What does it cost to run a model on AWS?

It depends on the approach. A managed model on Bedrock is billed per use and suits variable traffic. Self-hosting on GPUs pays off above a certain load but needs careful cost attention. Both options are estimated during planning.

Will our data stay private?

With Bedrock inside your own AWS account, requests are not used to train models and can be routed over a private network. With self-hosting, the data never leaves your account in the first place.

Yes, and it is the sensible route: one endpoint, a defined budget and measured cost per request before expanding.

Do you build the model itself?

No. Our work is the infrastructure around the model: deployment, security, scaling, monitoring and cost.

AI workloads on AWS

The AI part of your product needs infrastructure like everything else: secure access to models, compute that matches the load, somewhere to keep vectors, and visibility into what each request costs. That is what we build — as code, in your own account.

What the service covers

Model access

Amazon Bedrock with precise permissions, private access through your VPC and full usage logging.

Serving and hosting

SageMaker endpoints or containerised inference services, with scaling that follows load.

GPU capacity

GPU nodes on EKS that come up when needed and shut down when not — the most expensive line on the bill.

Vector stores and RAG

storage and retrieval for semantic search, including the pipeline that keeps documents current.

Cost control

budgets, per-usage measurement and alerts, because AI bills climb quickly.

Security and privacy

data separation, encryption, and control over what leaves the organisation and what stays inside.

What this is not

This is not model training or data science. We do not build the model — we build the infrastructure that runs it reliably, securely and at a predictable cost. If you need researchers, we will say so plainly.

How it works

1Understand the case — which model, expected traffic, what data and how sensitive it is.
2Design and cost — architecture and a monthly estimate, before any code is written.
3Build as code — Terraform for the whole stack, including permissions, networking and monitoring.
4Load testing — real measurement of response times and cost per request.
5Handover — documentation, training and optional ongoing support.

AWS cloud services
Kubernetes — the base for running GPUs at scale.
Cost optimization — especially critical for AI workloads.

Frequently asked questions

What does it cost to run a model on AWS?: It depends on the approach. A managed model on Bedrock is billed per use and suits variable traffic. Self-hosting on GPUs pays off above a certain load but needs careful cost attention. Both options are estimated during planning.
Will our data stay private?: With Bedrock inside your own AWS account, requests are not used to train models and can be routed over a private network. With self-hosting, the data never leaves your account in the first place.
Can we start small?: Yes, and it is the sensible route: one endpoint, a defined budget and measured cost per request before expanding.
Do you build the model itself?: No. Our work is the infrastructure around the model: deployment, security, scaling, monitoring and cost.

Tell me what you are running today

A short call, then a written plan with a cost estimate and a fixed scope. No obligation.

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