While many cloud and DevOps engineers can configure pipelines, provision infrastructure, and scale applications, the difference between an average setup and a high-performing, resilient, and cost-effective system is often found in the details—things that only experience will teach you.

In this blog, I’ll take you through some of the less obvious challenges that developers face today, along with the kinds of solutions that only come after years of making, recognizing, and correcting big mistakes.

1. Mistake: Overengineering Infrastructure

One of the biggest mistakes I’ve seen over the years is overengineering—adding complexity for complexity’s sake. Whether it’s microservices that are too granular, overly complex CI/CD pipelines, or cloud infrastructure with too many moving parts, overengineering can make systems harder to maintain and more prone to failure.

Solution: Keep It Simple, Scalable, and Modular

The key to solving overengineering lies in designing with simplicity in mind. This doesn’t mean cutting corners but focusing on simplicity, scalability, and modularity. Here’s how I approach this:

• Assess Service Granularity: In the rush to adopt microservices, many teams overdo it, creating too many microservices that end up causing more problems than they solve. The general rule I follow is this: if two services need to communicate frequently and are interdependent, they likely belong together. Group related functions together and don’t split microservices to the point where they introduce overhead and latency.
• Modular Pipelines: CI/CD pipelines can quickly become a tangled web of dependencies. Avoid this by breaking pipelines into modular, reusable components. For example, have separate pipelines for build, testing, and deployment stages, and reuse these modules across projects. A well-designed pipeline should be easily maintainable, modular, and reusable.
• Layered Infrastructure: When designing cloud infrastructure, I aim to abstract complexity into logical layers. For instance, use network segmentation (VPCs, subnets, security groups) to create isolated environments (prod, staging, dev) but keep the networking rules simple. The goal is to isolate risk without making the environment overly difficult to manage.

2. Mistake: Focusing Only on Uptime Instead of Resilience

Many teams focus solely on uptime as the ultimate measure of success. However, even systems with 99.999% uptime can suffer catastrophic failures if they aren’t resilient. The real-world failures I’ve seen aren’t because of downtime—they’re because systems aren’t built to recover gracefully when things go wrong.

Solution: Design for Failure, Implement Chaos Engineering

The biggest shift in thinking that helped me over the years is moving from preventing failure to designing for failure. No system is infallible, so the key is building resilience into your architecture.

Here’s how you do that:

• Use Circuit Breakers and Timeouts: In a microservices environment, cascading failures are a serious risk. I implement circuit breakers and timeouts in every service-to-service communication. This prevents one failing service from dragging down the entire system. Circuit breakers will automatically fail fast rather than waiting indefinitely, allowing the system to recover quickly.
• Redundancy Everywhere: Build redundancy into every layer—starting with your data. Use multi-region setups for critical applications and ensure that your databases are set up for automatic failover (e.g., AWS RDS Multi-AZ deployments). For stateless applications, run multiple instances across availability zones and use auto-scaling groups to ensure that you can handle traffic spikes even if instances fail.
• Chaos Engineering: One of the most eye-opening strategies is chaos engineering. I’ve used tools like Chaos Monkey from Netflix to simulate random failures in production. By introducing failure intentionally, I’ve been able to uncover weak points in architecture that would’ve otherwise gone unnoticed until disaster struck. Build resilience by planning for failure—create scenarios where a system fails and observe how it recovers.
• Automated Rollbacks: Ensure your deployment process can handle failure gracefully. Automate rollbacks within your CI/CD pipeline by implementing blue/green deployments or canary releases. This way, if something breaks during deployment, you can instantly roll back without affecting users.

3. Mistake: Underestimating the Impact of Latency in Distributed Systems

As systems grow and become more distributed, latency becomes an invisible killer. Even if a system looks healthy from a monitoring perspective, small latency issues can build up and cause poor performance, timeouts, or even cascading failures across microservices.

Solution: Implement Latency-Aware Design Patterns

Here’s how to address latency:

• Implement Caching Smartly: One of the easiest wins in combating latency is caching. But over the years, I’ve learned that caching is as much an art as a science. I use in-memory caches like Redis or Memcached for frequently accessed data but am careful about what to cache and where. Over-caching or poorly designed caches can introduce new bottlenecks. For instance, caching database queries makes sense, but caching dynamically generated content (like user-specific data) requires a more nuanced approach.
• Use Load Balancing Efficiently: Intelligent load balancing with tools like Nginx, HAProxy, or cloud-native options like AWS Elastic Load Balancing ensures that traffic is evenly distributed across services and regions. However, load balancing should also account for latency. I use latency-based routing to ensure that users are directed to the fastest available server. Tools like AWS Route 53 make this possible by automatically routing traffic based on response time from different regions.
• Use Asynchronous Communication: Latency in microservices can sometimes be due to blocking, synchronous communication between services. Where possible, I switch to asynchronous messaging using queues (e.g., AWS SQS, RabbitMQ) to decouple services. This allows services to operate independently, reducing the impact of latency.

4. Mistake: Relying on Vendor-Specific Cloud Features (Vendor Lock-In)

Cloud providers like AWS, Azure, and GCP offer a wide range of managed services that make development easier. But what I’ve learned over the years is that relying too heavily on vendor-specific features can create serious problems down the line—especially when migrating, scaling, or switching vendors.

Solution: Design with Portability in Mind, Leverage Open-Source Tools

To avoid vendor lock-in, I use the following strategies:

• Cloud-Agnostic Architectures: When designing cloud systems, I prefer to use tools and services that are cloud-agnostic. For example, instead of using AWS Lambda (which is vendor-specific), I’ll consider Kubernetes or Docker Swarm, which can run on any cloud provider. Similarly, for databases, using PostgreSQL or MySQL rather than a vendor-specific solution like DynamoDB gives me more flexibility if I need to migrate.
• Infrastructure as Code (IaC): One of the most important practices I follow is using Terraform or Pulumi for infrastructure management. These tools are cloud-agnostic and allow you to provision infrastructure on multiple providers without being tied to any single one. This gives you the freedom to switch providers or create multi-cloud architectures easily.
• API Gateways for Abstraction: I implement API gateways to abstract away direct dependencies on cloud-specific services. For example, by using Kong or Traefik, I can route requests to different backend services without exposing the underlying infrastructure. This way, if we need to change cloud providers, the API layer remains consistent, and the impact on the application is minimal.

5. Mistake: Overlooking Observability and Focusing Only on Monitoring

Many teams confuse monitoring with observability. Monitoring tells you when something is wrong, but observability gives you the tools to understand why. Without proper observability, you’re flying blind when complex issues arise, particularly in distributed systems.

Solution: Build an Observability Stack from Day One

Here’s how I ensure that observability is baked into every system I build:

• Full-Stack Tracing: I use distributed tracing with OpenTelemetry or Jaeger to trace every request as it flows through the system. This allows me to visualize the entire request path, see where bottlenecks occur, and identify services that are slowing down the entire system.
• Real-Time Metrics and Alerts: I set up dashboards using Grafana or Datadog that display real-time metrics for latency, error rates, request rates, and system resource usage. I also configure automated alerts for when critical thresholds are breached. The trick here is to avoid alert fatigue—set up alerts that are actionable and don’t overwhelm the team with noise.
• Log Aggregation and Analysis: Logging is another key component. I centralize all logs using tools like the ELK stack (Elasticsearch, Logstash, Kibana) or Splunk. The critical step that many miss is indexing logs properly—don’t just dump logs into a