Building Intelligent Systems on AWS

The AWS ecosystem has matured into one of the most comprehensive cloud platforms available today, spanning infrastructure, data, AI/ML, security, and developer tooling. For engineering teams building AI-native products, it offers a depth of services that, when composed well, can power everything from real-time inference pipelines to petabyte-scale data platforms. This article explores the key AWS capability areas relevant to modern product engineering, and where a consulting partner adds genuine technical value.

Before any AI capability can be layered in, most enterprises face a more fundamental challenge: their existing workloads are running on aging infrastructure, monolithic codebases, or on-premises systems that were never designed for elastic scale. Migration to AWS is not simply a lift-and-shift exercise. Done well, it is an opportunity to re-platform, re-architect, and retire technical debt that has accumulated over years.

AWS structures migration through the 7Rs framework: Retire, Retain, Rehost, Relocate, Replatform, Repurchase, and Refactor. Each strategy carries different cost, risk, and value profiles.

Key AWS tooling for migration includes AWS Migration Hub, which provides a central dashboard for tracking workload migrations. AWS Application Migration Service (MGN) handles server replication and cutover for lift-and-shift scenarios. For database migrations, AWS Database Migration Service (DMS) supports homogeneous and heterogeneous migrations, including Oracle-to-Aurora and SQL Server-to-PostgreSQL paths, with minimal downtime using change data capture (CDC).

Modernization typically involves decomposing monoliths into containerized microservices on Amazon EKS or ECS, adopting event-driven patterns via Amazon EventBridge, and replacing self-managed databases with Aurora, DynamoDB, or ElastiCache. The payoff is architectural: systems designed around managed services are inherently easier to scale, observe, and evolve.

AWS is no longer just an infrastructure provider. With over 200 fully featured services, it has become the substrate on which modern intelligent systems are built. For AI-native companies, the platform offers a tightly integrated stack: compute optimized for ML workloads, managed data services, a fully featured MLOps layer, and a growing suite of generative AI tooling. The challenge is not access. It is knowing which services to compose, how to architect them for scale, and where to avoid over-engineering.

At the infrastructure layer, AWS offers a range of compute options purpose-built for different workload profiles.

• EC2 GPU Instances (P4, P5, G5)

Commonly used for model training and large-scale batch inference. NVIDIA A100 and H100 GPUs are available via EC2 UltraClusters, connected through 400 Gbps Elastic Fabric Adapter (EFA) networking, which is critical for distributed training jobs that would otherwise bottleneck on inter-node communication.

• AWS Inferentia2 and Trainium

Optimized for transformer-based model inference. Inferentia2 chips, accessible via inf2 instances, can deliver significantly lower cost-per-inference compared to GPU-based alternatives when models are compiled using the AWS Neuron SDK.

• AWS Lambda and Fargate

For lighter inference workloads, event-driven pipelines, or microservice architectures, Lambda (supporting up to 10GB memory and container images) and Fargate provide scalable compute without the overhead of instance management.

AI systems are only as good as their data infrastructure. AWS provides a layered data stack that supports everything from raw ingestion to feature serving.

INGEST

Kinesis Data Streams   |   MSK (Kafka)   |   AWS Glue ETL   |   DMS / Transfer

STORE

Amazon S3 (Data Lake)   |   Lake Formation   |   Glue Data Catalog

SERVE

Amazon Redshift   |   Athena   |   SageMaker Feature Store   |   OpenSearch

Amazon S3 remains the backbone: a massively scalable object store serving as the landing zone for raw data, model artifacts, and processed datasets. Its integration with Glue, Athena, and Lake Formation makes it the natural foundation for a modern data lakehouse. Amazon SageMaker Feature Store provides a managed repository for ML features, supporting both online (low-latency retrieval) and offline (batch training) access patterns with consistency guarantees across both stores.  

One of the most underestimated challenges in AI product engineering is not building models. It is operating them reliably at scale. AWS SageMaker provides a comprehensive MLOps layer that addresses this directly.

Model Monitor: continuous drift detection feedback loop

• SageMaker Pipelines

Enables teams to define, schedule, and version end-to-end ML workflows, from data preprocessing to model training, evaluation, and registration. Pipelines integrate natively with SageMaker Model Registry, enabling model versioning and approval workflows before promotion to production.

• SageMaker Model Monitor

Provides continuous monitoring of deployed models, detecting data drift, model drift, and bias drift in production traffic. Essential for maintaining model quality over time without manual intervention.

• SageMaker Endpoints

Support real-time inference with auto-scaling, multi-model endpoints for cost-efficient hosting, and shadow deployments for safely validating new model versions against live traffic before full rollout. 

Amazon Bedrock has quickly become a central service for teams building generative AI applications on AWS. It provides API access to a range of foundation models, including Anthropic Claude, Meta Llama, Mistral, Cohere, and Amazon Titan models, without requiring teams to manage the underlying infrastructure.

Bedrock's managed nature means teams can move quickly, but architectural decisions around model selection, prompt design, chunking strategies for RAG, and latency management still require experienced engineering judgment. 

AWS's security model is built on the shared responsibility framework: AWS secures the underlying infrastructure, while customers are responsible for what runs on it. For AI-native products, this translates into a set of concrete engineering concerns.

• IAM and Service Control Policies

Fine-grained identity and access management is foundational. Service-to-service communication should use IAM roles rather than long-lived credentials. AWS Organizations and SCPs enable governance at scale across multi-account architectures.

• VPC Design and PrivateLink

Production AI workloads should run within private subnets, with traffic to AWS services routed via VPC Endpoints. PrivateLink provides private connectivity to SageMaker endpoints and Bedrock, an important consideration for data-sensitive applications.

• Macie, GuardDuty, and KMS

Macie identifies sensitive data in S3; GuardDuty monitors for anomalous API activity and unauthorized access patterns. AWS KMS handles encryption key management for data at rest across S3, RDS, Redshift, and SageMaker. For regulated industries, KMS with Customer Managed Keys is typically a compliance requirement.   

The AWS ecosystem's breadth is both its strength and its complexity. Individual services are well-documented, but composing them into a coherent, production-grade architecture that is cost-efficient, observable, and secure requires experience across the stack.

The distinction worth noting is between cloud lift-and-shift engagements and genuine product engineering. The former moves workloads to AWS; the latter redesigns them to take full advantage of what AWS's managed services offer. For AI-native products, that difference in approach has a direct impact on time-to-market, operational overhead, and total cost of ownership.

Aziro, as an AI-native product engineering firm and AWS Consulting Partner, sits at the intersection of these concerns, bringing both cloud infrastructure knowledge and applied AI engineering depth to translate business requirements into systems that work reliably in production. 

AWS provides a powerful, deeply integrated platform for building AI-native products, but realizing that potential requires more than provisioning services. It demands architectural thinking across migration strategy, compute, data, MLOps, and security, combined with the practical experience of having shipped production systems on these primitives. For engineering teams navigating this landscape, the right consulting partnership, one grounded in technical depth rather than service reselling, can meaningfully compress the path from prototype to production.