Scaling to 10 Million Messages: Our Infrastructure Journey

The Growth Challenge

When we launched Agent Rush, we handled a few thousand messages per day. Now we process over 300,000 daily. Here's how we scaled without breaking.

Architecture Evolution

Phase 1: The Monolith (0-100K messages/month)

We started simple:

This worked great for our first customers. Simple to debug, easy to deploy.

Phase 2: Service Split (100K-1M messages/month)

As load increased, we split into services:

Each service could scale independently.

Phase 3: Event-Driven (1M-10M messages/month)

At scale, synchronous processing hit limits. We moved to events:

Message In → Queue → Process → Queue → Deliver

Benefits:

Key Technical Decisions

1. Database Strategy

We use a hybrid approach:

2. AI Inference

Running LLMs at scale is expensive. Our approach:

3. Multi-Region Deployment

Indian users expect low latency. We run in:

Lessons Learned

1. Observability is Non-Negotiable

We track:

You can't fix what you can't see.

2. Graceful Degradation

When systems strain, we:

3. Chaos Engineering

We regularly break things on purpose:

Better to find weaknesses in testing than production.

Current Numbers

Scale doesn't require a massive team—it requires smart architecture.