Data boundaries
Decide which records, repositories, and conversations the assistant can read before adding write actions.
Assistant hosting planner
Free Self-Hosted AI Assistant Planner
A self-hosted AI assistant planner helps you define where your assistant runs, what data it can access, how memory should work, which tools it needs, and who will operate it after launch.
Hosting track
Single-tenant managed hosting
Complexity
Low
Readiness score
93/100
Plan the assistant
Tool permissions
Start read-only, then promote high-confidence workflows to approval-gated actions.
Human escalation
Define when the assistant should ask a person instead of attempting another automated step.
Self-hosting checklist
The assistant is only production-ready when deployment, memory, tool access, observability, and support ownership are all written down.
Compare managed NitroClaw setupSecrets and API keys are stored outside prompts.
Tool calls are logged with status and owner.
Memory retention is explicit and reversible.
High-risk actions require human approval.
Monthly model spend has alert thresholds.
Fallback owner is assigned for outages.
Self-hosted AI assistant FAQ
What is a self-hosted AI assistant?
A self-hosted AI assistant is an AI agent that runs in infrastructure you control or in a dedicated managed environment. It can use private tools, memory, and business context without sharing one generic workspace with unrelated users.
Do I need to host the model myself?
No. Many self-hosted assistants use hosted model APIs while keeping the agent runtime, tools, logs, and memory in your own environment. Local models make sense when data policy, latency, or cost targets require them.
What should I plan before deploying an AI assistant?
Define the assistant job, allowed channels, data sensitivity, model provider, memory rules, tool permissions, monitoring, human escalation, and ongoing maintenance ownership before launch.
When is a managed private deployment better?
A managed private deployment is best when the assistant needs private integrations, code or customer context, reliable monitoring, and someone accountable for updates without your team owning the full infrastructure stack.