Service
AI-Ready AWS Infrastructure
AI projects don't fail because of the model — they fail because of the infrastructure supporting it. A disorganized AWS account with no cost controls or network segmentation multiplies risk and spend from day one. CloudTing puts the foundations in order: IAM, networking, observability, and cost controls, so that when AI workloads arrive, the infrastructure is already ready.
What's included?
- AWS account structure review and organization (Organizations, SCPs)
- IAM hardening: least-privilege roles for AI workloads
- Network segmentation (VPC) to isolate AI workloads from the rest of the environment
- Encryption at rest and in transit for training data and inference
- Baseline observability: CloudWatch with token consumption metrics and per-service costs
- Billing alerts for AWS Bedrock, SageMaker, and S3 (data storage)
- S3 and data services configuration review: access, lifecycle, and costs
Who is this for?
- Teams that want to adopt AI on AWS (Bedrock, SageMaker) but whose account isn't ready yet
- Companies that received an unexpected Bedrock bill and need visibility and control
- CTOs who want to start an AI project with the right controls in place from day one
Our honest scope
CloudTing does not implement AI models. What we do is ensure the infrastructure supporting them is organized, secure, and with costs under control — which is exactly what fails when an AI project starts without preparation. It's our core work, applied to an AI adoption context.
How we work with you
- 30-min initial call to understand your AWS environment and AI adoption goals
- Diagnosis with an AWS Health Check focused on AI workload controls
- Statement of Work (SoW) with scope, deliverables, and SLA, under NDA and a 12-month master agreement
- Sprint-based implementation of prioritized controls
- Ongoing operations with monthly reporting and quarterly reviews
Frequently asked questions
Does CloudTing implement AI models?
No. We specialize in the infrastructure that supports them: organized accounts, secure networks, and cost control. Your team or an ML specialist handles model adoption — we make sure AWS is ready to receive it.
Which AWS AI services do you work with?
We prepare infrastructure for AWS Bedrock, SageMaker, and associated data services such as S3, RDS, and OpenSearch.
How is this different from a standard AWS Health Check?
It focuses specifically on controls that matter for AI workloads: model permissions, per-token cost visibility, training data isolation, and service limits relevant to AI.