Why I Built a Distributed SQLite on S3 (And Why You Might Care)

If you've ever deployed on AWS Lambda or App Runner, you know the pain: You need backend storage, but RDS is overkill. RDS is great. It's also $30–$100/month minimum before you write a single query. For small apps, side projects, or cost-sensitive workloads, that's a hard pill to swallow. So you look at the alternatives. The obvious candidates DynamoDB? Great, until your access patterns don't fit and you're fighting it constantly. SQLite? Perfect size. Except Lambda and App Runner are ephemeral. No persistent local disk. Dead on arrival. Mount S3 directly? Tools like Mountpoint-S3 exist, but SQLite on a mounted S3 bucket only supports a single writer. The moment you have more than one container, you're in trouble. So what do you do? What I built I wanted SQLite. I wanted S3. I wanted multiple App Runner instances or Lambda functions to be able to write concurrently without corrupting each other. So I built distributed-sqlite: a Python library that lets multiple ephemeral compute instan