Deal adds Solana’s breakout execution platform to MoonPay’s expanding financial stack
MoonPay, the leading crypto payments network, announced the acquisition of DFlow, the fastest-growing trading infrastructure platform on Solana.
Since April 2025, DFlow has processed more than $50 billion in cumulative trading volume — with over $12 billion in Q1 2026 alone — and serves over 1 million active traders across more than 500 applications. The platform processes ~10 million transactions per month with 99.9% token coverage on Solana, and powers major platforms including Coinbase, Phantom, Solflare, and Kamino. In November 2025, it became the first aggregator ever to surpass Jupiter in daily trading volume on Solana. During peak periods, more than 85% of Solana blocks include a DFlow-powered transaction. DFlow also built the first and only system that fully tokenizes Kalshi’s prediction markets on Solana.
DFlow is the latest in a series of acquisitions MoonPay has made since January 2025, each expanding the platform in a specific direction – from payments and stablecoins to institutional custody and now high-performance onchain execution.
“DFlow has become one of the most important pieces of trading infrastructure on Solana in just a year,” said Ivan Soto-Wright, Founder and CEO of MoonPay. “By bringing their execution layer into MoonPay, we’re adding the speed, reliability, and scale needed to support everything from high-volume trading to the next generation of agent-driven financial applications.”
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DFlow’s rapid adoption was driven by a technical breakthrough: just-in-time routing. Traditional aggregators compute the optimal route before a transaction is submitted to the network, but on a high-throughput chain like Solana, where prop AMMs update prices at high frequency, conditions can shift between the moment a route is calculated and the moment it executes onchain. DFlow was the first aggregator to re-optimize trades dynamically during onchain execution itself, checking venue prices at the point of settlement and rerouting within the same transaction if conditions have changed. The result is better pricing, fewer failed transactions, and a system that improves under load rather than degrading.
“DFlow was built to solve one of the hardest problems in crypto: delivering reliable execution in a fragmented onchain environment,” said Nitesh Nath, Founder and CEO of DFlow. “Joining MoonPay allows us to scale that infrastructure globally and support a new generation of applications, from trading platforms to autonomous agents.”
Tokenization and Prediction Markets
Beyond token trading, DFlow provides the infrastructure layer for a new category of programmable financial markets onchain. The company built the first and only API that fully tokenizes Kalshi’s prediction markets on Solana, giving developers direct access to real, onchain representations of every market in Kalshi’s orderbook, by representing each Kalshi market position as a native Solana token, minted and settled through DFlow’s infrastructure.
By exposing regulated, offchain liquidity through a developer-facing API, DFlow makes prediction markets composable for the first time. Applications can embed forecasting markets directly into their product surfaces, and positions can interact with existing financial infrastructure through standard token interfaces.
This same system now underpins prediction market functionality across leading consumer applications and the system is being extended to support a broader class of tokenized real-world assets.
Built for the Agent Economy
DFlow is designed for programmatic and autonomous trading. Its Agent CLI allows developers to provision trading agents with a Solana wallet and execution capabilities in a single step, while integrations with MoonPay’s CLI enable funding from fiat to crypto. The system integrates with Claude Code through a native MCP server, allowing AI agents to access trading functionality directly within their workflows. Operators can define guardrails, including trade size limits, approved assets and rate controls, so agents can operate within clear parameters.
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