Is the AWS Bedrock Academy free?

Yes. The full course is free and includes 25 modules, 52 lessons, 16 labs, and quiz-based progression with no sign-up required.

How is Infinity AWS Bedrock Academy different from AWS Skill Builder?

Unlike AWS Skill Builder which often gates advanced hands-on labs behind paid subscriptions or AWS Builder ID logins, Infinity Tech Stack's AWS Bedrock Academy is 100% free, ungated, and browser-first with zero sign-up required. It includes 25 modules, 52 lessons, 16 hands-on labs, and 125+ quiz checks covering the Converse API, Bedrock Guardrails, AgentCore, and multi-model evaluations without requiring active AWS account credentials.

Who is this course for?

Developers, architects, and platform teams building AI systems on AWS Bedrock from MVP to production scale.

What does this Bedrock course cover?

The academy covers runtime inference, prompt engineering, guardrails, knowledge bases, agents, evaluation, observability, security, compliance, resilience, incident response, and production launch discipline.

Escalation to stronger model tiers should be triggered by:

Policy and quality thresholds — Threshold-based escalation keeps cost controlled while protecting quality on hard tasks.

Choosing Models by Workload Tutorial | Model Selection Strategy

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1. Choosing Models by Workload2. A/B Testing and Rollout Gates

Choosing Models by Workload

📚 Model Selection Strategy⏱ 11 min⭐ 85 XP⌨ Hands-on lab

One Model Is Rarely Optimal

Bedrock hosts 100+ foundation models across Amazon, Anthropic, Meta, Mistral, DeepSeek, Moonshot AI (Kimi), MiniMax, Google DeepMind (Gemma), xAI (Grok), and OpenAI. Production systems route requests to different model tiers based on risk and required quality - paying frontier prices for trivial tasks is the most common AI budget leak.

A Practical Routing Matrix

Workload	Risk/complexity	Model tier	Why
Summarization, tagging, classification	Low	Amazon Nova Micro/Lite, Claude Haiku class	Fast, cheap, quality ceiling irrelevant here
Customer-facing drafting, RAG answers	Medium	Nova Pro, Claude Sonnet class	Balanced quality/cost at volume
Complex reasoning, agentic coding, multi-step plans	High	Claude Opus/Sonnet frontier, Nova Premier, OpenAI frontier via Responses API	Capability is the bottleneck, not price
Open-weight / self-hostable mandates	Varies	DeepSeek, Meta Llama, Mistral, GPT-OSS	Licensing and portability requirements
Regulated decisions (credit, medical, legal)	Critical	Strongest tier + guardrails + human gates	Audit trail matters more than cost

Routing Mechanics

Static routing - endpoint/task type maps to a model in config. Simple, predictable, covers most systems.
Dynamic routing - a cheap classifier scores request complexity, then dispatches. Adds cost/latency; justify with traffic diversity.
Escalation routing - try the cheap tier; if confidence/validation fails, retry on the premium tier. Great when most requests are easy.
Intelligent Prompt Routing (Bedrock feature) - managed routing between models in a family based on predicted response quality per prompt.

Keep the matrix versioned: the model space shifts fast - review your routing matrix quarterly with fresh eval scores. Last year's "best value" tier is often this year's overpriced one.

⌨ HANDS-ON LABBuild a Routing Matrix

⭐ +150 XP

Map support, analytics, coding, and compliance tasks to model families.

1Create a workload-to-model matrix file.

2Add an escalation rule.

lab-sandbox — simulated environment

INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$

OBJECTIVE 1 / 2 — type "hint" if stuck

🧪 Knowledge Check

Press 1-4 to select1 of 2

Why do mature teams use model routing?

Because one model is always best

To optimize quality-latency-cost across workloads

To avoid testing

To remove telemetry