← Back to Technical Section

GenAI Infrastructure on AWS

Fully managed, hybrid, and self-managed options — trade-offs, architecture diagrams, and decision criteria for each approach.

Three Infrastructure Approaches

AWS offers a spectrum of GenAI infrastructure options, from fully managed services that abstract all operational complexity, to hybrid architectures that combine managed and custom components, to self-managed on-premises deployments for maximum control and predictable cost.

Fully Managed

Bedrock + AgentCore. Minimal ops effort, fast time-to-market.

Hybrid

Lambda + Step Functions + Bedrock. Custom control where it matters.

Self-Managed

On-prem GPUs or private cloud. Maximum control, predictable TCO.

Amazon Bedrock & AgentCore

Amazon Bedrock provides access to leading foundation models (Anthropic Claude, AWS Nova, Cohere, Meta Llama) with fully managed inference, automatic scaling, and per-token billing. AgentCore extends Bedrock with an enterprise agent execution runtime.

AgentCore Features

Sessions up to 8 hours with async task support
Short-term and long-term memory services
Tool gateway converting APIs to MCP tools
Identity service with OAuth credential injection
Built-in critics for factuality and safety
Cedar policy language for guardrails
CloudWatch metrics for tokens and latency

Async Task Pattern

@app.async_task
def process_report(context, data):
    # Long-running agent task
    result = orchestrator.run(data)
    return result

# Client submits and polls
job = app.invoke_async(process_report, data)
status = app.get_task_status(job.id)

Advantages

Zero infrastructure management
Built-in compliance and governance tools
Automatic scaling to zero cost when idle
Fast integration with AWS ecosystem

Limitations

Higher per-token cost at scale
Limited model customization options
Vendor lock-in risk
Less control over inference parameters

Hybrid Architectures

Lambda + CrewAI

Each agent runs in its own Lambda function. State is persisted in DynamoDB or S3. API Gateway acts as the central orchestrator, dispatching function invocations sequentially or in parallel.

Configure reserved concurrency per Lambda to prevent burst throttling
Use SQS between agents for decoupled, resilient message passing
Pay only for execution time — ideal for bursty, event-driven workloads

Step Functions + Agents

Step Functions provides visual, durable orchestration of complex agent workflows. Supports parallel execution, choice branching, map iterations, and built-in error handling with retries and circuit breakers.

Scales to thousands of concurrent workflow executions
Express workflows for high-throughput, short-duration tasks
Standard workflows for long-running, durable executions

RAG Container + Bedrock

The retrieval engine (OpenSearch or pgvector) runs in ECS/EKS containers for full control over indexing and search configuration, while generation uses Bedrock. This decouples retrieval performance from generation cost.

Self-Managed / On-Premises

For organizations with strict data residency requirements or high, predictable token volumes, running models on dedicated GPU hardware can significantly reduce TCO.

TCO Analysis: Studies show on-premises H100/H200 GPU servers deliver token costs up to 8x lower than cloud at steady-state loads, with break-even in under 4 years. A hybrid strategy — cloud for peak demand, on-prem for baseline — optimizes both cost and flexibility.

Pros: Lowest per-token cost at scale, full data control, no vendor dependency.
Cons: High capital expenditure (capex), requires MLOps team, limited elastic scaling.
When to choose: Steady high-volume workloads, strict data sovereignty requirements, or mature MLOps capabilities.

Infrastructure Comparison

Dimension	Fully Managed	Hybrid	Self-Managed
Ops Effort	Minimal	Medium	High
Token Cost	Highest	Medium	Lowest
Control	Low	Medium–High	Maximum
Time to Market	Fastest	Medium	Slowest
Compliance	Built-in	Shared	Full ownership
Vendor Lock-in	High	Moderate	None

Test Your Knowledge

Score 8/10 or higher to pass