Tag: Google Cloud Gen AI Series
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Gemma Open Models on Vertex AI, from Model Garden to Endpoint (Google Cloud Gen AI Series, Part 18)
When a managed Gemini bill stops making sense, Gemma is the open model you host yourself on Vertex AI. Here is the 2026 lineup, the deploy path through Model Garden, and the volume where self-hosting actually pays.
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Distilling Gemini on Vertex AI, from Teacher to Student Model (Google Cloud Gen AI Series, Part 17)
Distillation trains a small Gemini student to copy a large teacher, so the teacher writes the labels and you serve the result at Flash prices. When it pays, what it costs, and where it fails.
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Fine-Tuning Gemini with Supervised Tuning on Vertex AI (Google Cloud Gen AI Series, Part 16)
Supervised fine-tuning on Vertex AI adjusts Gemini to your task with a few hundred labelled examples, and because it uses LoRA the tuned model costs the same to run as the base. Here is when to tune, how to build the dataset, the knobs that matter, and what it costs.
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Grounding Gemini with Google Search and Your Own Data (Google Cloud Gen AI Series, Part 15)
How to ground Gemini on Vertex AI against the live web and your own Vertex AI Search data store, read the grounding metadata, render citations correctly, and size what it costs.
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Vertex AI Safety Filters and Model Armor (Google Cloud Gen AI Series, Part 14)
Gemini safety filters and Model Armor are two separate layers on Vertex AI. Here is what each one catches, why the built-in filters default to off, and the configuration I would actually run in production.
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Vertex AI Agent Builder and the ADK, From Local Agent to Managed Runtime (Google Cloud Gen AI Series, Part 13)
Build an agent in code with the ADK, deploy it to the managed runtime, and see what the always-on replica really costs before your first user shows up.
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Vertex AI Search and RAG Engine, from Data Store to Grounded Answer (Google Cloud Gen AI Series, Part 12)
Vertex AI Search gives you managed retrieval with almost no plumbing, while RAG Engine hands you the chunking, embeddings, and vector backend. Here is when each one wins.
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Calling Gemini Models with the API, Streaming, and Function Calling (Google Cloud Gen AI Series, Part 11)
The legacy Vertex AI generative SDK was removed in June 2026, so every Gemini call now goes through one library. Here is how to call a model, stream the answer, and wire function calling without letting it run away with your latency.
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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.
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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.
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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.
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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.
Architect’s Toolkit
PJ’s Tools
VMware Cloud Foundation
- VCF Documentation
- VCF 9 Planning & Preparation Workbook
- VCF Bill of Materials (BoM)
- VMware Compatibility Guide
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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.
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