Cloud Anti-Patterns: Real Failures and How to Avoid Them

Introduction

Most cloud outages trace back to predictable anti-patterns: brittle assumptions, insufficient isolation, or misaligned scaling strategies. This post highlights common failures seen in production systems and provides practical mitigations.

Anti-Pattern 1: Single-AZ Dependencies

Designing a system that depends on a single availability zone creates a single point of failure. Even managed services can be affected by AZ-level issues.

Mitigation:

• Use multi-AZ databases and replicas.
• Distribute workloads across subnets in multiple AZs.
• Validate failover during game days.

Anti-Pattern 2: Unbounded Concurrency

Naively parallelizing every request can overwhelm downstream systems.

async function fetchAllOrders(orderIds) {
  const responses = await Promise.all(
    orderIds.map((id) => fetch(`https://orders.internal/${id}`))
  );
  return Promise.all(responses.map((res) => res.json()));
}

This pattern can trigger rate limits, exhaust connection pools, and collapse the service under load.

Mitigation:

• Apply concurrency limits.
• Use bulk endpoints and batch requests.
• Introduce queue-based buffering for spikes.

Anti-Pattern 3: Shared Databases for All Tenants

A single database used for unrelated workloads creates contention and noisy neighbor issues.

Mitigation:

• Separate workloads by tier or tenant.
• Use read replicas for analytics or heavy reporting.
• Enforce resource isolation with separate clusters when necessary.

Anti-Pattern 4: Autoscaling Without Load Testing

Auto-scaling policies that were never tested during real load can lead to oscillation or slow response to spikes.

Mitigation:

• Perform load tests at realistic traffic patterns.
• Validate scale-up and scale-down behavior.
• Monitor scale events and adjust cooldowns.

Anti-Pattern 5: Secrets in Images or Configuration Files

Embedding secrets inside container images or repository files leads to accidental exposure and long-lived credentials.

Mitigation:

• Use a secrets manager with rotation.
• Shorten credential TTLs.
• Audit access logs regularly.

Anti-Pattern 6: Treating Cloud as a Data Center

Lifting and shifting legacy architectures without rethinking assumptions leads to cost and reliability issues.

Mitigation:

• Decompose monoliths into bounded services where appropriate.
• Use managed services for undifferentiated heavy lifting.
• Align architecture with cloud-native scaling patterns.

Conclusion

Cloud anti-patterns are rarely exotic. They are the result of teams skipping fundamentals or failing to validate assumptions under load. Use incident retrospectives and game days to identify these patterns early and design them out before they become outages.