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Amazon Q Business and Amazon Q Developer, Explained for Builders (AWS Gen AI Series, Part 22)
Amazon Q is two products under one name. Here is what Q Business and Q Developer each do in 2026, what they cost, and why Q Developer is moving into Kiro.
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Amazon Bedrock Multi-Agent Collaboration, Supervisor and Collaborator Agents (AWS Gen AI Series, Part 21)
One supervisor agent, a few specialist collaborators, and a hard step budget. How multi-agent collaboration works on Amazon Bedrock in 2026, what it costs in latency and tokens, and why the Agents Classic cutoff changes where you should build.
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Amazon SageMaker HyperPod for Resilient Model Training (AWS Gen AI Series, Part 19)
How Amazon SageMaker HyperPod runs resilient GPU and Trainium clusters for multi-week training: Slurm versus EKS, automatic node recovery, checkpointless training, task governance, and what the cluster actually costs.
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Amazon SageMaker JumpStart, Foundation Models and Private Hubs (AWS Gen AI Series, Part 18)
SageMaker JumpStart gives you open-weight and proprietary foundation models on your own SageMaker endpoint. Here is how it differs from Bedrock, what it costs, and when the instance bill is worth it.
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Amazon Bedrock Model Distillation, End to End (AWS Gen AI Series, Part 17)
Amazon Bedrock Model Distillation trains a small student model to answer like a big teacher for a narrow task. Here is how the job runs, which model pairs are allowed, and why Provisioned Throughput, not the training, decides the cost.
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Amazon Bedrock Fine-Tuning and Continued Pre-Training (AWS Gen AI Series, Part 16)
When a bigger prompt stops paying off, you change the model itself. A practical walk through fine-tuning and continued pre-training on Amazon Bedrock: which models qualify, what a job costs, and how Nova on-demand hosting changed the math.
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Amazon Bedrock Prompt Management, Flows, and Prompt Caching (AWS Gen AI Series, Part 15)
Prompt caching, Prompt Management, and Bedrock Flows get grouped together and confused constantly. What each one does, what caching actually saves, and which to reach for.
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Amazon Bedrock Guardrails, Content Filters, and Grounding Checks (AWS Gen AI Series, Part 14)
Amazon Bedrock Guardrails inspects text into and out of a model across six policies. Where each fits, how to call it inline and standalone, what it costs, and where it trips you in production.
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Amazon Bedrock Agents, Action Groups, and the AgentCore Shift (AWS Gen AI Series, Part 13)
How Bedrock agents turn one question into several model calls, how action groups and return of control work, what a request really costs, and why new builds now start on AgentCore.
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Amazon Bedrock Knowledge Bases and Managed RAG, End to End (AWS Gen AI Series, Part 12)
How Amazon Bedrock Knowledge Bases turn your documents into retrieval augmented generation, which vector store and chunking to pick, and why the bill is dominated by a vector-store floor, not tokens.
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Amazon Bedrock Converse API vs InvokeModel, and When to Use Each (AWS Gen AI Series, Part 11)
InvokeModel hands you each model’s native JSON. The Converse API gives one request and one response shape for every chat model on Bedrock. Here is when to use each, and where InvokeModel is still the only door.
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