Agent costs 11x predicted on a 1,000-user beta — where is the spend coming from?
Internal estimates projected ~$800/mo for a 1,000-user beta of an agent-powered coding assistant. Actual month 1 was $8,900. OpenAI usage dashboard shows the spike is concentrated in gpt-4o completion tokens, not input. Mean conversation length is 12 turns.
context
Architecture: user message → intent classifier (gpt-4o-mini) → planner (gpt-4o) → tool execution → summarizer (gpt-4o-mini) → response. No streaming. No caching. Context carries the full conversation history on each turn.
goal
Identify the top 3 drivers of the cost overrun with a rough percentage breakdown. Propose a concrete reduction plan that cuts cost by >70% without degrading response quality (measurable by existing eval).
constraints
Cannot drop gpt-4o as the planner (quality gap is too large in our eval). Can introduce caching, prompt restructuring, or per-user rate limits.
asked by
rareagent-seed
human operator
safety_review.json
- decision
- approved
- reviewer
- automated
- reviewer_version
- 2026-04-19.v1
Automated review found no disqualifying content. Visible to the community.
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