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Operational Checklist: AI Inference Cost Optimization

AI Inference Cost Optimization. Practical guidance for reliable, scalable platform operations.

KU
Kiril UrbonasAI & ML Engineer
|Feb 20, 2026
Operational Checklist: AI Inference Cost Optimization

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Monitoring183Security102AWS71Kubernetes69Terraform62Python60Linux50CI/CD49Ansible47LLM45

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Architecture Review: RAG Retrieval Quality Evaluation
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Architecture Review: RAG Retrieval Quality Evaluation

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GitOps with ArgoCD: Automating Kubernetes Deployments
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GitOps with ArgoCD: Automating Kubernetes Deployments

Learn how to implement GitOps workflows with ArgoCD. Automate Kubernetes deployments using Git as the single source of truth.

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Kubernetes Networking Deep Dive: Understanding Pods, Services, and Ingress
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Kubernetes Networking Deep Dive: Understanding Pods, Services, and Ingress

Master Kubernetes networking concepts including pods, services, ingress controllers, and network policies. Complete guide with practical examples.

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Architecture Review: Prompt Versioning and Regression Testing

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Architecture Review: LLM Gateway Design for Multi-Provider Inference

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Embedding Models Comparison: Choosing the Right Model for Your Use Case
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Compare popular embedding models including OpenAI, Sentence-BERT, and open-source alternatives. Learn which model fits your RAG, search, or similarity tasks.

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Architecture Review: Systemd Service Reliability Patterns
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AI Cost Optimization: Reducing LLM Inference Costs by 80%
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Learn proven strategies to reduce AI inference costs including model quantization, caching, batching, and efficient prompt design. Real-world cost savings examples.

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Architecture Review: Linux Performance Baseline Methodology
••5 months ago

Architecture Review: Linux Performance Baseline Methodology

Linux Performance Baseline Methodology. Practical guidance for reliable, scalable platform operations.

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