I Built a Production App. The Credit Counter Almost Stopped Me.
The first time I built something real with an AI coding tool, I felt unstoppable. I described what I wanted. The AI generated working code. In an afternoon I had a working application, a backend, a database, user authentication, everything.
Then I tried to debug a tricky layout issue.
The AI fixed it. Introduced a new bug in the process. I asked it to fix that. It introduced another. Forty minutes later I had burned through credits without shipping anything, and the counter was getting low.
That was the moment I realized I had misunderstood what I was buying.
The Business Model I Missed
Credit-based AI builders are not development tools. They are software-as-a-service products with a consumption business model. The credits are the revenue mechanism.
This is not a criticism. It is a structural description. The business is designed to monetize AI generation cycles. The free tier exists to create sunk costs, a half-built application, before you realize what production use actually costs.
When you’re in a debugging loop, the math gets uncomfortable:
- AI introduces a bug while fixing another one: 30–60 credits
- You explain the context again because the AI lost it: 20–50 credits
- You ask it to refactor the component it got wrong: 40–80 credits
A single frustrating debugging session: 90–190 credits. Nothing shipped. That’s a meaningful fraction of a monthly credit budget on a single bad hour.
I did not discover this from a review or a comparison post. I discovered it by running out.
The Ceiling I Hit
When I wanted to add a message queue to my application, nothing exotic, just a standard async job pattern, I discovered that the infrastructure my app lived on didn’t support it. One framework. One database. That’s the model.
When my hosting free tier expired, the pricing I’d been comparing was not the pricing I faced. The number I needed was on a different line of the pricing page.
I started researching what real infrastructure looked like for a vibe-coded application.
What I Found
The answer was simpler than I expected: use the same AI tools I was already using (Claude Code), but connect them to infrastructure I actually owned.
OSC’s MCP server means Claude Code can provision infrastructure directly. I typed: “I need a PostgreSQL database, a Redis cache, and MinIO object storage for my app.” Three services were provisioned. Connection strings returned. I added them to my environment and continued building.
No credit counter for infrastructure. No hosting tier that expires. No architectural ceiling at “one database.”
I deployed my app from GitHub using My Apps. OSC runs it. I own the repo, the data, the configuration. If I ever want to move to AWS, GCP, or self-hosted infrastructure, the same open-source services run anywhere.
What This Looks Like at Scale
I am not an isolated case. Jonas Birmé, VP R&D at Eyevinn Technology, built an entire production streaming service, Streaming Tech TV+, in 36 hours with six AI agents and 14 OSC services. Fifteen thousand lines of code. A real streaming platform: transcoding, CDN, player, analytics, intercom. Not a prototype. Not a demo. A thing that works.
The difference is infrastructure that scales with what the AI can imagine, not infrastructure that charges you every time the AI tries.
The Practical Decision
If you have a vibe-coded app that is approaching an infrastructure ceiling, or if your free hosting tier is about to expire, the path to real infrastructure is shorter than it sounds.
Your code already exists. You are not rebuilding. You are moving where it runs.
OSC Professional: €69/month (reduced from €199 in March 2026). Everything included. No credit counter.
Deploy free, no credits required →
Open Source Cloud is operated by Eyevinn Technology. All services are unmodified open source. Full disclosure: I work at Eyevinn Technology.
