-time market data for smart contracts, blockchains, and applications
• Benchmarks: historical market data for smart contracts, blockchains, and applications
• Express Relay: smart contracts or protocols that need protection against MEV (Express Relay) Express Relay is one of a kind product that offers developers to auction off valuable transactions directly to MEV searchers without validator interference
• Entropy: smart contracts that require secure on-chain random numbers. Secure and verifiable random numbers are incredibly important for creating a fair and unpredictable on-chain actions (e.g., for games)
• Pyth DAO Governance model

Examples:

• Decentralised Exchanges (DEXs) require reliable real-time price feeds to provide users accurate trades.
• Pyth’s data pull model provides data directly from the source, such as exchanges, market makers or DeFi protocols. Because data is pulled only on demand and not pushed at a given interval, it scales efficiently, and costs are offloaded to users where updates are demand-based.

Case Study: Drift (DEX)

Refresher: What is a DEX?

Decentralized Exchange (DEX) allows users to trade cryptocurrencies directly, without intermediaries, using smart contracts on a blockchain. DEXes operate peer-to-peer, providing greater privacy and control over assets compared to centralized exchanges.

There are two main types of DEXes:

1. Order Book DEXes: These platforms match buy and sell orders using a live order book, similar to traditional exchanges. Examples include dYdX.
2. Automated Market Makers (AMMs): AMMs use liquidity pools and algorithms to determine asset prices, allowing users to trade instantly without needing a counterparty. Examples include Uniswap and SushiSwap.

Context

Drift is a perpetual trading DEX built on Solana. Speed, reliability, and performance make or break a perpetual trading ecosystem. Drift is a perpetual trading platform that allows traders to create leveraged positions against the performance of synthetic assets.

Why Pyth?

Drift seeks to offer the most feature-rich, powerful perpetual DEX with lightning-fast execution. This ambition necessitates a robust Oracle solution. Legacy oracles are slow and susceptible to front and back running.

Pyth and Drift partnered to rapidly deploy a proof-of-concept. This successful relationship satisfies the ultra-fast network requirements of Drift’s execution tools and is capable of supporting thousands of users and hundreds of assets.

This is only one of many examples of an effective partnership and integration that gives Web3 users an enhanced user experience than DApps that use other Oracle solutions. There are presently over 410 integration partners supporting the transition from push to pull Oracles with Pyth Networks.

Pyth versus Chainlink

We compare Chainlink and Pyth Network with two main metrics: Total Value Secured (TVS) and Total Transaction Volume (TTV)

Total Value Secured

Pyth’s Total Value Secured (TVS) is more distributed across different blockchains and applications compared to Chainlink, offering greater resilience and diversification. Here's how the comparison breaks down:

• Blockchain Distribution: Pyth’s TVS shows a broader spread across multiple blockchains. For instance, only 61.1% of Pyth’s TVS is concentrated on the Solana blockchain, which means the remaining value is distributed across other blockchains, contributing to its decentralized footprint. In contrast, 97.1% of Chainlink’s TVS is concentrated on Ethereum, creating a higher dependence on a single blockchain. This heavy reliance on Ethereum makes Chainlink more vulnerable to network-specific issues, such as scalability concerns or market downturns affecting Ethereum.
• Application Distribution: Pyth also demonstrates a healthier diversification across different applications. Only 23.8% of Pyth’s TVS is tied to its top application, meaning the remaining value is distributed among various other applications. This broader application spread lowers the risk of one dominant app affecting the network’s overall performance. Chainlink, however, has 48.8% of its TVS tied to its top application, meaning nearly half of its secured value relies on a single application. This concentration creates a potential single point of failure, making Chainlink more sensitive to shifts in the usage or success of that key application.

Pyth's more balanced distribution of TVS across different blockchains and applications enhances its resilience. With a healthier spread of its value, Pyth is better positioned to withstand market fluctuations or downturns that may affect individual blockchains or applications, making it less exposed to risks associated with dependency on any single network or product. This diversified approach gives Pyth a structural advantage in terms of long-term stability and adaptability.

Total Transaction Volume

Another, perhaps better, metric to measure the true market share and usage of an Oracle network is TTV (Total Transaction Volume). TTV is strongly correlated with the frequency of oracle price updates and therefore oracle revenue and true demand for its products and services. TVS can overstate or understate an application’s demand for price updates, because an application could have a disproportionate amount of locked value relative to the amount of Oracle interactions one would expect to observe.

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