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title: LLM API Cost Optimizer
emoji: 💰
colorFrom: yellow
colorTo: blue
sdk: docker
pinned: false
license: mit
LLM API Cost Optimizer
Question
How do token volume, model price, caching, batching, and routing change monthly LLM cost?
System Boundary
This Streamlit Space is a deterministic cost model. It is not a billing system; it is a planning tool for model-serving decisions.
Method
The user enters request volume, input/output token counts, model prices, cache hit rate, batching gain, and routable traffic share. The app computes baseline and optimized monthly costs.
Technique
This is systems modeling for LLM inference. It separates the variables that drive cost: request count, input tokens, output tokens, price per token, cache hit rate, batching efficiency, and model routing.
The calculations are deterministic so the economic assumptions are visible.
Output
The app returns cost metrics, a strategy comparison table, and a bar chart of monthly cost by optimization strategy.
Why It Matters
LLM engineering includes economics. A model that works technically may still fail if prompt length, routing, or caching is ignored.
What To Notice
The biggest savings often come from reducing repeated work: semantic caching, shorter prompts, and routing easy requests to smaller models.
Effect In Practice
Cost modeling informs whether to use hosted APIs, Hugging Face Inference Endpoints, local inference, batching, caching, or a router.
Hugging Face Extension
The Space can be extended with real endpoint pricing, model latency measurements, and quality/cost Pareto charts for open models.
Limitations
Real costs depend on provider pricing, latency constraints, cache implementation, batch windows, retries, and quality tradeoffs. Treat this as a planning estimate.
Run Locally
pip install -r requirements.txt
streamlit run app.py