Most cloud architectures are designed for the vendor's diagram, then paid for by you.
Cloud development is building applications directly on cloud services like managed databases, queues, and serverless functions, instead of renting virtual machines and running everything yourself.
When you need this
Situations that bring teams to us
01
The proof of concept worked, the bill did not
A prototype on Lambda and DynamoDB cost $40 a month. At real traffic it costs $9,000 and nobody can explain why. We model cost per request before we commit to an architecture, and we show the spreadsheet.
02
You are building new and want to skip the ops team
A three-person product team should not be patching servers. We pick managed services that remove operational work, and we are explicit about which ones lock you in and what leaving would cost.
03
Your current setup is cloud-hosted but not cloud-built
Virtual machines running the same stack you had in the data center, at a higher price. That is hosting, not cloud development. We rework the parts where managed services actually beat what you run by hand.
What we deliver
Scoped deliverables with timeframes attached
- Architecture proposal with a modeled monthly cost at three traffic levels
- 1-2 weeks
- Infrastructure as code for every environment, reviewed like application code
- 1-2 weeks
- Application build with CI/CD pipeline and staging environment
- 8-16 weeks, scope-dependent
- Observability setup: logs, metrics, alerts, and a cost dashboard
- 1 week, runs in parallel
- Handover with runbooks and a recorded architecture walkthrough
- 1 week
How we work
The process, drawn honestly
01
Model the cost before the architecture
We estimate requests, storage, and data transfer at launch and at ten times launch. The architecture has to survive both numbers. If it only survives one, we redesign before writing code.
02
Build on managed services, with eyes open
Managed databases, queues, and auth save real payroll. Each one we pick comes with a written note on what migrating off it would take, so the lock-in is a decision rather than a surprise.
03
Ship behind a pipeline from week one
Every environment is created from code and every deploy goes through the same pipeline, including the first one. Console-clicked infrastructure is how staging and production quietly diverge.
Our take
Serverless is a billing model, not a religion
We use serverless functions for spiky, low-volume work, where paying per request beats paying for idle servers. Past a sustained few hundred requests per second, the same workload on containers usually costs 40-70% less and is easier to debug. Teams get this wrong in both directions: they run cron jobs on always-on VMs, and they run their hottest API path on per-invocation pricing. The honest trade-off is operational. Serverless removes patching and capacity planning but adds cold starts, execution limits, and a harder local development story. We pick per workload, not per project, and the cost model we hand over shows the crossover point for your traffic so the next team can re-check the decision when the numbers change.
FAQ
Asked before most engagements
For most workloads the capabilities are equivalent and the deciding factors are organizational: existing Microsoft licensing and identity favor Azure, startup credits and team familiarity often favor AWS. We work in both and will tell you in the first call which one your situation points to, with reasons you can check.
Engineering is the dominant cost: a typical product build runs $40k-150k over 3-5 months depending on scope. The cloud bill itself is usually $300-3,000 a month at launch. We put both numbers in the proposal, because vendors who only quote the build fee are deferring the bad news.
Budget alerts on day one, tagging on every resource so spend maps to features, and a cost dashboard reviewed in the same meeting as the sprint. Most creep comes from forgotten environments and unbounded log retention, both of which are policy problems we configure away.
Partly, and we are honest about the limits. Containers, Postgres, and standard queues move between clouds with modest effort. Proprietary services like DynamoDB or Cosmos DB do not. We document which category each component falls in, so portability is a budget line you chose, not a discovery.
Yes. AWS and Azure both operate UAE and Saudi regions, and several GCC regulators expect certain data classes to stay in-country. We confirm the residency requirement with your compliance contact before picking regions, since moving data later is expensive.
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