ClawPaneThe Cheapest Way to Run AI Agents at Scale
AI agents at scale get expensive fast. A single agent processing 10K requests/month might cost $100. Ten agents at 100K requests each? That's $10,000+/month — and it grows with usage. Here's how to run the same workload for a fraction of the cost.
The Cost Drivers
Agent costs come from three places:
- Model API calls — 80–90% of total cost. This is where you focus optimization.
- Infrastructure — hosting, orchestration, database. Relatively fixed.
- Tool calls — external APIs the agent invokes. Variable but usually small.
Since model API calls dominate, that's where the leverage is.
Strategy 1: Route Simple Tasks to Cheap Models
This is the single most impactful optimization. In any agent workload:
- 35–40% of requests are simple (greetings, classifications, format conversions)
- 30–35% of requests are moderate (summaries, Q&A, basic generation)
- 25–30% of requests are complex (multi-step reasoning, creative generation)
Simple requests don't need GPT-5 ($1.25/1M input tokens). They produce identical results on GPT-5-nano ($0.05/1M tokens) or Gemini 2.5 Flash ($0.15/1M tokens) — a 8–25x cost reduction on those requests.
A model router like ClawPane does this automatically. Configure your cost/quality weights and every request gets routed to the cheapest sufficient model.
Expected savings: 30–45%
Strategy 2: Minimize Token Usage
Every token costs money. Reduce them:
Shorter System Prompts
Most system prompts can be cut by 30–50% without affecting output quality. Remove:
- Redundant instructions
- Lengthy persona descriptions
- Examples the model already handles correctly
Constrained Outputs
Set max_tokens to match the expected response length. A classification task doesn't need 1,000 output tokens. Cap it at 20.
Structured Output
Use JSON mode or function calling instead of free-form text. The model outputs less, responses are more consistent, and you parse more reliably.
Expected savings: 10–20%
Strategy 3: Cache Repeated Responses
Agents often answer the same questions:
- "What are your hours?"
- "How do I reset my password?"
- "What's the status of my order?"
A semantic cache stores responses and returns them for similar queries without making an API call. This eliminates 15–30% of requests entirely.
Expected savings: 15–30% of cached request volume
Strategy 4: Batch Non-Urgent Work
Not everything needs real-time responses:
- Content moderation queues
- Data extraction pipelines
- Report generation
- Bulk classification
OpenAI's Batch API is 50% cheaper. Anthropic and Google offer similar discounts. If 20% of your workload can be batched, that's another 10% off total spend.
Expected savings: 5–10% of total
Strategy 5: Right-Size Your Agent Architecture
Some agents are overengineered:
- Chained agents that could be single agents with tools
- Multi-turn conversations where a single request would suffice
- Redundant reasoning steps that add tokens without improving output
Audit your agent workflows. Every unnecessary LLM call is pure waste.
The Combined Formula
| Strategy | Effort | Savings |
|---|---|---|
| Model routing | 5 min | 30–45% |
| Token reduction | 2–4 hrs | 10–20% |
| Response caching | 1 day | 15–30% |
| Batch processing | 1 day | 5–10% |
| Architecture audit | 1 week | 10–30% |
Start with routing. It's 5 minutes of setup with immediate, significant savings and zero code changes. Everything else builds on top of it.
Example: Real Numbers
A team running 10 agents at 50K requests/month each:
| Approach | Monthly Cost |
|---|---|
| All GPT-5 | ~$3,200 |
| With model routing | ~$2,400 (40% savings) |
| + Prompt optimization | ~$2,000 (50% savings) |
| + Caching | ~$1,600 (60% savings) |
That's $2,400/month saved — $28,800/year — from optimizations that take a day or two to implement.