The infrastructure advice you'll find online falls into two camps: "just use a single server" from the indie hacker crowd, or "here's how to set up a multi-region Kubernetes cluster" from the enterprise architects. Neither is wrong — they're just advice for different stages. The real skill is knowing when to transition from one to the next. (For a frank account of how we did this at VIZIQO, see Building a SaaS in India — 12 Months, Real Lessons.)
Stage 1: Pre-Product-Market Fit (0-100 users)
At this stage, your infrastructure should be the simplest thing that works. A single EC2 instance (t3.medium or t3.large) running Docker Compose is more than enough. PostgreSQL, Redis, your API, and your frontend — all on one machine. Total cost: $30-50/month.
Why not start with containers or Kubernetes? Because every hour spent on infrastructure is an hour not spent on finding product-market fit. You're going to rewrite most of your code in the next six months anyway. Optimize for deployment speed (one git pull and docker-compose up) and debugging simplicity (everything's on one machine, logs are in one place).
The only non-negotiable: automated backups. Set up daily RDS snapshots or a cron job that dumps your database to S3. This takes 30 minutes and will save your company someday.
Stage 2: Early Traction (100-1,000 users)
You've found some product-market fit and usage is growing. The single-server approach starts showing cracks: deployments cause brief downtime, the database competes with the application for resources, and a traffic spike from a marketing push takes everything down.
Time to separate concerns. Move your database to RDS (managed PostgreSQL). Put your API behind an Application Load Balancer with 2 EC2 instances. Use S3 + CloudFront for static assets. Add ElastiCache for Redis. Total cost: $150-300/month.
This architecture handles 10x more traffic than a single server, gives you zero-downtime deployments, and separates your data from your compute. It's still simple enough for one engineer to manage.
Stage 3: Growth (1,000-10,000 users)
Now you need proper container orchestration. ECS (Elastic Container Service) with Fargate is our recommendation over Kubernetes at this stage. Fargate eliminates the need to manage EC2 instances entirely — you define your container, set resource limits, and AWS handles the rest. (If you're running AI workloads on top, the production lessons in LangChain + Qdrant in Production are where the surprise infrastructure costs hide.)
Add auto-scaling based on CPU/memory metrics or request count. Set up a proper CI/CD pipeline (GitHub Actions → ECR → ECS). Implement centralized logging (CloudWatch or Datadog). Add a CDN for your frontend. Total cost: $500-1,500/month.
Stage 4: Scale (10,000+ users)
At this point, you likely need Kubernetes — not because it's trendy, but because your deployment complexity demands it. Multiple services, different scaling requirements, canary deployments, service mesh for inter-service communication. EKS (Elastic Kubernetes Service) with managed node groups keeps the operational overhead manageable.
Consider multi-region if your users are geographically distributed. Add a global load balancer, replicate your database, and implement a caching strategy that works across regions. Total cost: $3,000-10,000+/month.
The Golden Rules
Never use a technology you can't debug at 3 AM when production is down. Always have automated backups AND test your restore process regularly. Monitor your infrastructure costs monthly — cloud bills have a way of growing silently. And remember: the best infrastructure is the simplest one that meets your current needs. You can always add complexity later — removing it is much harder.