Deploy a high-performance RTB infrastructure that processes billions of daily requests with sub-500ms response times. The optimized architecture with direct pod routing and Graviton instances handling up to 3.5 million requests per second ensures you capture every revenue opportunity in time-sensitive bidding scenarios.
Overview
This Guidance demonstrates how retailers can transform operations withpowerful AI solutions that maximize existing hardware investmentswithout requiring additional infrastructure. Lightweight computer visionmodels detect high-traffic areas, safety issues, and long customerqueues while running directly on current in-store systems. The solutioncombines fine-tuned foundation models with container-based deploymentstrategies that efficiently manage data from IoT sensors, cameras, andpoint-of-sale systems. You can boost employee productivity, enhanceoperations, and deliver better customer experiences while keepingbandwidth consumption low through optimized AI models running on yourexisting systems.
Benefits
Maximize revenue with millisecond-level performance
Scale dynamically while reducing operational costs
Automatically adjust capacity to match traffic patterns with intelligent auto-scaling that provisions resources only when needed. The combination of AWS Graviton instances, optimized HAProxy configurations, and selective log sampling delivers up to 60% energy savings while maintaining enterprise-grade performance for AdTech workloads.
Ensure continuous availability during traffic spikes
Eliminate revenue-impacting downtime with a multi-layered resilient architecture that withstands component failures at every level. The distributed design across multiple Availability Zones with automated health checks and instance replacement ensures your bidding platform remains operational even during unexpected traffic surges common in advertising campaigns.
How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
Step 1
IoT sensors, IP cameras, RFID readers, point-of-sale systems, mobile clients, and appliances collect raw data in-store
Step 2
Lightweight computer vision models detect high-traffic areas, safety issues, or long customer queues. Containers running inference code and AI models are deployed to retail locations.
Step 3
Alerts and application events are shared between applications via an on-premises event bus. Common choices include AWS IoT Core, Apache Kafka or Redis.
Step 4
In-store AI Agents use fine-tuned foundation models, vector databases for Retrieval Augmented Generation, Model Context Protocol (MCP) servers tool implementation, and the Strands SDK.
Step 5
Amazon SageMaker AI is used to prepare training data and fine-tune / distill lightweight models for use in stores. Model artifacts are stored in Amazon Simple Storage Service (S3)
Step 6
Model artifacts and inference code are combined in container images using AWS CodePipeline. Container images are pushed to Amazon Elastic Container Registry for later deployment.
Step 7
User feedback and application data are ingested from the event but (step 3) into Amazon Simple Storage Service (S3) using Amazon Data Firehose. This data is used for training the lightweight models.
Step 8
Amazon Elastic Kubernetes Service (EKS), Amazon Elastic Container Service (ECS), or one of several AWS partner solutions automate deployment and lifecycle management for container applications running in retail locations.
Step 9
Customers and staff interact with AI agents via kiosks, interactive displays, or mobile applications.
Step 10
In-store workloads are configured using AWS IAM Anywhere, allowing secure access to AWS cloud-based services like Amazon CloudWatch and AWS CloudTrail for logging and alerts.
Related content
Consolidate, modernize, transform: Edge computing for modern retail
Learn how retailers can leverage AWS edge computing solutions to consolidate infrastructure, establish unified cloud-edge architecture, and enable next-generation store applications while maximizing existing investments and reducing operational costs.