AWS Bedrock Implementation
AWS Bedrock Implementation
For businesses whose systems already live on AWS.
What this is
Bedrock is AWS's managed access to models from Anthropic, Meta, Mistral, Amazon and others, through one API inside your own AWS account.
It matters for the same reason Foundry does: the model runs against your content inside a boundary you control, with your own identity system and your own logging.
Where a business already runs on AWS, this is normally the shorter route. The account, the network and the permissions model are already there, so the work is connecting a model to them rather than standing up a second cloud estate alongside the first.
What you get
What the work involves
The same shape of engagement as Azure, against AWS primitives.
Account and guardrails
Where Bedrock sits, which accounts may call it, and the service control policy around it.
VPC endpoints
Private connectivity so calls do not traverse the public internet, with routing that works from every subnet that needs it.
IAM, not keys
Roles scoped to what each application actually calls, rather than a long-lived credential in an environment file.
Retrieval over your own storage
Indexing content in your S3 buckets, with the refresh strategy chosen for how that content changes.
Model choice and region
Which models are enabled, in which regions, and what that means for where requests are processed.
Cost and request visibility
Per-application attribution, so spend can be traced to the workload that caused it rather than arriving as one line on the AWS bill.
Guardrails on inputs and outputs
Bedrock's own filtering configured for what this workload should refuse, tested against the things people actually type.
A tested path for model changes
When a model is superseded, the replacement runs against the same evaluation set before anything switches over.
How it works
How the work runs
Look at the account structure
Organisations, accounts, networking and how identity is handled today.
Enable and fence it
Model access, the endpoints, and the policy boundary around them.
Connect the content
Retrieval over the buckets that matter, permissions checked first.
Hand over with the controls on
Logging, cost attribution and a tested path for model changes.
In practice
What gets built on it
The same categories of work, against AWS primitives.
Retrieval over an S3 estate
Content already sitting in buckets, indexed and queryable, with IAM deciding what each caller can reach.
A model behind an existing application
Something already running in the account that needs summarisation, classification or extraction added to it.
Document processing in a pipeline
Extraction as a step in a workflow that already exists, rather than a separate system somebody has to remember to use.
A governed internal endpoint
One place model calls go, with per-application attribution, instead of credentials spread across several services.
What matters
The parts that take the time
Different primitives, the same underlying difficulties.
Model availability varies by region
Not every model is enabled in every region, and that constrains architecture before anything else does.
Throughput is a capacity decision
On-demand and provisioned throughput behave differently under load, and the choice has to match the real request pattern.
IAM is where these projects get stuck
Getting the role boundaries right takes longer than wiring the calls, and it is what makes the deployment defensible afterwards.
Retrieval quality is still the product
Chunking, metadata and refresh cadence decide answer quality here exactly as they do on Azure.
Cost tracks requests, not seats
Spend is driven by tokens. Without per-application attribution nobody can tell which workload is responsible.
Non-deterministic output still needs testing
The evaluation harness is platform-independent and just as necessary.
Who it is for
When Bedrock is the right answer
The estate is already on AWS
The account structure, the network and IAM are in place.
The data is already in S3
Retrieval works against storage that already exists.
There is an AWS-native application
Something already running that the model needs to sit alongside.
A specific model is required
A model available on Bedrock that the workload has been built around.
Questions
Frequently asked questions
Should we use Bedrock or Microsoft Foundry?
Where your estate already is decides it. AWS-native businesses go to Bedrock; Microsoft 365 businesses go to Foundry. Both run inside an account you own.
Does our content leave our AWS account?
The content stays in your storage. Requests go to the Bedrock service inside your account boundary, and with VPC endpoints they do not traverse the public internet.
Which models are available?
Anthropic, Meta, Mistral and Amazon's own, varying by region. Availability gets checked against your region before anything is designed.
Can you work with our existing AWS team?
Yes. Much of this work is done alongside an in-house team that owns the account and wants the AI portion built properly.
How is spend controlled?
Cost is attributed per application and reported, so the workload driving the spend is identifiable.
Do you run both platforms?
Yes. Several clients have Microsoft 365 for their people and AWS for their product, and the work spans both.
Also on this site