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
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Lifecycle. Rollouts, graceful shutdown, and draining active tasks so a
SIGTERMdoesn'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'm a MonkeyCode user sharing my own experience, not affiliated with the project.
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