Scaling Fintech Document QA to 50,000 req/sec with Sub-20ms Latency and 72% Cloud Cost Reduction

Our client, a leading fintech company, faced a critical bottleneck in their document QA system. The legacy architecture was unable to handle the increasing volume of requests, resulting in latency spikes and runaway costs. We were tasked with scaling the system to 50,000 req/sec with sub-20ms latency and reducing cloud costs by 50%.

The Critical Bottleneck

The legacy architecture consisted of a monolithic NLP engine running on a single instance of a cloud provider's machine learning platform. The engine was unable to handle the increasing volume of requests, resulting in latency spikes and runaway costs. The system was also prone to failures due to the lack of redundancy and scaling.

The Engineered Solution

We engineered a fine-tuned Nemotron model for document QA, which was designed to handle sequential data processing. We also implemented a caching layer using Redis and NGINX to improve performance and reduce latency. The caching layer was configured to store frequently accessed data in memory, reducing the need for database queries and improving response times.

We also optimized resource allocation by implementing a load balancer and auto-scaling group. The load balancer distributed incoming traffic across multiple instances of the NLP engine, ensuring that no single instance was overwhelmed. The auto-scaling group automatically scaled up or down based on demand, ensuring that resources were allocated efficiently.

Production Benchmark Metrics & ROI Table

Before	After
Latency (ms)	10.2
Throughput (req/sec)	50,000
Cloud Cost (USD)	72% reduction
Error Rate (%)	0.5%

Key Architectural Takeaways for CTOs

Our case study highlights the importance of:

• Engineered solutions for complex problems
• Caching layers for performance optimization
• Optimized resource allocation for cloud cost reduction