Observability as the Foundation for AWS Well-Architected Cloud Infrastructure

Part 3 of Sycomp's 4 Part Observability Blog Series

Introduction

Your team just finished implementing a comprehensive AIOps platform. Automated alerting, intelligent correlation, even self-healing capabilities. Everything should be perfect, right? But six months later, you're still dealing with the same fundamental issues—performance hasn't improved as expected, your AWS bill keeps surprising you, and your recent security audit revealed gaps you didn't know existed.

Here's what I've learned: automation and intelligence are powerful, but they can't fix architectural problems. In my previous posts, I discussed how observability provides the foundation for understanding your systems, and how AIOps uses that data to automate operations. But there's a critical piece missing; observability isn't just about operations. It's the foundation for well-architected systems.

The three pillars of observability (metrics, logs, and traces) directly enable the six pillars of the AWS Well-Architected Framework. More importantly, observability data should inform your architectural decisions from day one, not be bolted on after the fact.

The Observability-Architecture Connection

For years, the standard approach has been to design the architecture, build it, deploy it, then figure out how to monitor it. This creates blind spots, inefficiencies, and architectural decisions that look good on paper but fail in practice.

The modern approach flips this around. Observability informs architecture through a continuous feedback loop: collect comprehensive data, observe how your system behaves, use those insights to make informed architectural decisions, implement improvements, and repeat. This works because of one principle: you can't optimize what you can't measure.

The AWS Well-Architected Framework consists of six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability. You can't achieve excellence in any of these without visibility into how your systems actually work.

How Observability Enables Each Well-Architected Pillar

Operational Excellence: Operations as Code Requires Observable Operations

Metrics tell you if your operations are healthy—deployment success rates, mean time to recovery (MTTR), change failure rates. Logs provide detailed records for post-incident analysis and help identify patterns. Traces show where application workflows are inefficient.

For example, I once worked with a team who was manually scaling their production environment every Monday morning before the weekly traffic spike, taking two engineers 30 minutes each week. Using CloudWatch metrics and X-Ray traces, we identified exactly what they were doing, which resources needed scaling, and by how much. We automated the entire process. Now it happens automatically at 6 AM every Monday, and those engineers focus on more valuable work.

Security: You Can't Secure What You Can't See

Metrics help detect anomalies in access patterns. For example, 10,000 API calls in an hour when normal is 100 signals problems. Logs provide your audit trail; CloudTrail logs every API call made in your account. Traces reveal potential attack vectors by showing unusual request paths through your system.

A client's observability platform detected an unusual pattern in CloudTrail logs, someone methodically calling describe* and list* APIs across all services. Individually, each call looked innocent. But the pattern revealed a threat actor mapping their entire infrastructure. Because they had comprehensive logging and intelligent correlation, they detected and shut down the compromised credentials before any damage occurred.

Reliability: Preventing Failure Requires Predicting Failure

Metrics let you see problems before they become failures. In this example, RDS storage at 85% of capacity and growing means you should add capacity before hitting 100% and causing an outage. Logs help identify failure patterns. Traces show exactly how failures cascade through distributed systems, letting you build better circuit breakers and reduce dependencies.

Performance Efficiency: Right-Sizing Requires Actual Utilization Data

Metrics show actual utilization. Logs reveal slow transactions and bottlenecks. Traces display end-to-end latency across all services, showing exactly where to focus optimization efforts.

A client provisioned Lambda functions with 3GB of memory because "more is better." After implementing proper observability, we discovered most functions completed in under 100ms using less than 256 MB. We helped them to right-size, which improved performance (less cold start time) and cut Lambda costs by about 75%.

Cost Optimization: FinOps is Observability Applied to Spending

This is an opportunity to leverage observability which often gets overlooked but, Sycomp chooses to lean into. Metrics track cost per transaction, cost per user, cost per service. But you need to correlate cost metrics with usage metrics. Logs validate cost allocation tagging. Traces reveal the cost of specific features—some API calls might cost $0.001 while others cost $0.10 due to inefficient queries.

For example, a client spending $50,000/month on AWS wants to optimize. We can implemented comprehensive cost observability using AWS native tooling like CloudWatch metrics or 3^rd-party partner tools to track hourly spend, custom metrics correlating cost with business KPIs, Cost and Usage Reports ingested into their observability platform, and distributed tracing enhanced with cost context.

This data can reveal things such as EC2 instances running idle, dev/test environments running 24/7 but only used during business hours, API endpoint traffic inefficiently routing traffic to expensive resources, or Reserved Instances/Savings Plans which are underutilized. We can help right-size instances, implement automated shutdown schedules, optimized expensive queries, and restructured their commitment-based discount portfolio, reducing their monthly spend in meaningful was while improving system performance.

Sustainability: Green Cloud Requires Visibility into Waste

Utilization metrics aren't just about cost; they're about energy efficiency. A server at 15% utilization wastes electricity. Logs reveal usage patterns that affect sustainability, batch jobs running during peak grid hours when power relies on fossil fuels. By right-sizing based on observability data, you reduce both costs and environmental impact.

Building an Observability-First Architecture

During the design phase, define observability requirements alongside functional requirements. Identify key metrics, critical log events, and trace points before writing code. For an order processing system: metrics like orders per minute and processing time, logs for order received through completion, traces showing full request paths, and baselines for normal behavior.

Make instrumentation part of your definition of done—appropriate metrics emission, structured logging with correlation IDs, and distributed tracing instrumentation. When using Infrastructure as Code, include monitoring resources alongside application resources. Don't deploy a database without also deploying the CloudWatch alarms that monitor it.

Once running, use observability data for continuous improvement. Conduct regular Well-Architected Reviews informed by actual data. If observability shows a service is a bottleneck, that's architectural feedback. Every week, review cost and utilization metrics together—where are you paying for unused capacity?

The Well-Architected Review: Powered by Observability

Traditional Well-Architected Reviews are somewhat subjective—you answer questions based on what you think is happening. With comprehensive observability, reviews become data-driven. When asked "How do you monitor workload resources?" you can show your monitoring dashboard.

At Sycomp, we integrate observability data directly into our Well-Architected Service engagements. We don't just ask if you're following best practices, we show you the metrics that prove it. This data-driven approach helps prioritize remediation based on actual impact, not theoretical risk.

Preparing for AI Agents: Better Observability Required

AIOps from my previous blog was just the beginning. The next frontier is AI agents making autonomous decisions without human approval for each action. If you're uncomfortable with AI agents making decisions about your infrastructure, it's probably because you can't see what they're doing or understand why. That's an observability problem.

AI agents need comprehensive observability for explainability (why did it make that decision?), accountability (who's responsible?), and safety (how do we prevent catastrophic mistakes?). The irony is that AI agents need better observability than human operators—humans can explain their reasoning, but AI agents need to show their work through comprehensive logging, metrics, and tracing.

Building well-architected, highly observable infrastructure now is critical. When you're ready to implement AI agents, you'll need observability as your foundation. Without it, you're giving an unexplainable black box control over your infrastructure. In my next post, I'll show you exactly how observability makes autonomous AI safe, accountable, and valuable.

Conclusion

Observability isn't just monitoring tools; it's the foundation that enables excellence across all six. Without observability, you're making architectural decisions based on assumptions. With it, you're making decisions based on evidence.

The organizations that succeed in the cloud are those that can see what's happening, understand what it means, and act on those insights. Everything else—automation, optimization, innovation—flows from that visibility.

How Sycomp Can Help

Sycomp Intelligent Observability Plus: Comprehensive professional and managed services that take the complexity out of building an observability stack while optimizing costs. We help you instrument applications, set up monitoring infrastructure, and create dashboards and alerts that matter.

AWS Well-Architected Service: We conduct data-driven Well-Architected Reviews using your observability data. Our output includes detailed reports and actionable plans to optimize costs, improve performance, enhance security, and mitigate risks.

Managed FinOps Services: We help you implement the cost observability practices described here, from setting up cost allocation tags to continuous optimization recommendations, providing FinOps expertise and tooling (including Cloudability) to control spending while maintaining performance.