153. LLM Inference with Bedrock

153. LLM Inference with Bedrock

From AWS Bites by AWS Bites

March 6, 2026 · 43 min · Season 1 · Episode 153

About this episode

This episode discusses building reliable AI-powered applications using Amazon Bedrock for LLM inference, focusing on practical challenges and structured outputs.

If you’re curious about building with LLMs, but you want to skip the hype and learn what it takes to ship something reliable in production, this episode is for you.We share our real-world experience building AI-powered apps and the gotchas you hit after the demo: tokens and cost, quotas and throttling, IAM and access friction, marketplace subscriptions, and structured outputs that do not break your JSON parser.We focus on Amazon Bedrock as AWS’s managed inference layer: how to get started with the current access model, how to choose models, how pricing works, and what to watch for in production.We also go deep on structured outputs: constrained decoding, schema design that improves output quality, and how to avoid “grammar compilation timed out”. In this episode, we mentioned the following resources: fourTheorem: Bedrock structured outputs guide https://fourtheorem.com/amazon-bedrock-structured-outputs/ Amazon Bedrock https://aws.amazon.com/bedrock/ Bedrock docs https://docs.aws.amazon.com/bedrock/latest/userguide/ Bedrock pricing https://aws.amazon.com/bedrock/pricing/ Structured outputs https://docs.aws.amazon.com/bedrock/latest/userguide/structured-outputs.html Cross-region…

Topics covered

  • LLM Inference
  • Amazon Bedrock
  • AI-powered apps
  • production challenges
  • structured outputs
  • pricing
  • model selection

Keywords

  • LLM
  • inference
  • Amazon Bedrock
  • production
  • structured outputs
  • pricing
  • tokens
  • IAM
  • throttling

Mentioned in this episode

Organizations: Amazon Bedrock, fourTheorem, AWS

More episodes of AWS Bites

Explore listener stats, chart rankings, contacts and more on the AWS Bites podcast page.