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watsonx.ai Deployment Options, From SaaS to Software on OpenShift (IBM Gen AI Series, Part 4)
watsonx.ai runs two ways: as a Service that IBM operates for you, or as software you run on Red Hat OpenShift. Here is how to pick, and what you own in each.
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IBM Granite Models, Third-Party Models, and Licensing on watsonx (IBM Gen AI Series, Part 3)
A plain walk through the IBM Granite 4.1 family, the third-party models sitting beside it in watsonx.ai, and why Apache 2.0 and uncapped IP indemnity, not benchmarks, usually decide which model an enterprise ships.
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watsonx.ai Studio and Prompt Lab, Your First Governed Prompt (IBM Gen AI Series, Part 2)
The watsonx.ai studio and Prompt Lab, hands on: projects and the Runtime service, Chat mode after the 2026 removal of Structured and Freeform, decoding parameters, and how prompt length drives your token bill.
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IBM watsonx Generative AI Stack, End to End (IBM Gen AI Series, Part 1)
IBM watsonx explained end to end for beginners: how watsonx.ai, watsonx.data, and watsonx.governance fit together, where the Granite models sit, and what it costs to run.
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Google Cloud Generative AI vs the Field, the Verdict (Google Cloud Gen AI Series, Part 30)
The closing scorecard on Google Cloud generative AI. Where Vertex AI and Gemini win, where they fall short, and how I would choose against AWS Bedrock and Azure AI Foundry in 2026.
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Reference Architectures on Google Cloud, from Chatbot to Batch (Google Cloud Gen AI Series, Part 29)
Most generative AI on Google Cloud reduces to four shapes: a direct call, RAG, an agent, and batch. Here is how each maps to managed services, what it costs, and which one to reach for first.
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Vertex AI Pipelines for LLMOps, from Notebook to Nightly Retrain (Google Cloud Gen AI Series, Part 28)
How I turn a Gemini tuning notebook into a Vertex AI pipeline that reruns nightly, gates on evals, registers versions, and does not quietly ship a worse model.
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Vertex AI Responsible AI, Governance, and Audit Logging (Google Cloud Gen AI Series, Part 27)
Data Access audit logs, SynthID provenance, Model Registry, and Access Transparency, wired into a governance baseline for a Gemini workload on Vertex AI. What is on by default, what you have to switch on, and what it costs.
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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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Vertex AI Observability and Tracing, from Dashboard to Span (Google Cloud Gen AI Series, Part 25)
A green status code on an eight second request tells you nothing. Here is how Cloud Monitoring, Cloud Trace, and Cloud Logging on Vertex AI tell you which call was slow, what it cost, and what to never log.
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Multimodal on Vertex AI, from Nano Banana to Veo and Lyria (Google Cloud Gen AI Series, Part 24)
A field guide to generative media on Vertex AI: when to use Gemini native image generation, Imagen, Veo, Lyria, and Chirp, with real model IDs and a per-image cost model.
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Vertex AI Gen AI Evaluation Service, Pointwise to Trajectory (Google Cloud Gen AI Series, Part 23)
The Vertex AI Gen AI evaluation service turns a two-example spot check into a number you can gate a release on. How pointwise, pairwise, rubric, and trajectory metrics work, and how to validate the autorater judge before you trust it.
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