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Cloud transformation · DevOps · Infrastructure · UAE

From AWS to Google Cloud: Re-engineering a UAE Logistics Company's Infrastructure for
42% Lower Costs and 58% Faster Application Performance.

Our team migrated and re-engineered cloud infrastructure for a UAE logistics company, improving performance, efficiency and scale while keeping systems running.

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The challenge

Rising spend. Uneven performance. Hard-to-scale ops.

Cloud costs were climbing from inefficient allocation. Response times were inconsistent, with clear bottlenecks. Compute sat underused while management complexity grew across services, and scaling stayed limited. The goal was a cleaner architecture—without risking integrity, data consistency or business continuity.

Focus
  1. 01Cloud transformation
  2. 02DevOps
  3. 03Infrastructure optimization
Engagement
  1. 0112 weeks · 4 phases
  2. 02AWS → Google Cloud
  3. 0325+ services · 3 production databases
  4. 04Cutover under 30 minutes
01 · Challenge

Infrastructure under pressure

Spend, latency and operational load were pulling in different directions. The platform needed a deliberate migration and redesign—not another patch on top of the old layout.

02 · Approach

Phased migration in twelve weeks

VentureDev ran assessment and architecture, environment prep, staged workload and database moves, then a controlled cutover with monitoring and fine-tuning.

03 · Results

Lower cost. Faster apps.

All workloads moved. Monthly cloud spend fell 42%. Average API response improved 58%. Final cutover downtime stayed under 30 minutes.

Delivery path

Four phases.
Twelve weeks.

Assessment and architecture, environment preparation, migration of workloads and databases with testing, then cutover, validation and fine-tuning—100% of workloads moved.

  1. 01AssessWeeks 1–2 · architecture
  2. 02PrepareWeeks 3–5 · network & staging
  3. 03MigrateWeeks 6–9 · workloads & DBs
  4. 04Cut overWeeks 10–12 · validate
Results in detail

Performance and operations after cutover.

Average API response moved from 420 ms to 176 ms (58% faster). P95 fell from 1,200 ms to 480 ms. Database query time from 180 ms to 95 ms. Page load from 2.8 s to 1.3 s. Uptime from 99.9% to 99.97%. Deployments from 45 min to 12 min; provisioning from 3 h to 20 min; manual ops from 40 to 22 hours a month. Concurrent request capacity tested at 2.5× prior levels, with 35% better average compute utilization, automated scaling and centralized monitoring.

At a glance
  1. 01API avg 420 ms → 176 ms
  2. 02P95 1,200 ms → 480 ms
  3. 03DB query 180 ms → 95 ms
  4. 04Page load 2.8 s → 1.3 s
  5. 05Deploy 45 min → 12 min
  6. 06Provisioning 3 h → 20 min
  7. 07Manual ops 40 → 22 hrs/month
  8. 082.5× concurrent capacity
Technology

Stack used on
the engagement.

Migration path: AWS to Google Cloud. Platforms our team also works with include Azure. Containerization with Docker; orchestration with Kubernetes; infrastructure as code with Terraform; CI/CD with GitHub Actions; monitoring and observability with Prometheus, Grafana and CloudWatch; databases with PostgreSQL; caching with Redis.

What could we
build together?

A clear brief or a half-formed idea.
Start with what you know.

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