Lessons Learned Deploying a Multi-Service AI Application

Taking InternFlow from Docker Compose on a laptop to a production server exposed problems that no tutorial prepares you for.

Deploying a single web application is straightforward. You push code, a server runs it, done.

Deploying seven interconnected services — with AI models, background crawlers, databases, and a reverse proxy — is an entirely different challenge. Here’s what I learned taking InternFlow to production.

The production stack

Lesson 1: Separate infrastructure from application logic

The biggest mistake I made early on was mixing infrastructure configuration with application code. Environment variables were hardcoded in places. Database connection strings were duplicated across services. SSL config was tangled with application startup.

Untangling this was painful. The fix was treating infrastructure as a separate concern with its own files: .env files per environment, Docker secrets for sensitive values, and Nginx config managed independently from application deployments.

Lesson 2: Logs are your only debugger in production

Good logs are not a nice-to-have. In a multi-service architecture, they’re the only way to know what your system is doing.

Lesson 3: Health checks are load-bearing

Docker’s healthcheck directive sounds like a monitoring nicety. In practice, it's critical. Services come up in an unpredictable order, and a FastAPI service that starts before PostgreSQL is ready will crash on first database connection.

Adding proper health checks and depends_on: condition: service_healthy in Docker Compose eliminated an entire class of startup race conditions that had been causing random failures.

Lesson 4: Persistent storage requires deliberate planning

The FAISS vector indexes are large. Model weights are large. PostgreSQL data must survive container restarts. Early on I had all of this living in ephemeral container filesystems — one docker compose down wiped everything.

Named Docker volumes, mounted at predictable paths, with backup scripts attached to a cron job: this is what production persistent storage looks like for a self-hosted setup.

The key lessons, distilled

01

Infrastructure is application code

Treat Dockerfiles, Nginx configs, and Compose files with the same care as your Python or TypeScript. They are part of your system.

02

Observability before features

You cannot debug what you cannot see. Logging and health checks should be implemented before any feature reaches production.

03

Assume services will fail

Design for restart. Health checks, graceful shutdowns, and idempotent startup sequences prevent cascading failures.

04

Persistence is separate from compute

Containers are disposable. Data is not. Map your volumes explicitly and test your backup story before you need it.

Software engineering at this level isn’t only about writing features. It’s about designing systems that stay up, that fail gracefully, and that you can understand when they don’t.

InternFlow is still evolving. Part 4 covers where it’s going next — beyond an internship platform into a full developer career tool.

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