Key Takeaways
- Model tiering reduces costs by 50-70%: route 70% of queries to cheap models (Haiku, GPT-3.5) and reserve expensive models for complex reasoning.
- Prompt optimization has the highest ROI: every token in the system prompt is multiplied by every request. Cut prompt length by 30-50% through careful editing.
- Caching identical and semantically similar queries eliminates redundant LLM calls, saving 20-40% on repeat-heavy workloads.
- Conversation summarization prevents context explosion: a 200-message history adds thousands of tokens per request. Periodic summarization reduces this 80%.
- Daily cost dashboards prevent surprises: track costs per agent, per task type, and per user to identify optimization opportunities.
Understanding Agent Cost Drivers
| Cost Driver | % of Total | Optimization Lever | Savings Potential |
|---|---|---|---|
| LLM API calls (tokens) | 60-80% | Model selection, prompt optimization | 50-70% |
| Tool execution (APIs) | 10-20% | Caching, batching | 20-40% |
| Infrastructure (hosting) | 5-15% | Right-sizing, serverless | 10-30% |
| Memory (vector DB) | 2-5% | Pruning, tiered storage | 10-20% |
LLM token costs dominate agent expenses. A single agent task using GPT-4 for 10 reasoning steps consumes 20,000-50,000 tokens ($0.60-1.50 per task). At 1,000 tasks/day, that is $600-1,500/day in LLM costs alone.
Model Tiering and Smart Routing
| Task Complexity | Model | Cost/MTok Output | Quality | % of Workload |
|---|---|---|---|---|
| Simple (classification, extraction) | GPT-3.5 / Haiku | $1.50 / $1.25 | Sufficient | 40-50% |
| Standard (Q&A, summarization) | GPT-4o / Sonnet | $15 / $15 | Good | 30-40% |
| Complex (reasoning, analysis) | GPT-4 / Opus | $30 / $75 | Excellent | 10-20% |
Build a lightweight classifier (using Haiku or GPT-3.5 at $0.001/query) that routes incoming queries to the appropriate model tier. This adds minimal cost per query but saves 50-70% by preventing expensive models from handling simple tasks.
Prompt Optimization and Caching
- Trim system prompts: remove redundant instructions, verbose examples, and unnecessary context. A 2,000-token system prompt at 1,000 requests/day costs $60-200/month on GPT-4.
- Exact-match caching: store responses for identical queries. Use Redis with TTL for time-sensitive data. This eliminates redundant LLM calls entirely.
- Semantic caching: embed queries and check similarity to cached queries. If similarity exceeds 0.95, return the cached response. Saves 20-30% on repeat-heavy workloads.
- Conversation summarization: replace full conversation history with LLM-generated summaries every 10-15 messages. A 100-message history is 15,000+ tokens; a summary is 200-500 tokens.
- Few-shot example pruning: use the minimum number of examples needed for quality. 2-3 good examples often perform as well as 5-6.
Cost Monitoring and Budgets
- Per-request cost tracking: log token usage and model for every LLM call. Calculate cost per agent task, per user, and per use case.
- Daily cost dashboards: visualize spending trends, identify anomalies, and compare actual vs budgeted costs
- Budget alerts: set daily and monthly spending limits with automated alerts at 80% and 100% thresholds
- Token budgets per task: set maximum token consumption per agent session. When exhausted, the agent summarizes and stops.
- Cost attribution: tag requests with user, department, and use case metadata. This enables chargeback and identifies high-cost patterns.
Agent loops (repeated failed tool calls, circular reasoning) can consume thousands of tokens in minutes. Always implement maximum iteration limits (typically 10-20 steps) and session token budgets. A single runaway agent can cost $10-100 in minutes.
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