Allora is now live on Monad Mainnet, and predictive inferences from the Allora Network are available to developers in the Monad ecosystem. With Allora’s self-improving intelligence layer on Monad, the ultra-low latency EVM-compatible L1, developers can build applications that anticipate change instead of reacting to it.

Monad’s design combines speed, scalability, and decentralization with full EVM compatibility. With up to 10,000 transactions per second, sub-second finality, and very low fees, Monad offers the ideal foundation for DeFi and AI-powered systems that require real-time intelligence. Developers can now use familiar Solidity tools while taking advantage of Allora’s predictive data feeds to make applications more dynamic, responsive, and capital-efficient.

Why Predictive Intelligence Matters

Most decentralized systems today react to events only after they occur. Allora changes that dynamic. As a decentralized Model Coordination Network (MCN), it fuses insights from thousands of independent machine learning models into a single, continuously improving source of intelligence. This process, known as Inference Synthesis, transforms fragmented data into forward-looking signals developers can trust.

By intelligently weighting and combining model outputs, Allora generates predictive data feeds that evolve with market performance. These live forecasts, covering metrics such as price and volatility, equip developers to design applications that plan ahead rather than respond late.

Monad’s high-speed, EVM-compatible architecture creates a new standard for proactive DeFi. Builders can now compose smarter and more adaptive protocols that adjust in real time to emerging conditions, improving capital efficiency and user outcomes.

What Developers on Monad Can Do Today

Allora’s forecasts are available onchain via inference contracts or off-chain via API, enabling composable intelligence across Monad-based protocols. Builders can integrate these live predictive feeds into their smart contracts and agents to create applications that respond ahead of time to market shifts.

Examples include:

• Predictive Yield and Risk Vaults: Vaults that rebalance before markets move, using forecasts for volatility, liquidity, and funding dynamics to improve capital efficiency and reduce drawdowns.
  
  
• Adaptive Lending and ALM: Lending markets that adjust collateral ratios and interest rates proactively based on predicted volatility, utilization, and liquidity conditions.
  
  
• Dynamic-Fee DEXs: AMMs that optimize execution quality and LP returns by tuning fees in response to anticipated volatility and liquidity competition.
  
  
• Perpetuals and Derivatives Risk: Perps engines that anticipate volatility and funding trends, strengthening margin management and liquidation logic.
  
  
• Autonomous AI Agents: Agents that plan and execute trading, hedging, or liquidity strategies based on live predictions rather than static heuristics.
  
  
• Indexes and Structured Products: Indexes that dynamically adjust exposure using forward-looking signals for direction, dispersion, or volatility regimes.
  
  
• Prediction Markets: Markets that incorporate collective-intelligence forecasts on prices, volumes, outcomes, or other time-series data to improve accuracy and liquidity.

From Use Case to Design Pattern

Once developers integrate forecasts, they can combine multiple signals to create stronger, more adaptive strategies.

Here are a few proven patterns emerging across early adopters:

• Leveraged Looping Agents use both price and volatility forecasts to determine loop sizing, buffer ratios, and unwind triggers before conditions deteriorate.
• ALM for Concentrated Liquidity positions and adjusts ranges using predicted price bands, volatility, and volume data to capture more fees and reduce idle capital.
• Dynamic-Fee AMMs adjust fees automatically based on forecasted volatility and user flow to attract healthy liquidity and protect LP returns.
• Predictive Trading and Hedging blends short-horizon price forecasts with longer-term volatility trends to automate delta-neutral and directional strategies.
• Smarter DCA Strategies time entries using multi-horizon forecasts, allocating capital dynamically to achieve better long-term cost bases.

In practice, signals such as price, volatility, and volume often reinforce one another. Blending them yields more robust and responsive outcomes than using any single forecast alone.

Available topics include forecasts for volatility and asset price, with additional topics continuously added as the network evolves. The Allora Network learns over time which models to trust most, producing intelligence that continually improves as more developers and contributors participate.

Build Smarter with Allora

On Monad, predictive intelligence now moves as fast as the network itself. Allora’s intelligence layer is modular, open, and ready to integrate, giving builders the tools to create applications that think ahead.

Visit the Developer Docs to query your first forecast.

Join the Discord community to propose new topics and collaborate with contributors.

Allora on Monad brings predictive intelligence to the core of DeFi, enabling applications that think ahead, move faster, and adapt continuously.

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About Monad

Monad is a high-performance, EVM-compatible L1 built for scale. It delivers high throughput, sub-second finality, low fees, and robust decentralization, enabling a new generation of decentralized applications that require speed and reliability without sacrificing security.

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To learn more about Monad, visit the Monad website, X, and Developer Docs.

About the Allora Network

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Allora is a self‑improving, decentralized Model Coordination Network (MCN). Instead of providing monolithic models, Allora dynamically coordinates and aggregates thousands of models to solve objective‑centric tasks. This approach enables the network to produce better intelligence than any single model yields on its own, creating a smarter, more secure intelligence that anyone can integrate.

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To learn more about Allora Network, visit the Allora website, X, Blog, Discord, Research Hub, and developer docs.

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