pythroadmap

Traditional blockchain oracles often rely on a "push" model, where data providers send updates at regular intervals. In contrast, Pyt@undefined mploys a "pull" oracle model . In this model, smart contracts request data when needed, reducing unnecessary updates and associated costs.

Pyth's first-party data model means that data is sourced directly from reputable institutions, such as trading firms and exchanges, rather than from third-party aggregators. This approach enhances data accuracy and trustworthiness, as the data providers have a vested interest in maintaining the integrity of the information they supply .

By combining the pull model with first-party data sourcing, Pyt@undefined ffers a scalable and reliable solution for delivering real-time market data to blockchain applications.
@Pyth Network #PythRoadmap $PYTH

​In the ongoing "oracle wars," the debate often centers on speed and data quality. While these are critical metrics, the true differentiator for long-term viability lies in architectural design. Pyth Network’s "pull model" is a revolutionary departure from the industry standard "push model," and this subtle difference holds the key to its superior scalability and efficiency. Most oracles, including the market leader, operate on a push model where a network of nodes actively pushes price updates to the blockchain, regardless of whether a dApp needs them. This creates a significant amount of on-chain bloat and can lead to unnecessary gas fees. Pyth, on the other hand, operates on a pull model. Instead of pushing data, data providers continuously update a state-of-the-art price feed on Pythnet, which is a specialized blockchain built on Solana. This data is then "pulled" on-chain by the dApp when it's needed, with the cost of the transaction paid by the dApp user.
​This architectural choice is more than just a technical curiosity; it’s a fundamental shift in economic efficiency. The current trend of "cost-conscious" DeFi protocols is directly addressed by Pyth's model. By only publishing the data when it's consumed, the network avoids the high, continuous costs of on-chain storage and transaction fees associated with the push model. A concrete example of this is a long-tail asset. In a push model, an oracle would have to constantly publish updates for a rarely-traded asset, costing the protocol or the data providers money with every block. With Pyth, that data is only brought on-chain when a user specifically needs it for a transaction, say, to settle a perpetual futures contract on a less-common trading pair. This makes it economically viable to support a far greater number of assets, from minor cryptocurrencies to complex financial instruments, without incurring unsustainable costs.
​The unique insight here is the symbiotic relationship this model fosters between data providers and data consumers. Because the cost of on-chain updates is paid by the consumer, data providers are incentivized to contribute a vast array of high-quality data without worrying about prohibitive gas fees. This leads to a virtuous cycle: more data providers contribute more diverse datasets, which in turn attracts more dApps and users who need that data. It's a network effect built on economic logic, not just on a technical feature. This elegant solution to the oracle problem means Pyth is not just competing on speed, but on a more sustainable, scalable economic framework that positions it for a future where on-chain data needs are exponentially more diverse and demanding. 💡
​#PythRoadmap $PYTH @Pyth Network

In today's rapidly developing blockchain and decentralized finance (DeFi) landscape, the importance of market data is often underestimated. However, without timely, accurate, and reliable data support, no financial market can operate efficiently. Price data, volatility indices, trading volumes, and reference prices for derivatives form the foundation on which the financial system operates. The Pyth Network is representative of this new reality, becoming a new generation of decentralized data infrastructure.
In traditional financial markets, data is often held by a few giants, such as Bloomberg, Refinitiv, and FactSet, which provide subscription services to institutions at licensing fees worth thousands or even millions of dollars each year. This 'data oligarch' model, while effective in the Web2 financial system, seems out of place in the decentralized Web3 world. Web3 emphasizes openness, transparency, and verifiability, and data should adhere to these principles. The emergence of the Pyth Network is an innovative response to this issue.