I tried building on an agent platform for six months. Here is why I moved to a self-managed stack.

Reddit r/AI_Agents Tools

Summary

A developer shares their experience moving from an agent platform to a self-managed stack after six months, citing better control over model selection, cost, and execution isolation, leading to a 60% drop in token costs.

I built on top of an agent platform for six months. It had memory, tool calling, a skills marketplace. It looked complete. It was the wrong shape for what I needed. The platform handled scaffolding with auth and deployment. It did not handle model selection per task, plan review before execution, or isolation from production infrastructure. When the agent picked a premium model for linting, I had no visibility into why. When it touched a staging database, I had no way to sandbox the execution. The platform abstracted away the exact complexity I needed to manage. I rebuilt the same workflow with explicit model routing, cost per task, and a review gate before anything ran. Token costs dropped about 60%. The setup was harder because there are more knobs. The control was worth it. Not everyone needs this. Teams with existing infrastructure will probably prefer the managed path.
Original Article

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