Llm Cost Optimizer

LLM Cost Optimizer

Reduce LLM API costs systematically without sacrificing output quality.

Three Modes

1. Cost Audit

Assess current spend and find the 80/20 opportunities.

1. Instrument -- Add token counting and cost tracking per request. Log model, input tokens, output tokens, latency, and use case.
2. Find 80/20 -- Identify which 20% of use cases drive 80% of cost. Sort by total spend, not per-request cost.
3. Classify -- Tag each use case by complexity: simple (classification, extraction), medium (summarization, Q&A), complex (reasoning, code generation, multi-step).

2. Optimize Existing

Apply techniques to reduce cost on current workloads.

1. Routing -- Route simple tasks to cheaper/smaller models. See optimization-techniques.md.
2. Caching -- Cache repeated or similar queries. Prompt caching for system prompts.
3. Compression -- Reduce prompt size without losing quality. Trim examples, remove redundancy.

3. Design Cost-Efficient

Build new systems with cost awareness from day one.

1. Budget envelopes -- Set per-feature monthly cost budgets. Alert at 80%.
2. Routing layer -- Default to cheapest model that meets quality bar. Escalate on failure.
3. Observability -- Track cost per user, per feature, per model. Dashboard with trends.

Optimization Order

Apply techniques in this order (highest impact first):

1. Model routing (60-80% reduction potential)
2. Prompt caching (40-90% on cached portions)
3. Output length control (20-40%)
4. Prompt compression (15-30%)
5. Semantic caching (30-60% hit rate)
6. Request batching (10-25%)

See [optimization-techniques.md](./optimization-techniques.md) for detailed guidance on each technique.

What You Get

• A cost audit identifying which use cases drive the majority of LLM spend, with per-request and total cost breakdowns.
• Prioritized optimization recommendations (model routing, caching, compression) ranked by reduction potential.
• Concrete implementation steps with expected cost savings percentages for each technique.

Rules

1. Never optimize before measuring -- instrument first
2. Never sacrifice quality silently -- A/B test every change
3. Cost per request is misleading -- optimize total spend per outcome
4. The cheapest token is the one you never send

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: DROOdotFOO
• Source: DROOdotFOO/agent-skills
• License: MIT
• Homepage: https://droo.foo/

Install and usage instructions live in the source repository linked above.