Optimizing LLM Token Costs in Production: A Practical Engineering Playbook [Part 3] | Towards AI

Author(s): Garvit Agarwal Originally published on Towards AI. Optimizing LLM Token Costs in Production: A Practical Engineering Playbook In Part 2, we focus ...

lunedì 20 luglio 2026 New tab

1,847 words~8 min read

Author(s): Garvit Agarwal

Originally published on Towards AI.

In Part 2, we focused on optimizing how requests are constructed before they reach the language model. We explored how techniques like Model Routing, Prompt Caching, and Conversation Summarization reduce unnecessary token usage without affecting the user experience.Those optimizations alone can significantly reduce production costs. But here’s something that surprised me when I started studying production AI systems.

Many applications continue to spend thousands — or even millions — of unnecessary tokens after the request has already been optimized.

How?

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dev.to·Jul 19, 2026 · 1 g fa
8 LLM Cost Optimization Techniques for Production AI

Optimizing LLM Token Costs in Production: A Practical Engineering Playbook [Part 3] | Towards AI

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Optimizing LLM Token Costs in Production: A Practical Engineering Playbook [Part 3] | Towards AI

Other newsrooms on this story

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