# Guidance for Accelerator-Optimized Agentic Bidding on AWS

## Overview

This Guidance demonstrates how to optimize real-time bidding decisions across multiple machine learning models by deploying GPU-accelerated NVIDIA model architectures that run in parallel within an ARTF-compliant framework. When an OpenRTB bid request arrives, an orchestrator container distributes it simultaneously to four specialized containers: one predicts click-through rates and computes optimal bid prices, another scores user-segment affinities to activate high-value audiences, a third evaluates deal relevance to activate or suppress private marketplace opportunities, and a fourth enriches requests with viewability and brand safety metrics. Each container applies specific ARTF mutations—such as BID_SHADE for pricing optimization, ACTIVATE_SEGMENTS for audience targeting, and ADD_METRICS for quality enrichment—and the orchestrator consolidates all recommendations into a single OpenRTB response within milliseconds. You can reduce advertising overspend while maintaining win rates, autonomously manage private marketplace deals, and enable AI agents to make real-time bidding decisions through Model Context Protocol integration.

## Benefits

### Accelerate bid decisions with GPU inference

Process real-time bidding requests using GPU-accelerated inference on Amazon Elastic Kubernetes Service, enabling sub-millisecond model scoring that helps you win more auctions at optimal prices.


### Enrich bids with parallel model scoring

Fan out each bid request across multiple specialized models simultaneously. Score click-through rates, audience segments, and deal relevance in a single round trip to maximize yield.


### Optimize ad spend with intelligent bid shading

Deploy deep learning models that predict optimal bid prices and suppress poor-match deals automatically. Reduce wasted spend while maintaining competitive win rates across programmatic auctions.


## How it works

This architecture shows how accelerator-optimized compute can enable agentic real-time bid-stream mutations for OpenRTB auctions using Triton Inference Server on Amazon Elastic Kubernetes Service with GPU-accelerated inference. [Download the architecture diagram.](downloads/accelerator-optimized-agentic-bidding-on-aws.pdf)

![Architecture diagram for Accelerator-Optimized Agentic Bidding on AWS](/images/solutions/accelerator-optimized-agentic-bidding-on-aws/images/accelerator-optimized-agentic-bidding-on-aws.png)

1. **Step 1**: The Requester (for example, a Supply Side Platform) sends an OpenRTB bid request with JSON Web Token (JWT) tokens for session access.
1. **Step 2**: The request routes through a Network Load Balancer to the Amazon Elastic Kubernetes Service cluster's Orchestrator container.
1. **Step 3**: The Orchestrator on CPU nodes verifies the JWT token against Amazon Cognito's JSON Web Key Set (JWKS) endpoint, then fans out the request to all four containers (DLRM, Wide & Deep, NCF, and Metrics Enricher) in parallel.
1. **Step 4**: The NVIDIA model containers (DLRM, Wide & Deep, NCF) call Triton Inference Server via tritonclient Python library, which runs GPU-accelerated inference using Open Neural Network Exchange (ONNX) Runtime with CUDA Execution Provider on A10G GPUs.
1. **Step 5**: DLRM predicts click-through rate and computes an optimal shaded bid price. Wide & Deep scores user-segment affinities and activates audience segments above threshold.
1. **Step 6**: NCF scores user-deal relevance to activate high-affinity deals and suppress poor matches.
1. **Step 7**: The rule-based Metrics Enricher adds viewability and brand-safety scores.
1. **Step 8**: The Orchestrator merges all mutations from the four containers into a single OpenRTB response and returns it to the requester.
## Deploy with confidence

Everything you need to launch this Guidance in your account is right here.

- **Let's make it happen**: Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.

[Go to sample code](https://github.com/aws-solutions-library-samples/guidance-for-accelerator-optimized-agentic-bidding-on-aws)


[Read usage guidelines](/solutions/guidance-disclaimers/)