Skip to content
Samay
All engineering01 / 03

Case study

GPU Image Generation Platform

Designed infrastructure supporting GPU-based image generation workloads across cloud environments.

Technologies
  • AWS
  • GCP
  • GPU infrastructure
  • Terraform
  • CI/CD
  • Observability
Focus areas
  • GPU workloads
  • Multi-cloud infrastructure
  • Scaling
  • Automation
  • Cost optimization

Problem

GPU workloads behave differently from ordinary application workloads, and they are expensive to run. The core challenge was providing enough GPU capacity for image generation without paying for powerful instances that sat idle.

Around that sat the rest of the problem: infrastructure that had to be reproducible rather than hand-configured, workloads spanning more than one cloud, a predictable way for application teams to submit and track generation jobs, and better visibility into what the system was actually doing.

Approach

I treated the GPU workload as a platform rather than a manually managed GPU server. Infrastructure was provisioned through Terraform, and dedicated GPU compute for image generation was kept separate from the application layer that accepts and tracks jobs.

Generation requests became asynchronous jobs with tracked state, so callers could submit work and check on it later. Models and generated outputs lived in object storage, and reusable CI/CD workflows deployed both infrastructure and application changes.

Monitoring and logging covered the GPU workloads and the services around them. Scheduled scaling and right-sizing kept capacity available when it was needed, without running maximum capacity around the clock.

Architecture

Generation requests enter through an API layer and are recorded as jobs. Each job is handed to GPU-backed compute on AWS or GCP, where the image is generated; outputs are written to object storage and the job state is updated, so callers can follow progress and retrieve the result.

Storage, job state, networking and deployment are built from cloud-native services around the GPU workers, while Terraform, CI/CD and observability apply across the whole platform rather than to any single component.

High-level architecture

  1. Request

    • Generation request
    • API / application layer

  2. Job Management

    • Job state
    • Workload scheduling

  3. GPU Compute

    • GPU capacity · AWS
    • GPU capacity · GCP

  4. Storage & Delivery

    • Generated outputs
    • Object storage
    • Result retrieval

Across every stage

  • Terraform
  • CI/CD
  • Observability
Simplified, high-level view. Components are intentionally generic.

Outcome

What had been expensive, manually operated compute became a repeatable, manageable platform. Infrastructure was consistent, deployments were repeatable, and the health of the workloads was visible.

GPU capacity could scale with demand instead of staying at maximum, which kept cost under control, and developers reached image generation through a defined service interface.

Responsibilities
  • Designed the cloud infrastructure for GPU-based image-generation workloads
  • Built and maintained the infrastructure as code with Terraform
  • Designed the supporting networking, storage and job-management components
  • Built and standardized the deployment pipelines
  • Implemented monitoring and logging across the platform
  • Right-sized GPU compute and introduced scheduled scaling to reduce idle GPU runtime
  • Supported developer integrations and operated the platform across both clouds
Lessons & considerations
  • Availability versus cost: always-on GPU capacity is predictable but expensive, while aggressive scale-down saves money at the cost of startup time. Scheduled capacity and right-sizing provided the balance.
  • Consistency across clouds: AWS and GCP expose GPU compute, storage and networking differently, so the goal was a consistent deployment approach rather than pretending both platforms were identical.
  • Operational visibility: failed or stuck GPU jobs need to surface quickly because wasted GPU time is expensive.