Prediction markets often look simple from the outside: users place trades, prices move, and outcomes resolve. Internally, however, a production-grade prediction market behaves much closer to a real-time trading exchange than a traditional gaming or betting application.

At scale, every millisecond matters. Every wallet movement must be correct. Every price update must remain consistent. And every system component must continue working even when traffic spikes unpredictably.

This article explains how we designed and optimized our prediction market platform to reliably handle 10,000+ trades per second, while still remaining extensible, customizable, and safe for enterprise deployments.

What a “Trade” Actually Means in Our System

In many systems, a “trade” is treated as a single action—one request, one database write. In a prediction market, this assumption breaks down quickly.

In our architecture, a trade represents a deterministic pipeline of tightly coordinated operations, each with explicit consistency and latency guarantees. When a user places a trade, the system must validate the market state, ensure the user has sufficient balance, debit funds, compute updated prices using the Automated Market Maker (AMM), match the order against available liquidity, and finally broadcast the updated state to all connected users.

Some of these steps must be completed synchronously to preserve user experience and financial correctness, while others are intentionally deferred and processed asynchronously. This separation is one of the most important reasons the platform scales reliably.

Equally important is the distinction between read-heavy and write-heavy workloads. The majority of traffic in a prediction market consists of users reading prices, charts, and market states, while a smaller but critical portion involves writes such as trades and wallet updates. Our system is optimized around this reality.

From a system perspective, every trade can be broken down into three critical phases:

Order Placement vs AMM Swap

A trade begins with order placement. The system validates the request against market state, trading window, and risk limits. If sufficient opposing liquidity exists, the trade may match user-to-user and user-to-amm.

Wallet Debit and Credit

Every trade immediately interacts with the wallet system. Funds are debited synchronously using an append-only ledger model. This ensures that even if downstream processes fail, balances are never left in an inconsistent state.

Price Update

After execution, the AMM recalculates outcome probabilities based on volume, imbalance, and configured liquidity parameters. Updated prices are written to Redis and broadcast in real time to all active clients.

Read vs Write Workload Split

At scale, we observe that most system load is read-heavy. Users continuously fetch prices, charts, and portfolios, while writes (orders, ledger entries, settlements) represent a smaller but critical fraction. This split strongly influenced our caching strategy and database design.

Design Constraints That Shaped the Architecture

Before optimizing for performance, we defined strict non-negotiable constraints.

Latency targets were clear: trade acknowledgements and wallet updates needed to feel instantaneous, even under load. At the same time, the system could not compromise on correctness. Double spending, incorrect balances, or inconsistent pricing were unacceptable, regardless of traffic volume.

We also assumed failure as a normal operating condition. Application crashes, queue backlogs, cache evictions, and database failovers were all treated as expected scenarios rather than rare edge cases. Every trade had to either complete fully or fail safely without leaving the system in an inconsistent state.

These constraints ruled out many scaling approaches and pushed us toward an event-driven, carefully layered architecture.

Latency Targets (p50 / p95)

User-facing actions—especially order placement and wallet updates—must feel instantaneous. Our targets were sub-50ms latency for the median request and sub-150ms latency at the 95th percentile, even during peak load.

Consistency Requirements

Because the platform handles real money, correctness always outweighs raw speed. The system must prevent double spending, ensure balances are always accurate, and guarantee that market prices never drift into invalid states (for example, probabilities not summing correctly).

Failure Handling

We assume failures will happen: node crashes, queue backlogs, cache evictions, or database failovers. Every trade must either complete fully or fail cleanly without side effects. This assumption drove us toward idempotent APIs, ledger-based accounting, and asynchronous recovery mechanisms.

High-Level Architecture Overview

At a high level, the platform is built as a stateless, cloud-native system composed of independently scalable services. The core trading engine runs on Node.js, while background processing and third-party feed ingestion are handled separately to avoid interfering with live trading.

Redis sits at the center of the system as a real-time cache for prices, sessions, and frequently accessed data. Transactional data is stored in MySQL for strong consistency guarantees, while historical and analytical data is offloaded to MongoDB to keep the hot path lean.

Message queues connect everything together, allowing the system to absorb bursts of traffic without overwhelming any single component.

Event-Driven Trade Processing

One of the most important architectural decisions was to separate the trade lifecycle into synchronous and asynchronous stages.

The synchronous path handles only what is absolutely necessary to confirm a trade: validation, wallet update, and trade acceptance. These operations are designed to complete extremely quickly and are protected by lightweight locking and idempotency checks.

Everything else—AMM recalculation, order matching, settlement, notifications, and analytics—is processed asynchronously using message queues. This allows the platform to maintain low latency for users while still performing complex computations reliably in the background.

Financial Correctness Through Ledger-Based Design

Wallet operations are implemented using an append-only ledger model rather than mutable balance fields. Every debit, credit, refund, or settlement is recorded as a new ledger entry. A user’s balance is derived from the sum of these entries.

This approach eliminates race conditions, makes the system crash-resilient, and provides a complete audit trail for every financial movement. It also scales far better under concurrency, as writes become sequential inserts rather than conflicting updates.

Caching and Real-Time Updates at Scale

Redis plays a critical role in ensuring performance at scale. Live prices, order books, session data, and frequently accessed market metadata are served directly from memory. As a result, most user interactions never touch the database at all.

Price updates and portfolio changes are propagated in real time, allowing thousands of users to observe market movements simultaneously without creating database hotspots.

Optimizing the AMM Hot Path

The Automated Market Maker is one of the most performance-sensitive components of the system. Pricing calculations are optimized to avoid expensive operations, and intermediate states are cached to reduce repeated computation.

To prevent contention, each market operates with a single-writer model enforced using short-lived Redis locks. This ensures price integrity without introducing global bottlenecks. During burst traffic, price updates are batched and throttled at millisecond granularity, preserving responsiveness while reducing unnecessary recalculations.

Safeguards such as exposure limits and validation checks are carefully placed so they do not slow down the hot trading path.

How We Tested and Validated Scale

We validated our architecture through extensive load testing rather than theoretical estimates. Using industry-standard tools and realistic traffic models, we simulated both sustained high-volume trading and sudden burst scenarios.

During early tests, bottlenecks appeared in wallet locking and excessive synchronous writes. By moving to a ledger-based model, pushing settlement to asynchronous workers, and increasing cache efficiency, these issues were eliminated.

The result was stable, sustained performance at 10,000+ trades per second, with the ability to scale further through horizontal infrastructure expansion.

Customization Without Compromising Performance

A core principle of our platform is that customization must never weaken the core engine. When deploying client-specific features or integrations, we ensure that the trading engine, AMM logic, and wallet system remain unchanged.

Custom logic is added through APIs, feature flags, background services, or external integrations. This guarantees that even heavily customized deployments retain the same performance, reliability, and scalability characteristics as the base platform.

Final Thoughts

Scaling a prediction market is not about brute force. It is about understanding where time is spent, where contention occurs, and how correctness and performance interact.

By combining an event-driven architecture, ledger-based financial design, optimized AMM computation, aggressive caching, and asynchronous processing, we have built a prediction market platform that remains fast, correct, and reliable—even under extreme load.

Most importantly, this performance holds true even when the platform is customized for different clients, payment systems, and integrations.

That is how we optimized our prediction market software to handle 10,000+ trades per second—and why it continues to scale confidently as demand grows.