Category: Tech Notes
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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.
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Amazon Bedrock Data Residency, KMS, and Security (AWS Gen AI Series, Part 10)
What AWS can and cannot see in a Bedrock call, where your prompts live at rest, and when a customer managed key is worth the operational weight. A practical walk through residency, KMS, IAM, and invocation logging.
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Amazon Bedrock Private Access with PrivateLink and VPC Endpoints (AWS Gen AI Series, Part 9)
Bedrock traffic leaves your VPC by default. Here is how I wire it shut with PrivateLink interface endpoints, private DNS, and a scoped endpoint policy, plus what it costs per month.
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Amazon Bedrock Regions, Quotas, and Cross-Region Inference (AWS Gen AI Series, Part 8)
Regions decide which models you can call and how much throughput you get. Here is how Bedrock quotas, token burndown, and cross-Region inference profiles fit together, and how to size a quota request that gets approved.
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AWS Trainium and Inferentia vs GPUs, and When Each Wins (AWS Gen AI Series, Part 7)
Trainium and Inferentia are Amazon’s own AI chips, not GPUs. Here is when they beat H100 instances on cost, what the Neuron SDK actually demands, and when to stay on GPU.
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Amazon Bedrock Pricing Across On-Demand, Provisioned, and Batch (AWS Gen AI Series, Part 6)
The five ways Amazon Bedrock charges for the same model, from on-demand tokens to reserved model units, and the break-even math that tells you which mode your workload actually belongs on.
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Amazon Bedrock vs SageMaker AI, and When to Use Each (AWS Gen AI Series, Part 5)
Bedrock gives you models behind an API; SageMaker AI gives you the whole ML platform. Here is how I decide between them, with the cost math that usually settles it.
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Amazon Nova Models and Where Each One Fits (AWS Gen AI Series, Part 4)
A working tour of Amazon Nova on Bedrock: Micro, Lite, Pro and Premier, the creative and speech models, and what Nova 2 changes. With model IDs, context sizes, real cost math and the inference-profile trap that breaks first calls.
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.
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