Platform selection
Compare AWS, Google Cloud, and Azure against actual requirements.
aiqorxBackend & data / Cloud platforms
Cloud platforms offer compute, storage, networking, and managed services with different operating models.
Talk through your project
A connected engineering approach
People, tasks, accessibility
Components, logic, integrations
Data, operations, maintenance
The architecture follows your product requirements.
01 / Where it fits
Compare AWS, Google Cloud, and Azure against actual requirements.
Reduce selected operational burdens.
Plan capacity, backups, and recovery.
02 / Architecture
Map data residency, identity, cost, reliability, and team skills before selecting services. Keep infrastructure changes reviewable.
More services increase both capability and operational complexity. Cost assumptions need real workload measurements.
03 / Delivery & care
Test authorization, schema changes, concurrency, timeouts, and recovery. Inspect operational logs and rehearse restoring data and rolling back a release.
Review the users, existing systems, constraints, and definition of success.
Document the architecture, interfaces, risks, and delivery scope.
Review working increments against real use cases and failure conditions.
Prepare operating guidance, ownership, and a practical maintenance plan.
04 / A few useful questions
More services increase both capability and operational complexity. Cost assumptions need real workload measurements.
Start with a code and architecture review. The useful outcome is a clear decision about what to keep, improve, or replace, with the dependencies and delivery risks made explicit.
The estimate depends on the workflows, integrations, platform requirements, and quality expectations. A focused discovery phase can reduce uncertainty before committing to the full build.
Agree on source ownership, documentation, monitoring, support responsibilities, and a maintenance plan as part of the engagement.
Something in mind?
Let's build