aws

LLM Inference on AWS: Every Option Explained

AWS gives you two fundamentally different ways to run an LLM -SageMaker, you provision and pay for the infrastructure that serves the model. Bedrock, AWS already runs the model, and you just call

How to Build RAG on Your S3 Documents with Bedrock

If your documents already sit in Amazon S3 and you want to ask questions of them, the shortest path on AWS is Amazon Bedrock Managed Knowledge Base: point it at the bucket, and it handles parsing,

S3 Vectors vs OpenSearch Serverless for RAG on AWS

This is a choice you only face on the customer-managed path — if you have not ruled out the fully managed option yet, [building RAG on your S3 documents with Bedrock](/blog/rag-on-s3-documents-with-be

What an AI Agent Costs Per Conversation on AgentCore

You can read AgentCore's per-service rates straight off the AWS pricing page. What that page can't tell you — and what you actually need before you build a business on agents — is what one of your u

When Is Self-Hosting an LLM Cheaper Than Bedrock?

Two questions send teams down this path: "our Bedrock bill is growing, should we run this on our own GPU?" and "we fine-tuned a Llama, where does it go?" For most teams the answer to both is no, a

Bedrock Agents vs AgentCore: What to Use Now

Updated 2 September 2026: Amazon Bedrock Agents Classic moved to maintenance mode in June 2026. This post has been rewritten around the options that are actually available now. **Amazon Bedrock Age

What Is Amazon Bedrock AgentCore? (And When to Use It)

Amazon Bedrock AgentCore is a managed platform for deploying and operating AI agents you've already built — in any framework, with any model — without managing the runtime, memory, identity, or observ

Model Evals: How to Know If You Can Use a Cheaper Model

An eval, in the AI FinOps context, is a structured comparison: run a representative sample of real production inputs through your current model and a cheaper candidate, score both against a defined qu

What Is AI FinOps?

AI FinOps is the practice of making AI workload costs visible, attributable, and optimizable — applied to the specific economics of model inference, where the unit of cost is the token, not the instan

Deploying Engineering Resource Management Knowledge Graph on AWS

Resource planning in engineering orgs is a multi-hop problem. The data is there — skills, project history, availability — it's just stored in flat tables that you need to join on demand. This post wal

Connect Claude Code to Live AWS Tools with the Agent Toolkit

AI coding agents are getting remarkably capable — but they have a blind spot. The models powering them were trained on data that's months or years old. When you ask your agent about Amazon S3 Tables,

Stretch Your Claude Code Budget with Bedrock Prompt Caching

Anthropic recently tightened usage limits on Claude Code — and if you're doing serious development work, you feel it. Long refactoring sessions, codebase-wide architecture questions, iterative debuggi

AWS Bedrock vs SageMaker: How to Pick the Right One

If you're building an AI product on AWS, you'll hit this question early: Bedrock or SageMaker? The short answer is that they solve different problems, and most startups only need one. What Each Se

AWS Bedrock Cost Structure: What You're Actually Paying For

AWS Bedrock looks simple from the outside — call an API, get a response, pay per token. The reality is that a production Bedrock setup has several distinct cost layers, and they behave very differentl

Why Your AWS Bedrock Bill Makes No Sense (And How to Fix It)

When a startup says "our AWS bill is too high," the conversation almost always starts at the aggregate level — total monthly spend, a few large services, maybe a spike someone noticed. That's not wher

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