Deploying AI Apps on AWS: EC2, S3, and the Real-World Gotchas

Notes on deploying AI applications when storage, compute, credentials, and provider calls all meet production.

Deploying an AI app is not only about pushing code. AI products usually connect file storage, backend compute, environment secrets, third-party APIs, and user-facing latency.

EC2 gives control

EC2 is useful when the backend needs long-running processes, custom runtime setup, or predictable control over the environment. The tradeoff is that you own patching, monitoring, and process management.

S3 keeps generated assets simple

For products like resume generation or media workflows, S3 is a reliable place to store outputs. The important parts are access control, naming, lifecycle rules, and predictable upload flows.

Secrets and observability matter

AI provider keys, logs, rate limits, and retries become part of the production surface. A deployment is only finished when you can understand what happened after something fails.