Inside the AI Ad Server That Scales to 10 Million Concurrent Users

~Paras Choudhary

Advertising is an exercise in precision engineering across TV, mobile, and web platforms. Each ad impression represents a time-sensitive decision shaped by shifting user behavior, inventory availability, brand safety constraints, and privacy requirements. When delivery lags or targeting fails, monetization suffers.

At a leading FAANG company, I led the development of an AI-powered ad server and decisioning engine capable of supporting over 10 million concurrent users. The system enabled real-time personalization, maintained low latency, and delivered high monetization yields across video and display inventory.

This article outlines the technical and organizational strategies behind building a platform of this scale and complexity.

The Objective: Scalable Intelligence for Ad Decisioning

Legacy ad systems often rely on rule-based engines. While transparent and suitable for narrow domains like finance (Addepto, 2023), these systems struggle under the volume and variability of modern advertising requirements. They rely on static “if-then” logic, and as rule sets grow, they become harder to maintain, slower to evaluate, and less adaptable.

Our goal was to build a unified AI-powered decisioning engine that could support targeting, brand safety, auction logic, programmatic buying/selling, and ad delivery, all within a sub-second decision window. This required continuously retraining models on streaming session data, including user searches, viewing patterns, and interactions, so decisions could adjust dynamically to evolving context. 

Optimizing impression-level decisions meant simultaneously evaluating signals like geolocation, device type, browser environment, and content relevance. These variables enabled the platform to determine the most appropriate ad for each moment, improving personalization, engagement, and monetization outcomes.

By consolidating decision-making into a single model-driven engine, we improved consistency, minimized latency, and scaled personalization across a broad range of ad surfaces.

Architecture Overview: Intelligent Infrastructure by Design

To serve millions of users concurrently, we built a distributed infrastructure that processed real-time session data, ad inventory, and contextual signals. A distributed system integrates core distributed system components, including data ingestion nodes, inference engines, and observability modules, to ensure both high throughput and low latency.

Inference models, trained on billions of historical data points, scored and ranked ads based on contextual fit, pacing, and predicted performance. Decisions were executed in milliseconds and routed through streamlined APIs for instant delivery.

Freshness was a key principle. Research indicates that the timing of targeted ads significantly influences consumers’ browsing intentions (Li et al., 2023). Updating behavioral data in near real-time ensured responsiveness as audience intent shifted.

Traffic was distributed across regions to balance load, and observability tools were embedded throughout. These systems tracked decision quality and surfaced anomalies, enabling real-time tuning without degrading performance.

Addressing Complex Technical Challenges

Operating at this scale required concurrent solutions to multiple interdependent challenges.

Latency Control

Ad-load balancing, particularly in high-scale environments, requires careful optimization between user satisfaction and revenue outcomes (Sagtani et al., 2023). We maintained strict response windows to preserve user experience and auction fairness. Real-time monitoring and intelligent traffic routing kept latency within thresholds, even during peak traffic events.

Sparse Data Handling

To address situations where user signals were incomplete or delayed, we introduced fallback paths that could still return contextually appropriate ads. This was especially important in cold-start conditions, where defaulting to generic placements would reduce effectiveness.

Brand Safety and Compliance

To protect users and advertisers alike, the system applied creative labeling mechanisms that screened ads for contextual relevance and compliance. This allowed potentially unsuitable content to be automatically flagged and excluded before delivery.

Inventory Forecasting

Predictive models based on historical usage helped forecast available impressions and maintain pacing against advertiser goals. This reduced the risk of underdelivery and minimized the need for reactive adjustments during critical periods.

Resilience

The platform was built with failover capabilities to reroute traffic in case of localized outages or degraded performance. This ensured stable service levels even when individual components were under strain.

Organizational Execution: Enabling Scale Through Structured Coordination

Building the system is only half the challenge. The other half is scaling the organization around it. Delivering a unified ad decisioning engine required aligning over 50 engineering teams across more than 10 VP-level organizations, each with their own roadmaps, priorities, and ownership boundaries.

To coordinate this effort, a dedicated technical program management (TPM) function was established. This group managed roadmap alignment, drove adoption of shared standards, and handled integration timelines across multiple surfaces. Planning cycles were structured quarterly, translating organizational goals into actionable milestones.

We implemented a model of single-threaded ownership for each major platform component. Assigning clear leads to key initiatives prevents the common pitfall of fragmented accountability, which accelerates issue resolution and reduces ambiguity (Thizy, 2022). By building consistent decision-making channels across all stakeholders, we enabled focused execution across a distributed development structure.

Tangible Results: System Performance and Business Impact

The system consistently supported over 10 million concurrent users with stable average response times, including during high-traffic streaming events. This consistency allowed the business to capitalize on peak periods without compromising user experience.

One major outcome was an increase in audience addressability—from 60% to 95% of eligible users—enabled by advances in targeting models and identity resolution. This directly expanded the number of impressions that could be matched to relevant ads, increasing monetizable inventory.

Revenue impact followed. Across multiple video ad surfaces, the system contributed to a multi-billion-dollar uplift in annual monetization. Operational overhead decreased as well; automated fallback and failover mechanisms removed the need for manual intervention during high-stakes events.

Finally, modular architecture proved essential for agility. Teams were able to test and deploy new models and auction strategies quickly, reducing time-to-market across geographies and integrated media platforms.

Conclusion: Orchestrating Innovation at Scale

Scaling a real-time ad platform to this level demanded more than engineering. It required orchestrating technology, teams, and timelines around a unified vision. The result was a platform that drove revenue, improved reliability, and enabled organizational agility. Its modular design and deep observability ensured it evolved in step with user behavior and business needs.

More than any individual breakthrough, it was the coordination of technologies, teams, and timelines that made the system durable. It remains one of the most complex and rewarding projects I’ve led, not only for what it achieved, but for how it came together.

About the Author:

Paras Choudhary is a senior technology leader specializing in large-scale AI and AdTech platforms. With over a decade of experience driving complex engineering programs, he currently leads technical program management for AI-enabled advertising systems at a major FAANG company. Paras has successfully overseen initiatives spanning audience targeting, monetization, and infrastructure optimization, impacting billions in annual revenue.