Pyth announces the launch of Pyth Pro: reshaping the market data Supply Chain

Author: Douro Labs

In early 2021, only a few trading firms and exchanges published their trading data on the blockchain network, but today, this attempt has quickly evolved into the most comprehensive institutional-level market data source globally.

As a globally universal pricing layer, Pyth Network has completely reshaped the market data supply chain: 125 institutions have contributed their proprietary data to the network, collectively earning over $50 million; this data supports a trading volume of $1.7 trillion across 600+ applications. Pyth has also collaborated with industry leaders and government agencies such as Cboe, Jane Street, Revolut, and the U.S. Department of Commerce to establish a new paradigm that makes market data more accessible, accurate, and transparent.

Pyth's mission has always been to establish a “single source of truth” for market data. This concept may seem simple, but it carries profound significance. Once achieved, it will reshape the traditional financial system and expand access to global markets. Today, after achieving groundbreaking adoption in the DeFi space and rapidly extending to traditional finance, Pyth is reimagining the market data economy for global institutions.

Today, with Pyth announcing the official release of Pyth Pro, Pyth has taken an important step towards fulfilling its mission, allowing anyone to directly access professional institutional-grade data from the world's leading trading firms.

Pyth Pro aims to provide institutions with a transparent and comprehensive data perspective, covering various asset classes and geographical regions in global markets, eliminating inefficiencies, blind spots, and rising costs in the traditional market data supply chain. Several major institutions, including Jump Trading Group, are participating in the early access program of Pyth Pro, demonstrating a strong demand for new market data solutions.

Problem Analysis: Fragmented, Outdated Systems

Market data is the cornerstone of modern finance, powering every aspect from trading and risk management to clearing and reporting. With the continuous evolution of the internet in terms of speed and accessibility, the financial system is more interconnected and data-driven than ever before, and the importance of market data continues to grow. However, the infrastructure supporting market data today is mired in the dark depths of the pre-internet era, with almost no viable alternatives.

According to statistics, institutions spend more than $50 billion annually on market data, but the systems they rely on are not suited to today's global multi-asset financial environment. Due to the scarcity of market data competitors across regions and asset classes, this has also led to a sharp increase in data costs, which have risen by over 50% in the past three years. Institutions face huge discrepancies in the fees paid for the same products, access is restricted by geography, and new entrants still face very high barriers to entry.

Historical trends highlight this serious imbalance: over the past 25 years, the costs of market data have almost surpassed all major asset classes. This spiraling increase has placed an increasing burden on institutions that provide liquidity and efficiency to financial markets.

Structurally, the flaws are obvious:

Exchanges can only see their own order books, and these order books only cover a small portion of global trading activity.

The supplier will consolidate this part of the data, repackage it into large bundles, and then sell it at a high price.

Trading companies and banks, as institutions that provide the most accurate and high-frequency prices, rarely participate directly in the value creation of data.

This means that the most valuable price data is created upstream (before entering the exchanges), but most of the revenue flows to downstream intermediaries. This outcome is not the fault of either party; market data is constrained by an outdated system that inadvertently reinforces the position of existing enterprises, stifles competition, and limits innovation.

Solution: Pyth Pro

Pyth Pro addresses these systemic inefficiencies by fundamentally reconstructing the market data supply chain.

Institutions do not need to rely on fragmented, repackaged data from downstream, but can directly obtain prices from the companies generating them (trading firms, exchanges, and banks) through a single, unified integration platform.

Several leading institutions, including Jump Trading Group, have begun collaborating with Pyth Pro through the early beta version. As the relevant person in charge of Jump Trading Group stated:

“We are honored to be a long-term supporter of Pyth, which has developed one of the most comprehensive and valuable market data sources in history. Pyth Pro enables more consumers to access this data, including traditional financial companies, and brings competition to the market data economy by providing the purest data directly from the source.”

A source: covering all asset classes and regions

Pyth Pro integrates global data into a distribution network: over 2,000 data streams covering stocks, futures, ETFs, commodities, foreign exchange, cryptocurrencies, and fixed income. Data is updated at millisecond frequency, with uptime exceeding 99.9%, and accuracy reaching 95% relative to NBBO, with new data categories added weekly.

Pyth Pro aims to provide comprehensive market data insights, becoming the most reliable single source of truth for financial data.

Professional data: from companies that truly drive market development

More than 125 leading institutions have contributed proprietary price data to the Pyth Network, including Jane Street, Revolut, Jump, DRW, Optiver, Cboe, and LMAX. These contributions are aggregated through cryptographic verification and establish staking and penalty mechanisms to create a system that captures the sources of price discovery while rewarding accuracy and participation.

A streamlined and cost-effective model

Traditional data providers often charge up to $250,000 per month, but their data coverage is incomplete, forcing institutions to manage multiple data partnership agreements, make redundant purchases, and integrate, resulting in unpredictable costs. Pyth Pro addresses this dilemma with a transparent subscription model that scales according to institutional needs:

Pyth Crypto (Free): Cryptocurrency data updated every second

Pyth Crypto+ (USD 5,000 per month): Crypto data updated every millisecond.

Pyth Pro (10,000 USD per month): Global cross-asset coverage, updated every millisecond, providing enterprise support and redistribution rights.

By eliminating discriminatory pricing, isolated access, and opaque contracts, Pyth Pro not only reduces costs but also opens up the market for broader participation, creating a level playing field for institutions of all sizes and allowing everyone to benefit. Exchanges, trading firms, and banks can realize data monetization more directly; institutions gain more comprehensive and accurate global market data. This will lead to a healthier and more competitive ecosystem: where data is more accessible, accurate, and transparent than ever before.

Implementation method: Redesign of the market data supply chain

Pyth is able to provide functionalities that traditional providers cannot offer, relying on its structural innovation. The Pyth Network is not just another aggregator repackaging downstream data feeds, but fundamentally rethinks the way market data is captured, validated, and distributed globally.

Upstream data sources: Data is directly contributed by pricing entities such as trading companies, exchanges, and banks, even before the data is fragmented, averaged, or delayed.

Data source quality: Over 125 leading institutions contribute proprietary data to Pyth, including Jane Street, Jump, DRW, Optiver, Cboe, LMAX, and the U.S. Department of Commerce.

Transparent Aggregation: Data is aggregated using verifiable methods and confidence intervals are set to ensure that the quality of the data is auditable.

High-performance delivery: Data updates are distributed globally with an end-to-end latency of less than 100 milliseconds and support co-located aggregation low-latency data aggregation mechanisms with millisecond-level accuracy.

Collaborative Incentives: Through staking, penalties, and the Pyth ecosystem incentive model, publishers are rewarded for their accuracy, while the system punishes misconduct.

Sustainable model: Subscription revenue flows back to Pyth DAO, strengthening the incentives for publishers and increasing network value over time.

Pyth Pro aims to replace the fragmented and outdated infrastructure that today's market data relies on, but it does not simply replace existing participants. Exchanges, trading firms, and banks remain at the core of it all: their data is the source of value. By creating a unified, open distribution network, Pyth Pro enables more institutions to access the data they need while allowing data publishers to directly obtain the value they create, further increasing participation and eliminating structural constraints.

The market data model of Pyth Pro is more inclusive, accurate, and sustainable than any existing model. Fundamentally, Pyth Pro aims to create a bigger pie for everyone: contributors receive fair compensation, institutions can gain a more comprehensive and lower-cost understanding of global markets, and the entire financial system will benefit from greater transparency and healthier competitive cycles.

The next wave of financial turmoil has arrived; Pyth Pro is just the first step.

Under the core vision of Pyth “Global Universal Price Layer”, Pyth Pro aims to empower builders, innovators, institutions, and leaders by creating an open, trustworthy, and easily accessible financial system that benefits all participants.

Infrastructure is just the first layer; now everyone will have the opportunity to participate in the construction of the next generation of financial skyscrapers.

Welcome to start your journey with Pyth Pro, experiencing more accurate, convenient, and economical market data.