Tag: Cost Optimization
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How to Reduce LLM API Costs, From Prompt Hygiene to Self Hosting
A practical, beginner to expert guide to cutting LLM API costs: token math, prompt and context hygiene, caching and batching, model routing, and when self hosting actually pays off.
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FinOps for AI and GPU Spend, Tokens and Utilisation (Cloud FinOps Series, Part 17)
A GPU sitting at 30 percent utilisation costs more per useful hour than the provider you rejected for being expensive. Here is how AI spend actually meters, and which levers move it.
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Serverless and Managed Service Cost on AWS, Azure and GCP (Cloud FinOps Series, Part 16)
Two near identical functions, twenty times the cost. Billing granularity, not the published rate, decides what short serverless functions cost, and concurrency moves the number further than any platform choice.
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Kubernetes Cost Allocation and Container FinOps (Cloud FinOps Series, Part 15)
A Kubernetes bill arrives as one enormous compute line with no team names on it. How asset, workload, idle and overhead costs actually split, what the control plane really costs on EKS, GKE and AKS, and who should be charged for idle.
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Cloud Storage and Data Transfer Costs, Where the Money Actually Hides (Cloud FinOps Series, Part 14)
A storage class has four prices, not one, and the cheap-looking tier is often the expensive choice for small objects. How minimum durations, minimum billable object sizes, retrieval fees and data transfer boundaries really work on AWS, Azure and Google Cloud.
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Spot and Interruptible Capacity Without Losing Work (Cloud FinOps Series, Part 13)
Spot capacity is the deepest discount in cloud and the only one you have to earn with engineering. How interruption works on AWS, Azure and Google Cloud, which workloads can take it, and why the savings curve flattens long before 90 percent spot.
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Rightsizing Cloud Compute Without Breaking Production (Cloud FinOps Series, Part 11)
Rightsizing is the first cost lever most teams pull and the one most often aimed at the wrong workload. How the AWS, Azure and GCP recommendation engines actually decide, where they are blind, and how to act on them safely.
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Vertex AI Cost Governance and FinOps (Google Cloud Gen AI Series, Part 26)
Read a Vertex AI bill line by line, then cut it with model tiering, context caching, Batch mode, and provisioned throughput judged against a real break-even. With budgets, billing export, and label-based attribution.
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Amazon Bedrock Cost Governance and FinOps on AWS (AWS Gen AI Series, Part 26)
Bedrock cost is driven by tokens, model choice, and inference tier. Here is how I attribute spend by team, cut it with batch and prompt caching, and put budgets and anomaly alerts around it before the bill surprises anyone.
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VCF 9 Performance Tuning vs Cost Optimization: Where to Spend Your Effort (VCF 9 Series, Part 35)
Performance tuning and cost optimization in VCF 9 pull in opposite directions. Here is which levers help which goal, where they collide, and the order I run them in on real clusters.
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Architecture & Design
About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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