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      <managingEditor>gungunpriatna@qadrlabs.com (Gun Gun Priatna)</managingEditor>
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    <guid>https://qadr.tech/blog/ai-finops-cost-optimization-strategies-for-large-language-model-implementation</guid>
    <title>AI FinOps: Cost Optimization Strategies for Large Language Model Implementation</title>
    <link>https://qadr.tech/blog/ai-finops-cost-optimization-strategies-for-large-language-model-implementation</link>
    <description>Strategizing AI spend in a world of token-based pricing. Discusses prompt engineering for efficiency, choosing between small vs. large models, and implementing guardrails to prevent runaway AI costs. Covers token monitoring, cost attribution, model selection trade-offs, caching strategies, rate-limiting, and ROI measurement frameworks for sustainable AI deployment.</description>
    <pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate>
    <author>gungunpriatna@qadrlabs.com (Gun Gun Priatna)</author>
    <category>AI Costs</category><category>FinOps</category><category>Cloud Management</category><category>LLM Optimization</category>
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