What AWS infrastructure benchmarking is and why to assess it AWS infrastructure benchmarking consists of comparing your current patterns, your architecture, your costs, and your performance against recognized standards, in particular the AWS Well-Architected framework and industry best practices. Unlike a generic audit, benchmarking positions your real state on a scale: you do not just get a list of problems, but a view of where you stand, how far you are from a defined maturity target, and what you would need to change to get closer to that target. For scale-ups and mid-market companies operating on AWS for a few years, this positioning is crucial. Your infrastructure has often evolved organically: each team put in place what it deemed necessary at its moment, without an overall vision. Costs have climbed, dependencies have proliferated, and it is unclear where to act first to get the best return on investment. Benchmarking answers this confusion by concretely showing you the gap between your current state and best practices, measured against transparent criteria. It is also an internal governance tool: it gives you quantified elements to justify optimization efforts to management or finance, rather than making vague requests for an overhaul. ## The AWS Well-Architected pillars: the benchmarking reference AWS defines five pillars in its Well-Architected framework, which serve as the basis for any serious benchmarking. The Operational Excellence pillar covers the ability to manage your infrastructure efficiently: automation, monitoring, centralized logs, and robust deployment processes. An infrastructure that scores well here has full observability, relevant alerts, and a mature CI/CD chain. The Security pillar covers data isolation, access control, encryption in transit and at rest, compliance, and secrets management. A benchmark shows whether your VPCs are correctly segmented, whether your IAM roles follow the principle of least privilege, or whether your sensitive data escapes all protection. The Reliability pillar measures your ability to recover quickly after a failure: redundancy, defined RTO/RPO, and regular disaster recovery tests. The Performance Efficiency pillar evaluates whether you use the appropriate instance types, whether your databases are well sized, and whether your content distributions are optimized. Finally, the Cost Optimization pillar examines whether your AWS bill matches your real consumption, whether you take advantage of pricing discounts (Reserved Instances, Spot), and whether you have removed dormant resources. During a benchmark, these five pillars are not scored in the abstract: each translates into concrete observations about your architecture. For example, under Performance Efficiency, you do not write "your score is 6/10"; you write "your EC2 instances are over-sized by about 30 percent, which costs an extra 15,000 euros/month, and this performance level can be reached with smaller instances tested under load." ## How to conduct a benchmark: data collection and evaluation A rigorous benchmark begins with a systematic collection of data about your current state. This includes your CloudTrail and VPC Flow Logs to understand traffic patterns and access activity, your CloudWatch metrics for CPU, memory, and disk, your AWS Config configurations for resources and their compliance, and your detailed billing reports from Cost Explorer. Many companies discover at this stage that they have not enabled CloudTrail globally or that Cost Explorer is not finely configured by tag or cost allocation, which means they have been flying blind from the start. The second phase consists of evaluating this data against best practices. This is not done through the manual evaluation of a consultant skimming your logs: it is a systematic process. For example, for the Security pillar, you check for the presence of a VPC per environment, the activation of VPC Flow Logs, the absence of Security Groups with open access (0.0.0.0/0), the presence of a WAF on your public ALBs, the activation of MFA on your root and privileged users, the existence of key rotation policies, and the absence of hardcoded secrets in your repositories or configurations. Each point is verified, each gap is noted with its business impact (example: "AWS secret in a public repository: critical risk of compromise"). For Cost Optimization, you calculate the potential savings by comparing your current consumption to what it would be with Reserved Instances, Savings Plans, or Spot Instances, depending on your load profile. A static on-demand instance costs 50 percent more than a Reserved Instance: if you have 10 running 24/7, that is a saving easy to quantify. The third phase synthesizes this data into a scorecard that shows your position on each pillar, relative to a reference model (good / acceptable / needs improvement). This scorecard is not an abstract grade: it is a quantified roadmap. ## Interpreting the results and prioritizing actions Once the benchmark is complete, you have a detailed understanding of your gaps against AWS best practices. However, not all gaps have the same urgency or the same cost to fix. Correct interpretation consists of prioritizing along two axes: business impact (how much this gap affects security, performance, cost, or reliability) and the effort to fix. A high-urgency, low-effort gap (for example, enabling AWS Config, which takes only a few clicks and gives you visibility across your entire posture) should be addressed immediately. A high-urgency but considerable-effort gap (for example, redesigning your network architecture to enforce proper segmentation) justifies a dedicated project with a roadmap. A low-urgency, high-effort gap can wait or be grouped with other changes. Let us take concrete examples. If your benchmark reveals that you run t2 instances in production for stable loads, you have two issues: first, t2 instances are burstable, they can slow down if you exceed your burst credit (performance impact); second, they cost much more than fixed-performance instances such as t3. Switching to t3 or dedicated instances, depending on your measured load, is a light effort (restart, performance verification) for a high saving. If your benchmark shows that your customer data is not encrypted at rest, you have a major compliance risk (GDPR, NIS2) and a medium effort to fix (enabling EBS encryption, gradual migration). This is a project to launch quickly. If your benchmark reveals that you have no formalized change management process for production deployments, that is a gap on the Operational Excellence pillar: fixing it means putting in place or improving your CI/CD, which can take weeks. This is a medium-term project to integrate into your roadmap, not an immediate urgency unless you have had recent deployment incidents. Prioritization must also account for your current maturity. A company that has never done a benchmark and discovers 50 gaps cannot address them in parallel: it must phase them. Often, addressing the level 1 gaps (fundamentals, such as network segmentation or encryption) opens the door to resolving the level 2 gaps (optimization). ## Concrete cases of applied benchmarking: from gap to action To ground benchmarking in reality, here are three concrete cases from real assessments. Case 1: a fintech scale-up that thought it had a secure infrastructure discovers via benchmarking that its EBS encryption keys are not managed by AWS KMS but by default keys, that its secrets are stored in cleartext in Git config files, and that its RDS instances do not have automatic backup enabled. Impact: risk of data loss and exposure of credentials. Effort: enable KMS (1 day), migrate secrets to Secrets Manager (2 days), configure RDS backups (4 hours). Cost: about 500 euros/month in KMS and Secrets Manager fees. Timeline: 4 days. Benefit: GDPR compliance and reduced breach risk. Case 2: a SaaS mid-market company bills its customers by usage but does not know how much each customer costs on AWS because it has no coherent tagging. Benchmarking reveals that without per-customer tagging, it is impossible to optimize per customer, detect leaks, or bill with certainty. Impact: margin loss on intensive customers, inability to optimize. Effort: implement a tagging strategy (1 week of planning + gradual rollout over 4 weeks). Cost: low (native AWS tools). Timeline: 4-5 weeks. Benefit: full visibility, accurate billing, per-customer optimization opportunities. Case 3: an industrial company migrating from on-premise discovers that it has provisioned 500 GB of EBS storage for databases but uses only 120 GB. Impact: overspending. Effort: reduce the EBS volumes and test performance (3 days). Cost: saving of about 400 euros/month. Timeline: 3 days. Benefit: quick saving, a foundation for other optimizations. These cases show that benchmarking is not an academic exercise: it creates a concrete, quantified roadmap for action. ## Recurring benchmarking: measuring progress A benchmark should not be a one-off activity. An assessment done once gives you a snapshot, but your infrastructure evolves: new resources are created, configurations drift, and AWS updates introduce new standards. An annual or semi-annual benchmark, depending on your pace of change, makes it possible to measure whether your transformation roadmap is really progressing and where the corrective efforts are paying off. For example, if your initial benchmark showed a Cost Optimization maturity score of 4/10 (many idle resources, no RI), and six months later a second benchmark shows 7/10 (you have enabled Reserved Instances, reduced dormant resources, optimized sizing), you have proof that your cost reduction strategy works. You can also quantify the benefit: if you have saved 100,000 euros annualized, the benchmark demonstrates the ROI. For teams with a busy agenda, these successive benchmarks should not be heavy manual audits every time. Many checks can be automated via AWS Config rules, AWS Trusted Advisor, or third-party tools (Prowler, CloudMapper) that regularly run the same checks and generate comparable reports. This makes it possible to track progress without heaviness. Finally, successive benchmarks also identify regressions: if a new deployment has created Security Groups that are too open or poorly tagged instances, the benchmark detects it and alerts before the problem grows.