-
Vertex AI IAM, CMEK, and Data Governance (Google Cloud Gen AI Series, Part 10)
IAM decides who may call your Vertex AI models, CMEK decides who holds the encryption key, and data governance decides what Google keeps. Here is how to set all three without breaking production.
-
Vertex AI Private Service Connect and VPC Service Controls (Google Cloud Gen AI Series, Part 9)
Private Service Connect gives Vertex AI a private internal path; VPC Service Controls draws the perimeter that stops exfiltration. Here is how they differ and the order to roll them out.
-
Vertex AI Regions, Quotas, and the Global Endpoint (Google Cloud Gen AI Series, Part 8)
How Vertex AI locations, regional versus global endpoints, and Dynamic Shared Quota decide your latency, data residency, and 429 rate, with a clear default and a worked region choice.
-
Cloud TPUs vs GPUs on Google Cloud, and When Each Wins (Google Cloud Gen AI Series, Part 7)
When you train or self-host on Vertex AI, the accelerator choice lands on you. Here is how Cloud TPUs and GPUs really differ, and a plain rule for picking one.
-
Vertex AI Pricing, Provisioned Throughput, and Context Caching (Google Cloud Gen AI Series, Part 6)
A working architect’s breakdown of what you really pay for on Vertex AI: on-demand token rates, Provisioned Throughput and the GSU, context caching, and batch, with a worked cost example you can run.
-
Vertex AI vs the Gemini API in AI Studio, and When to Switch (Google Cloud Gen AI Series, Part 5)
Google AI Studio and Vertex AI are two doors to the same Gemini models. Here is what actually changes between them on auth, data governance, quotas and price, and exactly when to move from one to the other.
-
Third-Party Models in Vertex AI Model Garden, from Claude to Self-Deploy (Google Cloud Gen AI Series, Part 4)
Vertex AI Model Garden runs Claude, Mistral, Grok and more as managed APIs, or as proprietary models you license into your own VPC. Here is how each mode works and when to use it.
-
Gemini Flash vs Pro, and When to Pay for Reasoning (Google Cloud Gen AI Series, Part 3)
Flash or Pro? On Vertex AI the two Gemini tiers differ by about 4x on output tokens. Here is how to pick per request and route only the hard ones to Pro.
-
Vertex AI and Model Garden, from Catalog to Endpoint (Google Cloud Gen AI Series, Part 2)
Vertex AI is Google Cloud’s managed platform and Model Garden is its 200-plus model catalog. Here is how the three access paths, managed API, MaaS, and self-deploy, decide your cost, latency, and data isolation, and which one to start on.
-
Google Cloud Generative AI Stack, End to End (Google Cloud Gen AI Series, Part 1)
A map of the Google Cloud generative AI stack in 2026, from Gemini Enterprise Agent Platform (the service that used to be Vertex AI) down to Ironwood TPUs, and where a real project plugs in.
-
Azure Generative AI vs the Field, the Verdict (Azure Gen AI Series, Part 30)
After twenty-nine parts on the Azure GenAI stack, here is the plain verdict: where Azure wins, where it loses to AWS and Google Cloud, what the bill really looks like, and who should build on it.
-
Reference Architectures for Azure GenAI, from Chatbot to Batch (Azure Gen AI Series, Part 29)
The four architectures almost every Azure GenAI project actually needs, chatbot, retrieval, agentic, and batch, with the baseline Foundry components, real sizing numbers, and when to add an API Management gateway.
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