Category: Tech Notes
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watsonx.ai Regions, Private Connectivity, and Security (IBM Gen AI Series, Part 7)
The watsonx.ai region you pick is a one-way door, and it quietly decides which models, tuning, and compliance you get. Here is how to choose it, then lock the network with private endpoints and your own keys.
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GPUs and Running watsonx.ai on OpenShift (IBM Gen AI Series, Part 6)
How watsonx.ai actually uses GPUs on Red Hat OpenShift: the operators that expose a card, why memory sizing decides everything, the single node rule for multi GPU models, and when MIG or time slicing is worth it.
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watsonx.ai Pricing, Resource Units, CUH, and the Plan Tiers (IBM Gen AI Series, Part 5)
watsonx.ai bills two meters at once, Capacity Unit Hours for compute and Resource Units for inference. Here is what each of the four SaaS plans costs, and the point where Essentials stops being the cheaper choice.
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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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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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