It falls over at peak — or it stays up and costs a fortune
Two versions of the same problem: the architecture no longer matches the load it is carrying.
The problem
The first version: traffic arrives, the platform buckles, and adding hardware has stopped helping. Every peak is a risk, and the team has learned to dread the ones they can predict.
The second version: it stays up, but the monthly bill climbs while the customer count does not. Somewhere between the two, an architecture that was right three years ago has quietly stopped being right.
Both are the same underlying issue — the shape of the system no longer matches the shape of the load.
How we approach it
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Measure where it actually breaks, rather than where everyone assumes it does
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Design the target architecture against real traffic patterns
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Migrate data with no loss — the part that carries the genuine risk
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Cut over with minimal downtime, planned and rehearsed
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Right-size after the fact, once real load is visible instead of forecast
Where we have done this
100x Scale E-Commerce Migration
Migrated from Kinsta to AWS with auto-scaling architecture, increasing capacity from 300 to 30,000+ concurrent users.
Read the case study → HR TechServerless Cloud Migration
Migrated from EMR/Redshift to serverless AWS Glue and Athena, reducing costs and improving performance.
Read the case study →The services behind this
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