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Integration brief AWS BedrockAI and LLM Providers

Several Model Families Inside Your Own AWS Account

Bedrock puts Anthropic, Meta, Mistral, Cohere and Amazon's own models behind one AWS-native API. For an organization whose procurement and data-residency commitments are to AWS rather than Azure, that matters more than any individual model: a model change becomes an AWS decision rather than a new vendor relationship, a new contract and a new security review.

What you can do together

  • Reach several model families through one provider, so changing model is a configuration change inside an existing AWS relationship.
  • Keep inference in the AWS region and account your commitments require, including GovCloud.
  • Scope what the platform may invoke through the IAM role rather than granting broadly.
  • Run Bedrock for one agent and a self-hosted model for another, where material differs in sensitivity.

How it connects

The model behind an agent or a node is configuration. Bedrock is selected per deployment or per agent, and both the Bedrock and the Bedrock Converse interfaces are carried.

Inference runs in the AWS region and account you point it at, under the IAM role the connection is configured with. That role is the boundary: what the platform can invoke is what the role is granted, and scoping it is the control rather than relying on who is asking.

GovCloud is the case worth naming. Where a federal or state buyer requires an AWS GovCloud boundary, Bedrock model availability in that partition is narrower than in the commercial regions and differs by model family, so an engagement confirms which models are actually available in the target region before a workflow is designed around one. That is an AWS fact rather than ours, and it changes designs late if it is discovered late.

VIDIZMO and AWS Bedrock · Integration briefPage 1 of 2
How it works AWS BedrockAI and LLM Providers
Nexus your video, audio, images and documents, indexed under your access rules content the asking user may see AI Intelligence Hub agents, chat, workflows, retrieval with citations; model chosen per agent prompts and embeddings over API AWS Bedrock model endpoint, hosted by the provider or on your own servers answers Users chat, search, workflow output AWS Bedrock VIDIZMO

A scenario

  1. ConfirmationThe engagement establishes which model families are available in the GovCloud region, because that list is shorter than the commercial one.
  2. SetupAn IAM role scoped to the chosen models is configured as the provider in AI Intelligence Hub.
  3. In useAnswers are generated inside the contractor's own AWS account and region, with retrieval scoped to the asking user.
  4. A model changeA newer family becomes available in the partition; the provider setting changes and the workflows do not.
  5. An auditThe role and the region are the evidence of where inference happened.

What stays where

AWS remains the model platform

Model access, regions, quotas and the commercial relationship stay in Bedrock.

The IAM role is the boundary

What the platform may invoke is what that role is granted.

GovCloud availability is confirmed, not assumed

Model families differ by partition, and that is established before a design depends on one.

Content governance does not move

Retrieval runs under the asking user's identity whichever provider generates the answer.

Products and solutions

Next step

See it on your own AWS Bedrock instance.

We will show the connection made, the data moving and the output, then size it for your deployment.

Contact VIDIZMO

sales@vidizmo.ai

+1 571-969-2180

vidizmo.ai

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