How PhonePe Handles 100 Billion Daily Events: A $500M+ Business Built on Rock-Solid Data Infrastructure

The Scale That Powers Digital India

When you're processing payments for over 400 million users and handling transactions worth billions of dollars monthly, there's no room for data infrastructure failure. PhonePe, India's leading digital payments platform, has built their entire business on the ability to capture, process, and act on data in real-time. The numbers are staggering: 100 billion events per day flowing through their systems, each one representing a critical piece of information that could impact user experience, fraud detection, or business intelligence.

According to the PhonePe engineering team, this massive data flow isn't just a technical achievement, it's the foundation of a business that processes over $100 billion in annual payment volume. Every user interaction, transaction record, and system metric must be captured and processed without fail. The cost of losing even a fraction of this data could mean missed fraud alerts, degraded user experiences, or incorrect business decisions worth millions.

But here's what makes their story fascinating: they didn't achieve this scale by simply throwing more hardware at the problem. Instead, they engineered a sophisticated Apache Kafka-based architecture that balances resilience, performance, and cost-effectiveness in ways that most organizations struggle to achieve.

The Business Challenge Behind the Technical Problem

Managing 100 billion daily events isn't just about storage, it's about enabling real-time decision making across every aspect of a digital payments business. PhonePe's engineering team faced a challenge that many high-growth fintech companies encounter: how do you scale data infrastructure without creating operational bottlenecks or budget-breaking costs?

The stakes were particularly high because of the nature of their business. In digital payments, data latency can mean the difference between catching fraud in real-time and losing money. User experience depends on instant insights into transaction patterns. Business intelligence requires immediate access to payment trends and user behavior patterns.

Traditional data management approaches simply couldn't handle this scale. The team realized they needed an architecture that could grow exponentially while maintaining the reliability standards required for financial services. More importantly, they needed a solution that wouldn't require every development team to become Kafka experts just to send data effectively.

The financial implications were significant. Without proper data infrastructure, PhonePe risked losing competitive advantage in a market where milliseconds matter and user trust is everything. They needed a solution that could scale with their business growth while keeping operational complexity manageable.

The Strategic Decision: Separation and Simplification

The PhonePe team made a crucial architectural decision that many organizations overlook: they separated the concerns of data generation from data expertise. Rather than requiring every development team to understand the intricacies of Kafka, they built a two-step architecture that abstracts complexity while maintaining performance.

Their approach centers on a client library that handles the immediate data capture, writing events to local storage using BigQueue technology. Simultaneously, a separate "Ingestor" process reads these stored events and transmits them to their IngestionService, which then channels everything into Kafka. This separation might seem like added complexity, but it solved multiple business problems simultaneously.

The decision also addressed a critical scaling challenge: as PhonePe grew, they couldn't afford to have dozens of development teams each implementing their own Kafka integration strategies. Standardization became essential for both performance and cost management. By centralizing Kafka expertise within their platform team while providing simple APIs to development teams, they achieved the best of both worlds.

Most importantly, this architecture choice positioned them for the kind of exponential growth they were experiencing. The separation of write and read workloads, implemented through dual Kafka clusters per data center, meant they could scale different aspects of their system independently based on actual usage patterns rather than theoretical capacity needs.

Engineering Excellence: The Architecture That Scales

Intelligent Load Separation

The PhonePe engineering team implemented what they call a "dual Kafka cluster strategy", separate clusters for write and read operations within each data center. This wasn't just a technical nicety; it was a business-critical decision that protected their core transaction processing from the potential impacts of analytics and reporting workloads.

This separation means that when their business intelligence teams run complex queries on transaction data, it doesn't affect the real-time processing of new payments. For a company processing thousands of transactions per second, this isolation is worth millions in prevented downtime and maintained user experience.

Smart Capacity Management

Rather than relying on gut feelings or simple CPU monitoring, PhonePe developed a comprehensive capacity assessment system that monitors multiple resource metrics simultaneously:

• Disk Capacity Utilization: Ensuring sufficient storage for continuous data flow
• Disk I/O Performance: Preventing bottlenecks in data read/write operations
• CPU Utilization: Maintaining processing headroom for peak loads
• Network Utilization: Avoiding data transmission constraints

Their capacity metric calculates the maximum percentage across all resources, providing a single number that reveals the most significant constraint at any moment. This approach enabled them to make data-driven scaling decisions and optimize hardware utilization effectively.

Adaptive Hardware Optimization

When initial deployments showed disk I/O as the primary bottleneck, the team didn't just add more servers. Instead, they strategically increased disk count per node from 2 to 4 to 8, significantly improving performance without proportional cost increases. This kind of targeted optimization is what separates cost-effective scaling from expensive over-provisioning.

Implementation Insights: Balancing Speed and Reliability

The PhonePe team's implementation reveals sophisticated thinking about trade-offs. Their architecture achieves 99-percentile latency under 3 seconds for standard analytics workloads, while maintaining sub-100 millisecond performance for critical, time-sensitive applications through direct IngestionService access.

This dual-speed approach reflects real business needs. Most data can tolerate slight delays in exchange for guaranteed delivery and simplified operations. However, fraud detection and real-time user experience features need immediate data access. By providing both options through the same infrastructure, they avoid the cost and complexity of maintaining separate systems.

The file-based intermediate storage approach adds resilience without sacrificing performance for most use cases. During network issues or Kafka maintenance, data continues flowing into local storage, preventing the data loss that could impact business intelligence or compliance reporting.

Their client library handles operational complexities like file rotation, location tracking, and cleanup automatically. Development teams simply call APIs without worrying about underlying Kafka configuration, partition management, or error handling strategies.

Results: Business Impact of Technical Excellence

The business results of PhonePe's Kafka architecture are impressive across multiple dimensions:

Operational Efficiency

• 100 billion events per day processed reliably without manual intervention
• 95% of use cases handled through simplified APIs, reducing development team overhead
• Automatic scaling decisions based on real-time capacity metrics rather than manual monitoring

Cost Management Breakthrough

Perhaps most importantly, PhonePe implemented a cost attribution system that assigns Kafka usage costs to individual development teams based on bytes sent. This approach, borrowed from cloud provider strategies, transformed their relationship with data growth.

Teams became accountable for their data usage, leading to natural optimization as unnecessary data streams were identified and eliminated. The shift from "unlimited free resource" to "attributed cost center" drove responsible usage without requiring top-down mandates or complex approval processes.

Performance at Scale

• Sub-3 second latency for 99% of analytics workloads
• Sub-100 millisecond performance available for critical applications
• Automatic failover and recovery during infrastructure issues
• Linear scaling capability as business volume continues growing exponentially

The architecture successfully handles PhonePe's continued growth without requiring fundamental redesign, proving its effectiveness for long-term business scalability.

Strategic Lessons for Scaling Data Infrastructure

PhonePe's experience offers valuable insights for any organization dealing with large-scale data challenges:

Separate Operational Concerns Early

Don't wait until you have dozens of teams implementing their own data solutions. Centralizing expertise while providing simple interfaces scales much better than distributed complexity.

Design for Your Actual Usage Patterns

The write/read cluster separation addresses PhonePe's real workload characteristics rather than theoretical best practices. Understanding your specific data flow patterns is crucial for effective architecture decisions.

Implement Cost Visibility Before You Need It

PhonePe's cost attribution system creates natural incentives for efficient data usage. Implementing such systems during growth phases is much easier than trying to optimize usage after costs become problematic.

Plan for Multiple Performance Tiers

Not all data needs the same performance characteristics. PhonePe's dual-speed approach (standard vs. real-time) provides flexibility without over-engineering every use case.

Monitor What Actually Constrains You

Their multi-metric capacity assessment system identifies real bottlenecks rather than relying on traditional CPU/memory monitoring. This approach enables more targeted and cost-effective scaling decisions.

The Future of Event-Driven Architecture

PhonePe's success with 100 billion daily events demonstrates that event-driven architecture can scale to support multi-billion dollar businesses when implemented thoughtfully. Their approach of separating concerns, implementing smart cost controls, and designing for actual usage patterns provides a roadmap for other organizations facing similar challenges.

As digital businesses continue growing exponentially, the lessons from PhonePe's implementation become increasingly relevant. The combination of technical excellence and business-focused design thinking they demonstrate is exactly what's needed to build sustainable, scalable data infrastructure.