How to Run Multiple $10K/Month Companies on a $20/Month Stack

Instead of complex defaults like AWS's EKS, RDS, and NAT Gateway, the core approach is running services on a single $5-10 VPS. Even 1GB of RAM can be fully utilized, and a swapfile can be attached when needed to keep operating costs low.

The backend is written in Go, built as a single static binary, then uploaded to the server via scp and run. This avoids dependency hell like pip install, and lets you build a production-grade web server without stacking bloated frameworks.

Batch AI jobs run on a local GPU. Using an RTX 3090 and vLLM eliminates external costs like the OpenAI API—development and experimentation start with Ollama, then scale to vLLM in production, and further to Transformer Lab. For agent/LLM management, laconic and llmhub are used.

For user-facing low-latency inference, rather than sticking only to local models, everything is bundled through OpenRouter. Instead of managing Anthropic, Google, and OpenAI separately, a single OpenAI-compatible integration provides access to multiple frontier models, with automatic fallback to another model if one goes down.

For coding assistance, GitHub Copilot is used instead of expensive AI IDEs. Taking advantage of its per-request pricing structure, the strategy keeps costs low even when an agent spends a long time scanning the codebase and changing many files.

For databases, SQLite is used by default even for new projects. Enabling WAL reduces read/write contention with each other, and a single local .db file is considered sufficient to handle a good number of concurrent users. Authentication hassles are absorbed using self-built libraries like smhanov/auth.

The conclusion is clear:

• Infrastructure costing tens of dollars a month or less can sufficiently run a SaaS
• Simple servers + simple deployment + local AI are advantageous over complex cloud setups
• By eliminating most costs, you can focus longer on PMF exploration instead of worrying about burn rate