We've built Argmin AI after shipping LLM features where the demo worked, then the bill and latency got unpredictable in production. Prompts expanded, context grew, retrieval got noisy, retries appeared, and agent workflows added loops.
Argmin AI optimizes LLM-related expenses as a system:
1. prompt and context efficiency 2. model selection and routing 3. RAG inefficiencies and caching opportunities 4. agent workflows (tool calls, retries, loop control)
Changes are validated with evals and guardrails (tests, gates, judges), tailored to your quality definition and goals.
Before paying for optimization work, we start with a structured assessment: we map the top cost drivers in your pipeline and estimate savings, so you can align internally on where to focus.
I would love feedback from teams running LLMs in prod: what is hardest for you today, cost attribution per workflow, safe routing, or eval coverage?
P.S. If you are not sure whether your setup has room for optimization, we built a 3 minute cost calculator based on published industry research and pricing benchmarks: https://app.argminai.com/signup/cost-calculator