AI Chatbot Platform Architecture Template
RAG-powered chatbot with custom knowledge bases and streaming replies. Generate a complete cloud architecture with cost estimates, Terraform, sequence diagrams, CLI deployment workflows, and a GitHub Actions pipeline — on AWS, Azure, or GCP.
Production estimates. Your workspace generates actuals.
Architecture Overview
Ingests documents into a vector database, runs a RAG pipeline against your LLM, streams answers back to users, and enforces per-tenant rate limits through an API gateway with conversation history per session.
~8
cloud services
AWS Architecture Diagram
Full topology with all services and request flows — switch providers above to compare.
AI Chatbot Platform - AWS - Production implementation lanes - CloudDesign AI
Architecture Breakdown
Every major component, what it does, and the AWS service powering it.
API Gateway
Amazon API Gateway
Routes, authenticates, and rate-limits incoming requests.
RAG Handler
Amazon ECS Fargate
Handles business logic and integrates with surrounding services.
Vector Index
Amazon OpenSearch Service
Handles business logic and integrates with surrounding services.
LLM Inference
Amazon Bedrock
Handles business logic and integrates with surrounding services.
Document Store
S3
Stores and retrieves data with durability and access controls.
Ingestion Queue
Amazon EventBridge
Decouples producers from consumers for async processing.
Conversation DB
Amazon DynamoDB
Stores and retrieves data with durability and access controls.
Rate Limiter
Amazon ElastiCache Redis
Handles business logic and integrates with surrounding services.
Cost Estimate — AWS
Representative production estimate. Your workspace generates a breakdown based on your actual configuration.
AWS — $300 / month estimated
API Gateway
Request routing
Lambda
RAG handler
OpenSearch
Vector index
Bedrock
LLM inference
S3
Document storage
SQS
Ingestion queue
DynamoDB
Conversation history
CloudFront
CDN + WAF
Total estimate
$300 / month
What CloudDesign AI Generates
Every generation produces a complete set of production-ready artifacts.
Architecture Diagram
Full topology showing every service and how traffic flows between them.
Sequence Diagrams
Request lifecycle flows for upload, query, and overall system paths.
Cost Analysis
Per-service cost breakdown with total estimate for the selected provider.
Terraform Code
Complete infrastructure-as-code export you can deploy immediately.
CLI Deployment Workflow
Ordered provisioning commands for every service in the architecture.
GitHub Actions Pipeline
Ready-to-commit `.github/workflows/terraform.yml` for CI/CD.
Tradeoff Analysis
Cost, scalability, reliability, and operational complexity breakdown.
Production Checklist
Architecture-specific risks and mitigations before you go live.
Terraform Preview — AWS
Provider-specific infrastructure code. The full export is available after generating.
resource "aws_opensearch_domain" "vectors" {
domain_name = "${var.prefix}-vectors"
engine_version = "OpenSearch_2.11"
}
resource "aws_sqs_queue" "ingestion" {
name = "${var.prefix}-doc-ingestion"
}
resource "aws_dynamodb_table" "conversations" {
name = "${var.prefix}-conversations"
billing_mode = "PAY_PER_REQUEST"
hash_key = "session_id"
}
# + 310 more lines — generate the full export →Full Terraform export includes: variables, outputs, IAM roles, environment configs, and module structure.
Generate Full TerraformCLI Preview — AWS
Ordered provisioning commands for every service. The full workflow is generated in your workspace.
aws opensearch create-domain --domain-name $PREFIX-vectors \
--engine-version OpenSearch_2.11
aws sqs create-queue --queue-name $PREFIX-ingestion
aws dynamodb create-table --table-name $PREFIX-conversations \
--billing-mode PAY_PER_REQUEST --hash-key session_id
# + 24 more commands — generate the full workflow →Full CLI workflow includes: bucket creation, networking, IAM setup, application deployment, and health checks — in order.
Generate Full CLI WorkflowCloud Provider Mapping
Every architectural function mapped to its native service on AWS, Azure, and GCP.
Architecture Tradeoffs
How AWS, Azure, and GCP compare across the dimensions that matter most for this architecture.
Cost Efficiency
AWS Bedrock and GCP Vertex AI offer flexible per-token pricing; Azure OpenAI includes reserved capacity overhead.
LLM Model Variety
Azure OpenAI has exclusive GPT-4o access; Bedrock offers Claude, Titan, and Llama; Vertex AI provides Gemini.
Scalability
Lambda and Cloud Run scale to zero and burst instantly; Azure Functions premium plan is more predictable.
Vector Search Quality
Azure AI Search has first-class hybrid search (BM25 + vectors); OpenSearch and Vertex AI Search are strong alternatives.
Security & Compliance
AWS and Azure have the broadest enterprise compliance certifications; GCP is catching up rapidly.
Production Risks for This Architecture
Known failure modes with concrete mitigations — included in every generated checklist.
Vector DB cold-start latency spikes when OpenSearch scales from zero — pre-warm instances during office hours to avoid first-query delays
Multi-tenant index contamination risk: ensure strict tenant_id filter on every vector query or users may receive answers from other tenants' documents
LLM rate-limit cascades: Bedrock throttle errors under burst load will surface as 500s — implement exponential backoff and per-tenant queuing
Key Capabilities Covered
Frequently Asked Questions
Common questions about this architecture and what CloudDesign AI generates.
Generate the AI Chatbot Platform Architecture
Get the full architecture diagram, cost breakdown, Terraform, CLI workflow, and GitHub Actions pipeline — specific to your chosen cloud provider.
Free account · No credit card required · 5 architecture runs per month