Category: AI Infrastructure

Cost Management for Large Language Models: Pricing Models and Token Budgets

Cost Management for Large Language Models: Pricing Models and Token Budgets

Learn how to control LLM costs with token budgets, pricing models, and optimization tactics. Reduce spending by 30-50% without sacrificing performance using real-world strategies from 2026’s leading practices.

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Confidential Computing for Privacy-Preserving LLM Inference: How Secure AI Works Today

Confidential Computing for Privacy-Preserving LLM Inference: How Secure AI Works Today

Confidential computing enables secure LLM inference by protecting data and model weights inside hardware-secured enclaves. Learn how AWS, Azure, and Google implement it, the real-world trade-offs, and why regulated industries are adopting it now.

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Model Compression Economics: How Quantization and Distillation Cut LLM Costs by 90%

Model Compression Economics: How Quantization and Distillation Cut LLM Costs by 90%

Learn how quantization and knowledge distillation slash LLM inference costs by up to 95%, making powerful AI affordable for small teams and edge devices. Real-world results, tools, and best practices.

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Autoscaling Large Language Model Services: How to Balance Cost, Latency, and Performance

Autoscaling Large Language Model Services: How to Balance Cost, Latency, and Performance

Learn how to autoscale LLM services effectively using the right signals-prefill queue size, slots_used, and HBM usage-to cut costs by up to 60% without sacrificing latency. Avoid common pitfalls and choose the right strategy for your workload.

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