Why infrastructure technical debt appears after a cloud migration During an infrastructure migration to AWS, it is common to accept architectural trade-offs to meet the deadlines and budgets of the cutover. These decisions, documented or not, generate technical debt that accumulates quickly once teams move from project mode to operations mode. A server configured manually instead of automated, a database not optimized for the real load, security rules that are too permissive applied in a hurry, or a lack of high availability on critical components are all shortcuts that almost every migration accepts temporarily. The problem is that "temporary" often drags on, for lack of clear visibility into the scale of the corrections needed or for lack of internal resources to handle them. This technical debt undermines stability, increases security risks, limits the scalability of your platform, and makes day-to-day operations more expensive in manual maintenance. An architecture audit conducted shortly after the cutover generally reveals a list of 50 to 200 improvement points depending on the size of your infrastructure, of which 10 to 20 percent are critical.
Building an accurate inventory of your infrastructure technical debt The first step is to exhaustively identify the trade-offs and temporary configurations. Unlike software technical debt, which lives in the code, infrastructure technical debt is scattered across AWS resources (EC2, RDS, S3, networking, IAM), scripts, partial documentation, and above all in the memory of the engineers who ran the migration. A good inventory combines three sources. First, a post-migration architecture audit conducted by an independent third party (or by Stralya) that evaluates each key component against AWS Well-Architected Framework standards, identifies the gaps, and documents the associated risks (security, performance, cost, resilience). Second, a facilitated workshop with your infrastructure and security teams where you review the emergency decisions documented during the cutover, the workarounds deployed, the alerts not configured, and the deferred tasks. Third, an analysis of AWS costs compared to an optimized baseline, which often reveals resources that are poorly sized, poorly configured, or zombie. This inventory must classify each debt item by severity (critical affecting availability, high impacting performance or security, normal improving operational efficiency), by remediation effort (a few hours to several days), and by expected impact (cost reduction, performance improvement, closing a security hole). Only this complete and quantified view makes it possible to prioritize intelligently rather than focusing on the "most visible symptoms" and neglecting the silent risks.
Structuring a phased remediation plan Once the inventory is built, the challenge is not to discourage the organization or disrupt the teams with an endless list of corrections. An effective remediation plan divides the workload into successive phases over 6 to 18 months depending on the scale of the debt, with concrete and measurable objectives for each phase. Phase 0 (often very fast, 1 to 4 weeks) handles the critical items that present an immediate or security risk, for example a firewall rule that is too open, a database without backup, or a total lack of monitoring on a critical service. These corrections cannot wait. Phase 1 (1 to 3 months) generally aims to stabilize the infrastructure: automate the configurations that were manual, put high availability in place on components currently in single-point-of-failure, configure complete resource tagging for cost and governance, enable the missing logging and alerts. Phase 2 (the following 3 to 6 months) optimizes performance and costs: resize instances, configure auto-scaling, reorganize storage, bring backup strategies into compliance. The subsequent phases address finer optimization and gradual modernization. Each phase must be bounded by criteria defining a "resolved debt" (for example: 'every manual configuration migrated to Infrastructure as Code' or 'every critical service in multi-zone') and by a go/no-go decision to move to the next phase. This structure allows teams to progress without being overwhelmed, leaders to see results quickly, and resources to be allocated gradually rather than blowing the budget on a single large overhaul.
Executing the remediation without stopping production A frequent pitfall is to defer remediation until the next "maintenance window" or to believe that a large intervention is required. In reality, most corrections can be applied in production incrementally by following a few simple principles. First, each correction must first be tested in a non-production environment (staging or test) with a relevant set of test cases. Then, in production, deploy in stages: for example, improve a database configuration without interruption by creating a new optimized instance, gradually shifting traffic to it, then removing the old one once reliability is confirmed. For high-availability changes (moving from a single instance to a load balancer with several instances), use blue-green deployment or canary strategies to limit the risks. Each correction must be justified by the current risks and expected benefits, and backed by a change to the infrastructure code (Infrastructure as Code in Terraform, CloudFormation, or Pulumi) rather than by manual clicks, so that the improvements are durable and reproducible. Stralya's teams often support this kind of execution by co-piloting with internal teams: identify the correction, prototype it in non-prod, optimize it collectively, then facilitate its deployment in prod with the necessary safeguards. This co-construction mode avoids the "black box" effect and strengthens the internal capacity to manage the rest of the platform.
Tracking progress and adjusting the plan A remediation plan without regular tracking quickly bogs down, especially when teams are pulled into parallel projects or operational incidents. Putting a clear tracking dashboard in place is essential. This dashboard must show the number of debt items handled per phase, the status of each correction (pending, in progress, tested, deployed, verified), and the impact achieved in terms of cost savings, reduced risks, and stability gained. Monthly or quarterly reviews bringing together the technical team, the IT department, and the critical business lines make it possible to validate progress, adjust priorities if an unforeseen problem emerges, and maintain collective momentum. A good indicator of progress is also the reduction in the number of items detected as "unknown" or "poorly documented" in the infrastructure: the more this inventory shrinks, the more control over the platform increases. Finally, anticipate the long term by integrating into your operational governance process a discipline of not creating additional technical debt: for example, require that all new AWS resources be provisioned via Infrastructure as Code, that urgent configurations be marked as temporary with an expiration date, and that every incident be analyzed to prevent it from becoming future debt.