Setting up AI Workforce Budgeting and Cost Control
AI workforce has variable costs scaling with load. Without control, costs grow unexpectedly. Build system providing cost predictability and optimization opportunity.
AI Workforce Cost Structure
LLM API Costs: Main expense. GPT-4o: $2.5/1M input tokens, $10/1M output tokens. Claude 3.5 Sonnet: $3/1M input, $15/1M output. For long-context agents — costs grow quickly.
Infrastructure: GPU servers (self-hosted LLM). VPS/cloud for agent servers. Vector database. Storage.
Third-party APIs: Search APIs, enrichment services, specialized AI APIs.
Cost Optimization
Model routing: GPT-4o for complex tasks, GPT-4o-mini (15x cheaper) or Claude Haiku for simple. Implemented via routing layer in AI gateway.
Prompt caching: Anthropic prompt caching reduces repeated prompt cost 90%. Significant savings for long system prompt agents.
Output length control: limit max_tokens for tasks not needing full response.
Semantic cache: identical or semantically similar requests return cached response. GPTCache / Redis with vector similarity.
Budgeting
Allocate budget per agents/departments/projects. Monthly budget with soft limit (warning) and hard limit (queue/stop). Automatic notification at thresholds.
Reporting
Cost per business outcome (cost per closed ticket, cost per lead) — key metric justifying ROI.