When you're processing hundreds of academic documents through AI pipelines daily, your queue system becomes the backbone of everything. One bottleneck in message handling can cascade through OCR processing, data normalization, grade conversions, and multiple LLM calls—turning a smooth workflow into a frustrating user experience.

The Problem with AI Document Processing

Our marksheet conversion system for US universities presented a classic distributed systems challenge. Each uploaded document needed to flow through multiple stages: OCR extraction, data normalization, grade scale conversion, and several LLM API calls for validation and formatting. Each stage had different processing times, failure rates, and resource requirements.

A simple queue wouldn't cut it. We needed something that could handle high throughput, provide reliability guarantees, and manage complex workflows where later stages might need to reference earlier processing results.

Why We Chose Kafka

Apache Kafka emerged as the perfect solution for our AI pipeline architecture. Unlike traditional message queues, Kafka's distributed streaming platform could handle our complex requirements:

Kafka's producer-consumer model fit naturally with our pipeline stages. Each processing step could consume from one topic and produce to another, creating a clean separation of concerns. The system could process multiple documents simultaneously while maintaining order and ensuring no data loss during the heavy LLM processing phases.

The real game-changer was Kafka's ability to handle backpressure gracefully. When LLM API calls slowed down or failed, Kafka maintained message persistence without blocking earlier pipeline stages. This resilience proved crucial during peak processing times when universities uploaded batches of student records.

Taking Control with Self-Hosted Infrastructure

Initially, we used cloud-hosted Kafka services, but the costs quickly escalated with our processing volumes. We made the strategic decision to self-host Kafka on our VPS infrastructure using Docker containers.

The migration gave us complete control over our message streaming infrastructure while dramatically reducing operational costs. Docker containerization made deployment and scaling straightforward, and we could optimize Kafka configurations specifically for our AI workload patterns.

The Practical Turn: Moving to BullMQ

Despite Kafka's excellent performance in production, we encountered development bottlenecks that were impacting our team's velocity. Running Kafka locally for development proved challenging for developers, and our Nest.js backend was experiencing performance issues with the Kafka integration complexity.

Sometimes the most architecturally sound solution isn't the most practical one for your team's workflow.

We transitioned to BullMQ, which maintained our high-throughput requirements while solving the development experience challenges. BullMQ provided the reliability and processing power we needed with significantly simpler local development setup and better integration with our Node.js stack.

The Takeaway

Building high-throughput queue systems for AI workloads requires balancing technical excellence with practical implementation. While Kafka proved exceptional for our distributed AI pipeline architecture, BullMQ ultimately won by solving real development pain points without sacrificing core functionality.

The best queue system isn't always the most sophisticated one—it's the one that serves both your technical requirements and your team's ability to build and maintain it effectively.