AI safety budgets are often zero until an incident forces reactive spending. Proactive budget planning prevents this pattern and ensures safety infrastructure is in place before it is needed. This guide breaks down the cost categories and provides a framework for planning.
Self-hosted open-source tools (like Authensor) have infrastructure costs but no licensing fees:
Estimated cost: $200 to $2,000 per month depending on agent volume and retention requirements.
Managed safety services charge per API call, per agent, or per seat. Evaluate whether the convenience of managed services justifies the cost premium over self-hosting.
Refer to the team structure guide for role definitions. Budget considerations:
For a small team, expect 10 to 20% of one engineer's time dedicated to safety. For larger deployments, budget for dedicated headcount.
Budget for at least two red team exercises per year for each high-risk agent.
A reasonable starting budget for AI safety is 5 to 15% of the total AI/ML engineering budget. Organizations in regulated industries should plan toward the higher end.
| Organization Size | Agent Count | Suggested Annual Budget | |---|---|---| | Startup | 1 to 3 | $5,000 to $20,000 | | Growth | 4 to 15 | $50,000 to $200,000 | | Enterprise | 15+ | $200,000+ |
These figures cover tooling, allocated engineering time, and basic compliance. External red teaming and regulatory consultations are additional.
Present the budget alongside the ROI analysis from the measuring ROI guide. The cost of safety infrastructure is consistently lower than the cost of unmanaged incidents.
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