Does AWS Bedrock keep our data private?

Under enterprise terms your prompts and completions are not used to train the underlying models, and you control region and can reach the service over private networking within your VPC. That satisfies a great many regulated workloads. It is not the same as the data staying inside infrastructure you own and operate, and whether that difference matters depends on what your obligations actually require rather than on how the isolation is described.

What is Bedrock's main advantage over self-hosting?

Model choice without operational burden. You can route different workloads to different model families through one interface, inside networking you already control, without procuring hardware or staffing an on-call rota for a serving stack. For organizations already operating in AWS, the integration with existing identity, logging and networking is a substantial and often underrated benefit.

When does on-premise become necessary?

When an obligation requires the data to remain inside a boundary you control, when the workload must run without external connectivity, or when you must reproduce a specific output months later and cannot depend on a hosted model version remaining available. Absent one of those, on-premise is usually a more expensive route to a similar result.

Yes, and it is the common outcome. Bedrock for the general case, an on-premise deployment sized only for the workload classes that genuinely require it. This needs an abstraction layer so routing is configuration rather than a rewrite, and a written classification rule telling developers which path a workload takes.

How should we size an on-premise deployment alongside Bedrock?

For the subset that requires it, not for total volume. The workloads that genuinely cannot use a hosted service are usually a minority of requests and a majority of risk. Sizing for everything means paying the fixed cost of owned capacity for work that never needed to leave the hosted service.

AWS Bedrock vs On-Premise: Choosing Per Workload

Bedrock occupies a useful middle position that gets flattened in most private versus public comparisons. It is a hosted service, so it is not private deployment. But it runs inside a cloud you already operate, reachable over private networking, with the identity and logging integration you already have.

For a large share of enterprise workloads that is enough. The question worth answering carefully is which workloads it is not enough for.

What Bedrock gives you

Model choice through one interface. Different workloads have different requirements, and being able to route them to different model families without running several integrations is a real architectural benefit. It also reduces the cost of being wrong about a model choice.

Integration you already have. Private networking into your VPC, existing identity and access management, logging into the tooling your security team already watches. A private deployment that nobody wired properly into your access controls is worse than a hosted service that is wired correctly, and that is a more common outcome than it should be.

No operational burden. No procurement, no capacity management, no serving stack to patch, no on-call rota for inference infrastructure.

What it does not give you

The same three things that no hosted service can, regardless of provider:

Data inside a boundary you own. Private networking controls the path. It does not change the fact that the service is operated by a third party on infrastructure you do not control. If an obligation requires the data to stay inside your boundary, network isolation does not satisfy it. Whether your obligation says that is a question for legal and compliance rather than architecture.

Model version control. Hosted providers deprecate versions on their own schedule. If you need to explain months later why a system produced a particular output, the version that produced it has to still exist. In regulated decisioning this requirement alone frequently decides the question.

Operation without connectivity. If the workload must function during an outage or in an isolated environment, a hosted service cannot serve it.

The decision, per workload class

The framing that produces good answers is per workload class rather than per organization:

Does a law, regulation, accreditation or executed customer contract require the data to stay inside a boundary you control?
Must the workload function without external connectivity?
Do you need to reproduce a specific output months later for audit?
Would exposure breach privilege or confidentiality in a way contract terms do not adequately transfer?

Any yes points at on-premise for that class. All no, and Bedrock is very likely the faster, cheaper and better-integrated answer.

Sizing the private side correctly

This is where organizations lose money.

Having established that some workloads must be on-premise, the instinct is to size the deployment for total volume so everything can move eventually. That is usually wrong. The workloads that genuinely require private deployment tend to be a minority of requests and a majority of risk.

Size for that subset. You pay the fixed cost of owned capacity only for the work that needed it, and the hosted service continues to absorb everything that scales.

Utilisation is the reason this matters. Owned hardware costs the same idle, so sizing for a volume you route elsewhere anyway is straightforwardly wasted capital.

Making both work together

Put an abstraction layer in front of both before you need it, so routing is configuration rather than a rewrite. Build an evaluation set that runs against either backend, because it is the only way to know whether a routing change helped or hurt.

Then write the classification rule. One page telling a developer which path a given workload takes, based on data class rather than on convenience. Without it, work drifts to whichever path is easier to reach, and you end up carrying the cost of two systems while inheriting the compliance posture of the weaker one.

Where to go next

The same framework applied to other providers is in Azure OpenAI vs self-hosted and Private LLM vs ChatGPT Enterprise. For the on-premise build itself see the deployment architecture guide.