Cloud Platform
Building enterprise AI on AWS
The broadest service catalog of any major cloud provider. We use AWS where scale, infrastructure flexibility, or a specific AWS service is the deciding factor.
What role does AWS play in an enterprise AI system?
AWS is a strong fit for AI systems that need infrastructure scale, granular control over compute and networking, or access to a specific AWS-native service such as Bedrock for model access or S3-based data pipelines. Divine AI Systems uses AWS as the Cloud & Infrastructure layer where these factors — or an organization's existing AWS footprint — make it the better architectural choice over other cloud providers.
Where It Fits
Where AWS fits in the architecture
AWS's breadth means the architecture is assembled from more individually-chosen services rather than one bundled platform.
Cloud & Infrastructure
- EC2
- Lambda
- S3
- networking
AI & Reasoning
- Amazon Bedrock
- managed multi-provider model access
Knowledge & Data
- S3 data lakes
- retrieval and analytics pipelines
Orchestration & Integration
- Step Functions
- API Gateway
Security, Monitoring & Governance
- IAM
- CloudTrail
- CloudWatch
What We Build
What we build on AWS
The range of what an AWS-based engagement typically includes.
Bedrock-Based AI Applications
Applications drawing on multiple model providers through a single managed service.
Data Pipeline & Lake Architecture
S3-based pipelines feeding retrieval, analytics, and reporting systems.
Serverless Automation
Event-driven, multi-step workflows built on Lambda and Step Functions.
Custom Infrastructure Deployments
AI systems requiring specific compute, networking, or scaling configurations.
When It Makes Sense
When AWS is the right choice
These are the conditions where AWS tends to be the stronger fit.
You need infrastructure scale or granular control
AWS's depth of compute and networking options suits systems with demanding or unusual infrastructure needs.
Your organization already has an AWS footprint
Building on existing AWS accounts, networking, and data avoids duplicating infrastructure elsewhere.
You want managed access to multiple model providers
Bedrock allows switching between model providers without re-architecting the integration layer.
Tradeoffs
What to weigh before committing
These are the considerations we walk through with clients before recommending AWS.
Breadth adds architectural decisions
More service options mean more choices to get right — we scope this deliberately rather than defaulting to familiar services.
Less native fit for Microsoft-centric organizations
Teams standardized on Microsoft 365 or Active Directory often see smoother integration on Azure instead.
Cost visibility requires deliberate monitoring
Granular services mean cost can be harder to track without dedicated tagging and monitoring from day one.
Related Services
Services we typically pair with AWS
Related Solutions
Business problems this often supports
FAQ
Questions about building on AWS
We build on AWS for client engagements. We describe that as technical experience, not a formal AWS partnership or certification.
Both, depending on the project — Bedrock is useful when you want managed access to multiple providers; a direct connection can make sense for a single-provider deployment.
It depends entirely on your existing environment and requirements — we'll recommend based on your specific situation, not a general preference.
Yes — most engagements build within a client's existing AWS organization rather than standing up a separate one.
Already running on AWS?
Tell us what's in your environment today, and we'll show you exactly where AI fits into it.