Solutions
AWS Development
Design and build applications on AWS with an account structure, managed-service choices, and cost shape decided deliberately rather than inherited from whichever service was reached for first.
Inside the delivery
Compare managed services against runtime needs, data behavior, team skills and cost assumptions. The flow below shows the main delivery stages and the evidence produced at each step.
Compare managed services against runtime needs, data behavior, team skills and cost assumptions.
Output
AWS architecture and service decision record
Configure identity, network boundaries, encryption and environment separation for the application.
Output
Environment and access configuration
Build application services and integrations with repeatable releases and dependency handling.
Output
Application code and deployment definitions
Test failure paths and instrument logs, alarms and consumption before handover.
Output
Operational checks and AWS runbook
Controls across the workflow
Before you commit
Overview
AWS gives you several defensible ways to build the same system, and they differ mostly in operational burden and monthly cost rather than in whether they work. The value is in choosing deliberately: which workloads justify managed services, where serverless stops being cheaper, and how the account and network structure will hold when a second team needs to deploy.
Capabilities
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Decide the compute, storage, and data services against the workload, the team, and the budget rather than against a reference diagram.
Set up the account, network, and identity structure that everything else inherits, before it becomes expensive to change.
Build and integrate the application itself, with deployment automation treated as part of the deliverable.
Leave the workload observable and the spend attributable, so both can be managed without guesswork.
Integrations
Tools are chosen around your existing systems, access requirements and operating constraints.
Frequently asked questions
Use managed services when their constraints and costs fit the workload. Portability, specialist runtime requirements or existing operations may justify containers or other deployment choices.
It depends on traffic shape and team capacity more than on preference. Serverless usually wins for spiky or low-volume workloads and for teams without operational depth; sustained high throughput often crosses over into containers being cheaper and more predictable. We model both against your expected load.
Yes. We assess the current account structure, permissions, and network setup first, and are explicit about what can be built on as-is versus what should be corrected before more workloads land on it.
By modeling cost during service selection, tagging for attribution from the start, and setting up the reporting that shows which workload drives which line. Cost surprises are usually architecture decisions that nobody priced.
Yes, and many organizations run both. What matters is deciding deliberately which workloads live where and avoiding accidental duplication of the same capability in two clouds with two operating models.
Yes, though migration and greenfield development are scoped differently. Migration starts with assessing what the current system depends on and whether it should be rehosted, refactored, or partly replaced.
AWS account structure, application requirements, identity policies, data residency constraints and expected usage patterns.
The agreed delivery includes operational checks and AWS runbook. Test failure paths and instrument logs, alarms and consumption before handover.
We review the available inputs before estimating: AWS account structure, application requirements, identity policies, data residency constraints and expected usage patterns. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Start a conversation
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for AWS Development.