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Vduce Technology
AWS · Azure · Docker

Infrastructure you never have to think about.

Great software fails on bad infrastructure. We design cloud environments on AWS and Azure, containerise applications with Docker, automate deployments and set up monitoring that warns you before your customers notice. For AI workloads we plan GPU capacity, model hosting and cost controls.

Discuss your project

At a glance

Typical timeline
Audit: 1 week. Setup projects: 2–6 weeks.
  • AWS (S3, ECS, Lambda, SNS, CloudWatch)
  • Azure
  • Hetzner
  • Vercel
  • Docker
  • Kubernetes
  • Terraform
  • GitHub Actions
Who it is for

For product teams with manual deployments, rising cloud bills or no monitoring, and for companies preparing to run AI models in production.

Problems we solve

  • Deployments are manual, nerve-racking and happen at night.
  • Your cloud bill grows faster than your revenue.
  • You find out about outages from customers.
  • Only one person knows how the servers are set up.
  • You need GPUs for AI but don't know what to rent or buy.
What you get

Cloud, DevOps & AI Infrastructure

Cloud architecture

A documented target architecture on AWS or Azure, sized for today and ready for growth.

Infrastructure as code

Terraform or CDK definitions, so environments are reproducible and reviewable.

CI/CD pipelines

Automated tests, builds and deployments on every merge, with preview environments.

Monitoring & alerting

Logs, metrics, uptime checks and alerts in CloudWatch, Grafana or Sentry.

Security & backups

Least-privilege IAM, secrets management, encrypted backups and tested restores.

AI infrastructure

Model serving, GPU sizing, vector databases and per-request cost tracking.

Our approach

AWS · Azure · Docker

  1. 01

    Audit

    A one-week review of your current setup, costs and risks, with a prioritised list.

  2. 02

    Quick wins

    Backups, alerts and cost savings that pay off within weeks.

  3. 03

    Automate

    Infrastructure as code and CI/CD, so every change is repeatable.

  4. 04

    Operate

    Optional managed operations with an SLA and monthly reports.

From the edge to the cloud

Many of our projects connect physical sites to the cloud. In the smart washroom platform, sensors report to an edge PC on site, which forwards data to a cloud API, which feeds a live dashboard and a mobile app. In the ALPR parking system, a containerised FastAPI service stores images in S3, publishes alerts through SNS and reports health to CloudWatch. We design for the unhappy path: lost connections, full disks and restarts.

Lower cloud bills

Typical savings come from right-sizing instances, moving static assets to a CDN, using reserved capacity for steady workloads and shutting down idle environments at night. We show you the numbers before we change anything.

Ready for AI workloads

Running your own models requires different planning: GPU memory, batching, cold starts and monitoring of quality, not just uptime. We help you decide between managed APIs, rented GPUs and your own hardware, based on volume and data-protection needs.

Request an infrastructure audit

Related case studies

Similar project? Let’s talk

Client confidential · For 4CT · Smart DigiBuild parking module

Real-time licence plate recognition for smart parking

  • Python
  • YOLOv8
  • EasyOCR
View case study

Client confidential · For 4CT · international airport, India

Smart washroom platform for an international airport

  • Python
  • MQTT
  • Azure IoT Hub
View case study

Frequently asked questions

  • AWS or Azure?

    We work with both. Azure is often the natural choice for Microsoft-heavy companies; AWS offers the broadest service range. German hosters like Hetzner are great for cost-sensitive workloads.

  • Can our data stay in Germany?

    Yes. AWS Frankfurt, Azure Germany West Central and German hosting providers keep data in Germany.

  • Do you offer 24/7 support?

    Our SLA plans cover business hours in Germany and India, which gives extended coverage across time zones. 24/7 on-call is available for critical systems on request.

  • Do we need Kubernetes?

    Most companies don't. Managed containers (ECS, Azure Container Apps) or a simple Docker setup are easier to run. We recommend Kubernetes only when the scale justifies it.

  • How fast can you set up CI/CD?

    For a typical web or API project, a working pipeline with tests and preview environments takes one to two weeks.

Let’s talk about your Cloud, DevOps & AI Infrastructure project.

A 30-minute call with a senior engineer. You leave with a clear next step, whether you work with us or not.

Discuss your projectWork
Let’s talk