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Amazon Bedrock Data Residency, KMS, and Security (AWS Gen AI Series, Part 10)
What AWS can and cannot see in a Bedrock call, where your prompts live at rest, and when a customer managed key is worth the operational weight. A practical walk through residency, KMS, IAM, and invocation logging.
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Amazon Bedrock Private Access with PrivateLink and VPC Endpoints (AWS Gen AI Series, Part 9)
Bedrock traffic leaves your VPC by default. Here is how I wire it shut with PrivateLink interface endpoints, private DNS, and a scoped endpoint policy, plus what it costs per month.
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Amazon Bedrock Regions, Quotas, and Cross-Region Inference (AWS Gen AI Series, Part 8)
Regions decide which models you can call and how much throughput you get. Here is how Bedrock quotas, token burndown, and cross-Region inference profiles fit together, and how to size a quota request that gets approved.
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AWS Trainium and Inferentia vs GPUs, and When Each Wins (AWS Gen AI Series, Part 7)
Trainium and Inferentia are Amazon’s own AI chips, not GPUs. Here is when they beat H100 instances on cost, what the Neuron SDK actually demands, and when to stay on GPU.
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Amazon Bedrock Pricing Across On-Demand, Provisioned, and Batch (AWS Gen AI Series, Part 6)
The five ways Amazon Bedrock charges for the same model, from on-demand tokens to reserved model units, and the break-even math that tells you which mode your workload actually belongs on.
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Amazon Bedrock vs SageMaker AI, and When to Use Each (AWS Gen AI Series, Part 5)
Bedrock gives you models behind an API; SageMaker AI gives you the whole ML platform. Here is how I decide between them, with the cost math that usually settles it.
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Amazon Nova Models and Where Each One Fits (AWS Gen AI Series, Part 4)
A working tour of Amazon Nova on Bedrock: Micro, Lite, Pro and Premier, the creative and speech models, and what Nova 2 changes. With model IDs, context sizes, real cost math and the inference-profile trap that breaks first calls.
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Amazon Bedrock Model Catalog and Choosing a Model (AWS Gen AI Series, Part 3)
Bedrock ships more than a hundred models across fifteen providers, and the price gap between the cheapest and the priciest is over 400x. Here is how I read the catalog and pick a model without overpaying.
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Amazon Bedrock and the Shared Responsibility Model (AWS Gen AI Series, Part 2)
Bedrock is a managed service, but security is still split between AWS and you. Here is exactly which half is yours, the defaults that catch teams out, and the baseline I deploy before any prompt goes live.
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AWS Generative AI Stack, End to End (AWS Gen AI Series, Part 1)
AWS generative AI is really three layers: Amazon Bedrock for managed models, SageMaker AI to build your own, and Trainium and Inferentia underneath. Here is the whole map, with a real cost example and a first call that works.
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Customize Your Shell Environment: bashrc, Aliases, and PATH (Linux for Beginners, Part 23)
Your alias works in the terminal but not in cron because the shell reads different startup files depending on how it launched. Login vs non-login, .bashrc, aliases, PATH, and the prompt.
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Grow Storage With LVM Logical Volumes (Linux for Beginners, Part 22)
Fixed partitions are rigid; LVM puts a flexible layer between disks and filesystems so you can grow a full volume onto a new disk with no downtime. PVs, VGs, LVs, and snapshots.
Architect’s Toolkit
PJ’s Tools
VMware Cloud Foundation
- VCF Documentation
- VCF 9 Planning & Preparation Workbook
- VCF Bill of Materials (BoM)
- VMware Compatibility Guide
- VMware Interoperability Matrix
- VMware Configuration Maximums
- VMware Ports & Protocols
- VMware Hands-on Labs
- RVTools Download
Nutanix
AI & Cloud-Native Platform
- NVIDIA Build (Model Catalog)
- NVIDIA AI Enterprise Reference Architecture
- NVIDIA NIM Performance Benchmarking
- NVIDIA NGC Catalog
- NeMo Microservices Helm Chart
- Helm Charts Repository
- Hugging Face Models
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.
Recommendations & Endorsements
Pranay has very deep technical knowledge and exceptional capabilities. He has strong ability to well-manage complex situations and deliver the customer expectations with high standards of satisfaction. We worked together in one of most complex migration projects and Pranay delivered flawlessly.
Pranay was my VMware Architect on one of our financial accounts at IBM. He was instrumental in documenting and recommending our reference architecture. As a Chief Architect, you are only as good as your team that supports you. Pranay was top notch.
Pranay is proactive, result oriented, responsible and technically sound. Always ready to put all his energy to get the job done. Exceptional troubleshooting skills in Windows Server, virtualization and VMware — a great asset to any company.
Pranay is one of the most talented and dedicated persons I have ever worked with. His vast knowledge from different domains enables him to discuss and help on almost every topic in IT. Needless to say, he is expert of experts in VMware.
DrJha