Tag: Vertex AI
-
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.
-
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.
-
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.
-
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.
-
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.
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.
You May Have Missed
DrJha