InfrastructureServer Configuration2-month engagement
Production AI Infrastructure: Containerized Deployment with GPU Inference, Auto-Scaling and Observability
A software company preparing to launch an AI product had built their solution on local dev machines and a single cloud VM. They had no deployment pipeline, no container strategy, no observability, and no plan for handling traffic spikes.
Problem
No separation between dev and prod environments. API keys stored in plain text. A single Python process serving all requests with no queuing or load balancing. Model inference running on CPU with 8-second response times.
Solution
Designed and deployed a production infrastructure stack: Dockerized FastAPI services on Kubernetes, GPU node pool for inference, NGINX ingress with rate limiting, Redis queuing, Prometheus/Grafana monitoring, and a CI/CD pipeline via GitHub Actions.
Technology
Docker, Kubernetes (GKE), NGINX ingress, Redis, Celery workers, PostgreSQL, Prometheus, Grafana, GitHub Actions CI/CD, HashiCorp Vault, NVIDIA A100 GPU node pool, Terraform
8s→0.4s
Inference response time
99.95%
Uptime SLA achieved
Auto
Scales 1 to 40 pods on demand
60%
Cost reduction vs. initial setup
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