Three months ago, we rebuilt our entire inference pipeline. Again. Not because we wanted to, but because our monolithic approach couldn't handle the load. One client was processing 50,000 documents daily, another needed real-time predictions for their trading algorithms. The old architecture buckled under pressure. It's embarrassing to admit, but we learned something valuable from that failure.
Architecture patterns aren't just academic concepts. They're the difference between a system that gracefully handles 10x growth and one that crashes at 2am on a Saturday. We've built AI systems for fintech companies processing millions of transactions, healthcare platforms managing patient data, and manufacturing systems optimizing supply chains. The patterns that work aren't always the ones you read about in tech blogs.
Event-Driven Architecture: The Backbone of AI Systems
Event-driven architecture isn't trendy anymore. It's just necessary. When you're building AI systems that need to respond to real-world events, synchronous calls become a bottleneck fast. We learned this the hard way with a healthcare client who needed to process insurance claims in real-time. Their old system would lock up for 30 seconds every time our ML model ran inference. Users would click submit and wait. And wait.
Now we use events for everything. A claim gets submitted, an event fires. Our ML service picks it up asynchronously, processes it, and publishes the result. The user sees immediate feedback while the heavy lifting happens in the background. We've reduced response times from 30 seconds to under 200ms. The client processes 40% more claims with the same infrastructure.
But here's what most tutorials don't tell you about event-driven systems. Dead letter queues are your best friend. When your ML model throws an exception at 3am, you want those failed events recoverable, not lost forever. We use AWS SQS with dead letter queues and exponential backoff. It's boring technology, but it works. Our error recovery rate went from 60% to 95% just by implementing proper queue management.
Microservices: When to Split and When to Stay Together
Everyone talks about microservices like they're magic. They're not. They're a tool for managing complexity at scale, but they create their own problems. We have one client running 30 microservices for their e-commerce platform. Another client runs everything in three services and scales just fine. The difference isn't the architecture pattern, it's understanding when to use it.
Our rule is simple. If you can't deploy part of your system without touching other parts, you need better service boundaries. We split services based on business domains, not technical layers. The recommendation engine is separate from the inventory system because they change for different reasons. The user authentication service handles login for everything because it rarely changes and needs to be rock solid.
- Split on business boundaries, not technical ones - your ML pipeline shouldn't break when marketing updates the email templates
- Keep databases separate per service - shared databases defeat the whole purpose of service independence
- Use API gateways for external communication - internal service-to-service calls should be direct to avoid latency
- Monitor service dependencies religiously - a slow authentication service kills everything downstream
The hardest part about microservices isn't building them. It's monitoring them. We use distributed tracing with Jaeger to follow requests across service boundaries. When a user reports slow performance, we can trace their request through every service and find the bottleneck. Last week we discovered a 2-second delay in our recommendation service was caused by a misconfigured database connection pool in the user preferences service. Without distributed tracing, that would've taken days to find.
CQRS and Event Sourcing: Overkill or Essential?
Command Query Responsibility Segregation sounds fancy. In practice, it means separate your reads from your writes. Most systems don't need this complexity, but AI systems often do. When you're training models, you need to query historical data in ways you never anticipated. When you're serving predictions, you need fast reads optimized for real-time performance.
We implemented CQRS for a manufacturing client who needed to track every sensor reading for regulatory compliance while also providing real-time dashboards. The write side stores raw sensor data optimized for ingestion. The read side maintains pre-aggregated views optimized for queries. Engineers can slice data by machine, time period, or sensor type without impacting the real-time ingestion pipeline.
Event sourcing takes this further by storing events instead of current state. It's overkill for most applications, but invaluable when you need to understand how your data changed over time. Our fintech clients use it for audit trails. Every transaction is an immutable event. You can reconstruct account balances at any point in history. When regulators ask questions, you have answers.
The API Gateway Pattern: Traffic Control for Distributed Systems
API gateways get a bad rap because they can become bottlenecks. But they're essential for managing complexity in distributed systems. We use them as traffic controllers, not just proxies. Rate limiting, authentication, request routing, circuit breaking - all the cross-cutting concerns that every service needs but shouldn't implement themselves.
Our gateway handles authentication once and passes verified user context to downstream services. It implements circuit breakers that fail fast when services are unhealthy. It routes traffic based on request patterns - ML inference requests go to GPU instances, simple queries go to CPU-optimized services. The result is better resource utilization and more predictable performance.
“Don't go out stealing data. You're not going to be a successful company if you operate in bad faith with your users.”
We also use the gateway for request transformation. External APIs expect different data formats than our internal services. The gateway translates between them, keeping our internal services focused on business logic instead of API compatibility. When we need to version our APIs, the gateway handles routing to the appropriate service version based on request headers.
Database Per Service: The Data Consistency Challenge
Database per service is one of those patterns that sounds great in theory and creates headaches in practice. Each service owns its data, which gives you independence and scalability. But what happens when you need to query across service boundaries? What about transactions that span multiple services? These aren't theoretical problems. They're daily realities.
We solve cross-service queries with materialized views. When the inventory service needs customer data for recommendations, it doesn't call the customer service in real-time. Instead, it maintains a local copy of the customer data it needs, updated through events. This adds complexity but eliminates network calls and improves resilience. If the customer service goes down, recommendations keep working.
For transactions across services, we use the Saga pattern. Instead of distributed transactions, we model business processes as sequences of local transactions with compensating actions. When a customer places an order, we reserve inventory, charge the payment method, and update the order status. If any step fails, we run compensation logic to undo previous steps. It's more code, but it's more resilient than distributed transactions.
What This Means for Your Next Architecture Decision
Architecture patterns aren't fashion choices. They're tools for solving specific problems. Start with the simplest thing that works and add complexity only when you need it. Most systems can start as modular monoliths and split into services when team boundaries or scaling requirements demand it. Don't build for theoretical problems you might never have.
Focus on observable systems from day one. You can't debug what you can't see. Invest in logging, metrics, and tracing before you need them. When things break at scale, and they will, you'll need data to understand why. The patterns we use work because we can measure their impact and adjust when they don't.
