Every self-hosted platform comes with an invisible job description: patch it, monitor it, drain it during deploys, page someone when it breaks, and keep its model backend healthy. For an AI development platform, that's a standing operations commitment most teams underestimate.
MonkeyCode can be self-hosted (github.com/chaitin/MonkeyCode, AGPL-3.0), and it now offers a hosted platform at monkeycode-ai.net, free to start with no install. The interesting question for an ops-minded reader isn't "which is better" — it's "which operational burden am I signing up for."
What you own when you self-host
Lifecycle. Rollouts, graceful shutdown, and draining active tasks so aSIGTERMdoesn't corrupt in-flight work.
Observability. Metrics, logs, and traces for task success, latency, and model-backend health — you build the dashboards.
Capacity and failure domains. GPUs and workers become service capacity only when they're schedulable, drainable, and bounded by deadlines.
Patch cadence. Security updates on your clock, not the vendor's.
What the managed platform removes
The hosted SaaS takes those four off your plate in exchange for a service dependency and explicit data-flow decisions. That's a good trade when you have no platform team, when time-to-value matters more than control, and when your data path constraints are satisfiable by a hosted service.
A pragmatic move: pilot on the hosted platform to learn the workflow and demand shape, then decide whether owning the operations is worth it. Start at monkeycode-ai.net, free to start. Before you plan capacity, ask on the MonkeyCode Discord about current free model-credit availability, eligibility, and limits — treat free capacity as a trial signal, not a stable SLO.
Disclosure: I contribute to the MonkeyCode project. The notes above are based on the linked repository and the hosted platform described in this article.
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