MANAGING AWS COST OPTIMISATION IN THE GEN AI ERA
numbers only tell a partial story. Éric uses one client as an example.“ Their RDS costs had climbed to $ 12,000 a month,” he says.“ A service-level report would simply say‘ RDS: $ 12,000’, which doesn’ t help you understand what to do.”
Resource-level analysis revealed the full picture. By breaking it down at the resource level, Unicorne discovered that 30 % of the cost came from snapshots alone.“ Their policy was taking hourly backups and storing them for 90 days,” Éric shares. Hourly backups with 90-day retention for a development database was excessive. By moving to daily snapshots in development and more reasonable policies in production, the client saved more than $ 2,000 a month. Further investigation uncovered a synchronisation job transferring data unnecessarily between regions, saving another $ 900.“ In total, they reduced their bill by $ 3,800 each month: 32 % of the original cost,” Éric says.“ That kind of detail is invisible without resource-level visibility.”
How Stable operationalises this framework Stable emerged from the company’ s experience managing cloud infrastructure for clients.“ By supporting clients through managed services, we gained first-hand knowledge of how to optimise cloud environments under real constraints,” Éric says.“ Without that experience, Stable could never have existed. We don’ t sell theory, we deliver proven solutions tested with real companies, on real budgets and with real business stakes.”
The platform provides resourcelevel AWS cost analysis with prioritised recommendations based on implementation effort and business impact. Stable focuses specifically on AWS, with particular attention to serverless architectures and AI workloads.
Features include transparency over automation – no‘ auto-fix’ buttons that create infrastructure drift – as well
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