Bedrock AgentCore Runtime vs ECS Fargate: Choosing Your AI Agent Deployment Strategy

When deploying AI agents on AWS, you face a fundamental choice: use AWS Bedrock AgentCore Runtime (managed AI agent service) or deploy custom agent code on ECS Fargate (container orchestration). Both approaches work, but they serve different needs and involve different tradeoffs.

This post compares these deployment strategies across architecture, cost, operational complexity, and performance to help you choose the right approach for your use case.

TL;DR

Bedrock AgentCore Runtime:

  • Managed service for AI agents
  • Serverless scaling, pay per invocation
  • Pre-built integrations (knowledge bases, action groups, guardrails)
  • Limited to AWS infrastructure and Bedrock models
  • Best for: Standard AI agent patterns, rapid development, AWS-centric teams

ECS Fargate:

  • Container orchestration for custom code
  • You manage application, AWS manages infrastructure
  • Complete control over agent logic and dependencies
  • Works with any LLM provider or framework
  • Best for: Custom requirements, multi-cloud, complex workflows, non-Bedrock models

Decision Framework:

  • Use AgentCore Runtime if your agent fits standard patterns and you want managed services
  • Use ECS Fargate if you need custom logic, multi-cloud portability, or non-Bedrock models

Understanding the Options

Bedrock AgentCore Runtime

Bedrock AgentCore Runtime is AWS’s managed service for deploying AI agents. You define agents through configuration (agent instructions, action groups, knowledge bases) and AWS handles execution, scaling, and infrastructure.

Core Concept: Agents are configuration artifacts, not deployed code. You describe what the agent should do (instructions, available tools, knowledge sources), and Bedrock Runtime executes the agent when invoked.

Architecture: Serverless model where AWS provisions compute on-demand. You don’t manage servers, containers, or scaling logic. Each agent invocation runs in AWS-managed infrastructure with automatic resource allocation.

Model Support: Limited to Bedrock foundation models (Claude, Llama, Titan, etc.). You cannot use OpenAI, Google, or self-hosted models directly within AgentCore Runtime.

Integration: Native AWS service integration with minimal configuration. Connect to Lambda (action groups), OpenSearch/Aurora (knowledge bases), S3 (data sources), and CloudWatch (observability).

Reference: AWS Bedrock Agents Documentation

ECS Fargate

ECS Fargate is AWS’s serverless container platform. You package your application as Docker containers, and Fargate runs them without managing EC2 instances.

Core Concept: Containers are deployed code. You write agent logic in any language/framework, package as containers, and Fargate executes them. Complete control over application behavior.

Architecture: Serverless containers where AWS manages host infrastructure but you control application layer. Define task definitions (CPU, memory, image) and ECS schedules containers on Fargate infrastructure.

Model Support: Unrestricted. Call any LLM API (OpenAI, Anthropic, Google, Azure OpenAI, self-hosted), use any framework (LangChain, LlamaIndex, custom code), combine multiple providers.

Integration: You implement all integrations. Need vector database? Install client library and connect. Need observability? Add instrumentation code. More work, but complete flexibility.

Reference: AWS ECS Fargate Documentation

Architecture Comparison

Deployment Model

AgentCore Runtime:

Agent Definition (Configuration)
    ↓
AWS Bedrock Runtime Service
    ↓
Automatic Scaling & Execution
    ↓
Response to Caller

You define agents in the AWS console or via API. No code deployment. AWS handles all execution.

ECS Fargate:

Dockerfile + Application Code
    ↓
Build Docker Image
    ↓
Push to ECR (Container Registry)
    ↓
ECS Task Definition
    ↓
Fargate Executes Container
    ↓
Your Code Handles Requests

You build, push, and deploy containers. AWS runs them, but you manage application behavior.

Scaling Model

AgentCore Runtime:

Automatic, instant scaling. Each invocation gets dedicated compute. No cold starts for the service itself (though model initialization has latency). Scale from 0 to thousands of concurrent requests without configuration.

Cost model: Pay per invocation + model tokens. No compute charges when idle.

ECS Fargate:

Auto-scaling based on metrics (CPU, memory, custom). Configure minimum/maximum task counts. Scale from min tasks to max tasks based on load. Cold starts when scaling from 0 (if allowed).

Cost model: Pay for container runtime (vCPU-seconds + GB-seconds) regardless of utilization. Idle containers still cost money.

State Management

AgentCore Runtime:

Stateless invocations. AgentCore provides session management for conversation context. For workflow state, use Bedrock Memory (semantic state) or external stores (DynamoDB).

Sessions expire after inactivity. Long-running workflows require external orchestration (Step Functions, Lambda).

ECS Fargate:

Containers can maintain state in memory during task lifetime. For persistent state, use external stores (DynamoDB, Redis, RDS).

Long-running tasks supported up to task definition limits. Suitable for workflows that span hours if container stays running.

Cost Comparison

Bedrock AgentCore Runtime Costs

Components:

  1. Model Inference: Dominant cost. Claude Sonnet ~$3/million input tokens, $15/million output tokens
  2. Knowledge Base Queries: $0.10 per query for OpenSearch Serverless
  3. Memory Storage: If using Bedrock Memory, storage and retrieval costs
  4. No compute overhead: AgentCore Runtime itself has no separate charge

Example Cost:

  • 10,000 agent invocations/day
  • Average 2,000 input tokens, 500 output tokens per invocation
  • 2 knowledge base queries per invocation

Monthly cost:

  • Model: (20M input × $3 + 5M output × $15) / 1M = $60 + $75 = $135
  • Knowledge base: 10K × 30 × 2 × $0.10 = $60,000 queries × $0.10 = $6,000
  • Total: ~$6,135/month

Cost Characteristics:

  • ✅ Zero cost when idle
  • ✅ Predictable per-invocation costs
  • ⚠️ Knowledge base queries expensive at scale
  • ⚠️ Token costs can spike with verbose prompts

Reference: Bedrock Pricing

ECS Fargate Costs

Components:

  1. vCPU: $0.04048 per vCPU per hour
  2. Memory: $0.004445 per GB per hour
  3. Data Transfer: Standard AWS rates (free within region for most cases)

Example Cost:

  • 4 vCPU, 8 GB memory per task
  • 10 tasks running 24/7 for high availability and throughput
  • Model costs same as above (assuming using Bedrock API from containers)

Monthly cost:

  • Compute: 4 vCPU × $0.04048 × 730 hours = $118.20 per task
  • Memory: 8 GB × $0.004445 × 730 hours = $25.95 per task
  • Per task total: $144.15
  • 10 tasks: $1,441.50
  • Model costs: $135 (same as above, using Bedrock API)
  • Total: ~$1,576.50/month

Cost Characteristics:

  • ⚠️ Continuous cost even when idle
  • ✅ More predictable compute costs
  • ⚠️ Must maintain minimum capacity for availability
  • ✅ Can optimize with right-sizing

Reference: AWS Fargate Pricing

Cost Winner: Depends on Usage Pattern

AgentCore Runtime wins when:

  • Sporadic traffic with periods of zero requests
  • Burst traffic patterns
  • Don’t need constant availability
  • Knowledge base queries are minimal

ECS Fargate wins when:

  • Consistent 24/7 traffic
  • High request rates justify dedicated capacity
  • Using cheaper/free models (local inference, cached responses)
  • Can amortize compute cost across many requests

Breakeven Analysis:

If Fargate saves $6,135 - $135 = $6,000 in knowledge base costs but costs $1,441 in compute, and you can handle 10K requests/day with 10 tasks, Fargate is cheaper.

However, if traffic is sporadic (1K requests some days, 20K others), AgentCore’s pay-per-use model is more economical.

Operational Complexity

AgentCore Runtime Operations

Setup Complexity: Low

  1. Define agent in AWS console or via API
  2. Configure action groups (Lambda functions)
  3. Set up knowledge bases (OpenSearch or Aurora)
  4. Deploy—no container building, no registry management

Ongoing Operations: Minimal

  • No patching, no dependency updates (AWS handles)
  • No scaling configuration (automatic)
  • No health checks to configure (built-in)
  • Monitoring through CloudWatch (automatic)

Development Workflow:

# Update agent configuration
aws bedrock-agent update-agent \
  --agent-id AGENT_ID \
  --agent-name "MyAgent" \
  --instructions "New instructions here"

# No build/deploy cycle needed

Operational Burden: 🟢 Low

ECS Fargate Operations

Setup Complexity: Medium

  1. Write application code
  2. Create Dockerfile
  3. Set up ECR repository
  4. Build and push images
  5. Create ECS cluster, task definitions, services
  6. Configure load balancers, security groups, IAM roles
  7. Set up auto-scaling policies
  8. Configure logging and monitoring

Ongoing Operations: Higher

  • Dependency management (library updates, security patches)
  • Container image rebuilds and deployments
  • Scaling policy tuning
  • Manual health check configuration
  • Custom monitoring setup

Development Workflow:

# Build image
docker build -t my-agent .

# Tag and push
docker tag my-agent:latest 123456789012.dkr.ecr.us-west-2.amazonaws.com/my-agent:latest
docker push 123456789012.dkr.ecr.us-west-2.amazonaws.com/my-agent:latest

# Update service
aws ecs update-service \
  --cluster my-cluster \
  --service my-agent-service \
  --force-new-deployment

# Wait for deployment (can take minutes)

Operational Burden: 🟡 Medium

Winner: AgentCore Runtime for Simplicity

If your goal is minimal operational overhead and your use case fits AgentCore patterns, the managed service requires significantly less ongoing work.

Flexibility and Control

AgentCore Runtime Limitations

Model Lock-in:

  • Must use Bedrock foundation models
  • Cannot use OpenAI GPT-4, Gemini, or other providers
  • Cannot run local/self-hosted models
  • Model selection limited to Bedrock catalog

Framework Lock-in:

  • Cannot use LangChain, LlamaIndex, or custom frameworks
  • Agent behavior defined by Bedrock’s orchestration logic
  • Limited control over retry logic, error handling, prompt engineering

Integration Constraints:

  • Knowledge bases limited to OpenSearch Serverless or Aurora
  • Action groups must be Lambda functions (can’t call arbitrary APIs directly)
  • Custom preprocessing/postprocessing requires Lambda workarounds

Workflow Patterns:

  • Best for request-response patterns
  • Long-running workflows require external orchestration
  • Complex state machines awkward to implement
  • Limited support for iterative refinement patterns

ECS Fargate Flexibility

Complete Freedom:

  • Use any LLM: OpenAI, Anthropic, Google, Azure, self-hosted
  • Combine multiple providers in one application
  • Use any framework: LangChain, LangGraph, LlamaIndex, custom
  • Implement any orchestration logic

Custom Integrations:

  • Direct API calls to any service
  • Custom vector databases (Pinecone, Weaviate, Chroma)
  • Any data source or processing pipeline
  • Custom authentication and authorization

Advanced Patterns:

  • Complex state machines and workflows
  • Iterative refinement with custom logic
  • Hybrid approaches (multiple models, fallbacks)
  • Custom caching, batching, optimization strategies

Portability:

  • Same container runs on-premise, other clouds, local development
  • Not locked to AWS infrastructure
  • Easy migration if requirements change

Winner: ECS Fargate for Flexibility

If you need control, portability, or use cases outside AgentCore patterns, containers provide necessary flexibility.

Performance Characteristics

AgentCore Runtime Performance

Latency:

  • Cold start: Model initialization adds 1-3 seconds on first invocation
  • Warm invocations: ~100-300ms overhead beyond model inference
  • Knowledge base queries: Additional 200-500ms per query
  • Total: Typically 2-5 seconds for interactive queries

Throughput:

  • Concurrent invocation limits (account-level quotas)
  • Default: 10 concurrent invocations per agent
  • Request increase through AWS support
  • Bottleneck often model throughput, not service limits

Optimization:

  • Pre-warm agents by keeping them active (periodic invocations)
  • Minimize knowledge base queries
  • Use streaming for faster perceived response

ECS Fargate Performance

Latency:

  • Cold start: Container initialization 30-60 seconds (if scaling from 0)
  • Warm containers: ~10-50ms overhead for request routing
  • Model API calls: Same as AgentCore (both call Bedrock if using)
  • Total: 10-50ms overhead when warm, plus model latency

Throughput:

  • Limited by task count and resources
  • Easily scale to hundreds of concurrent requests
  • Add tasks to increase throughput (linear scaling)
  • Can handle sustained high load better than serverless

Optimization:

  • Keep minimum task count > 0 to avoid cold starts
  • Use application load balancer for efficient routing
  • Implement connection pooling for model API calls
  • Cache responses where appropriate

Winner: Situation Dependent

  • Interactive user queries: Similar performance, AgentCore slightly simpler
  • High sustained load: Fargate provides more predictable performance
  • Batch processing: Fargate handles long-running tasks better
  • Bursty traffic: AgentCore scales more elastically

Development Experience

AgentCore Runtime Development

Local Development:

  • Cannot run agents locally (requires AWS)
  • Test by invoking actual agents in AWS
  • Agent simulator tools limited

Testing:

  • Integration testing against real AWS resources
  • Unit testing limited to action group Lambdas
  • Mocking difficult due to managed service nature

Debugging:

  • CloudWatch logs and traces
  • Cannot step through agent orchestration logic
  • Limited visibility into decision-making process

CI/CD:

# Example GitHub Actions for AgentCore
- name: Update Agent
  run: |
    aws bedrock-agent update-agent \
      --agent-id $ \
      --instructions "$"
    
    aws bedrock-agent prepare-agent \
      --agent-id $

Learning Curve: Medium (AWS-specific concepts)

ECS Fargate Development

Local Development:

  • Run containers locally with Docker
  • Full development environment on laptop
  • Faster iteration without cloud dependencies

Testing:

  • Standard unit testing for all code
  • Integration tests with mocked services
  • Contract testing for external APIs
  • Full control over test environment

Debugging:

  • Standard debuggers (pdb, VS Code debugger)
  • Step through code execution
  • Local debugging identical to production code

CI/CD:

# Example GitHub Actions for Fargate
- name: Build and Push
  run: |
    docker build -t my-agent .
    docker tag my-agent:latest $ECR_REGISTRY/my-agent:$
    docker push $ECR_REGISTRY/my-agent:$

- name: Deploy to ECS
  run: |
    aws ecs update-service \
      --cluster production \
      --service my-agent \
      --force-new-deployment

Learning Curve: Medium (Docker + ECS concepts)

Winner: ECS Fargate for Developer Experience

Local development, standard debugging, and portable code make Fargate more developer-friendly for complex applications.

Security and Compliance

AgentCore Runtime Security

Built-in:

  • IAM-based access control
  • VPC support for private resources
  • Encryption at rest and in transit (managed by AWS)
  • CloudTrail logging of all API calls

Guardrails:

  • Native support for content filtering
  • PII detection and redaction
  • Toxicity screening
  • Harmful content blocking

Compliance:

  • Inherits AWS compliance certifications (SOC2, HIPAA, etc.)
  • Data residency depends on AWS region
  • Cannot deploy to on-premise environments

Limitations:

  • Cannot implement custom security logic
  • Limited control over data flow
  • Must trust AWS infrastructure

ECS Fargate Security

Built-in:

  • IAM roles for tasks
  • Security groups for network isolation
  • VPC networking
  • Encryption through AWS KMS

Custom Implementation:

  • Implement any authentication scheme
  • Custom authorization logic
  • Advanced threat detection
  • Rate limiting and abuse prevention

Compliance:

  • Full control over data handling
  • Can implement specific compliance requirements
  • Auditability through custom logging
  • Option to deploy elsewhere if needed

Responsibility:

  • You secure application code
  • Manage dependencies and patches
  • Implement guardrails manually

Winner: Tie (Different Tradeoffs)

AgentCore provides built-in security features. Fargate offers more control but requires more implementation.

Use Case Recommendations

Use Bedrock AgentCore Runtime When:

Conversational AI:

  • Customer support chatbots
  • Internal knowledge base Q&A
  • FAQ automation
  • Simple task assistants

Requirements:

  • Standard AI agent patterns
  • AWS-centric architecture
  • Bedrock models meet needs
  • Want minimal operational overhead
  • Rapid development priority
  • Built-in guardrails needed

Example: Sales support bot that answers product questions using knowledge base and triggers simple actions.

Use ECS Fargate When:

Complex Workflows:

  • Multi-step data processing pipelines
  • Long-running analytical tasks
  • Custom orchestration patterns
  • Iterative refinement workflows

Requirements:

  • Need specific LLM providers (OpenAI, etc.)
  • Custom agent frameworks (LangGraph, etc.)
  • Complex state management
  • Portable across environments
  • Existing containerized infrastructure
  • Advanced optimization strategies

Example: Research analysis system that processes documents with multiple models, custom caching, and complex workflow logic.

Hybrid Approaches

You don’t have to choose exclusively. Many organizations use both:

Pattern 1: Frontend + Backend

  • AgentCore Runtime for user-facing chatbots (simple, managed)
  • ECS Fargate for backend processing (complex, flexible)
  • Communication via SQS or EventBridge

Pattern 2: Tiered by Complexity

  • Simple agents on AgentCore Runtime
  • Complex agents on Fargate
  • Shared data stores and action handlers

Pattern 3: Migration Path

  • Start with AgentCore Runtime for speed
  • Migrate specific agents to Fargate as requirements grow
  • Keep some agents on AgentCore permanently

Migration Considerations

From AgentCore Runtime to ECS Fargate

Reasons to Migrate:

  • Need non-Bedrock models
  • AgentCore patterns too limiting
  • Cost optimization for high volume
  • Custom orchestration requirements

Migration Path:

  1. Replicate agent logic in code (LangChain/LangGraph)
  2. Containerize application
  3. Set up Fargate infrastructure
  4. Run parallel for testing
  5. Cutover traffic

Challenges:

  • Reimplement agent orchestration
  • Lose native knowledge base integration
  • Operational complexity increases

From ECS Fargate to AgentCore Runtime

Reasons to Migrate:

  • Reduce operational overhead
  • Standardize on AWS managed services
  • Leverage native integrations
  • Simplify architecture

Migration Path:

  1. Identify agent patterns in code
  2. Map to AgentCore concepts (instructions, action groups, knowledge bases)
  3. Create agents in Bedrock
  4. Test equivalence
  5. Cutover

Challenges:

  • May lose custom logic
  • Vendor lock-in to AWS
  • Model limitations

Decision Framework

Use this framework to choose:

1. Model Requirements

  • Need non-Bedrock models? → Fargate
  • Bedrock models sufficient? → Continue evaluation

2. Operational Capacity

  • Small team, want managed services? → AgentCore Runtime
  • DevOps expertise available? → Continue evaluation

3. Complexity

  • Standard agent patterns? → AgentCore Runtime
  • Custom workflows, complex logic? → Fargate

4. Cost Sensitivity

  • Sporadic traffic? → AgentCore Runtime
  • Consistent high volume? → Fargate

5. Flexibility Requirements

  • Need portability, multi-cloud? → Fargate
  • AWS-only acceptable? → AgentCore Runtime

6. Development Speed

  • Need to ship fast? → AgentCore Runtime
  • Have time for custom development? → Either works

Real-World Examples

Case Study 1: Customer Support Bot

Company: Mid-size SaaS company
Use Case: Customer support chatbot for documentation Q&A
Choice: Bedrock AgentCore Runtime

Why:

  • Standard chatbot pattern fit AgentCore perfectly
  • Small team valued managed service
  • Bedrock models sufficient
  • Knowledge base integration simplified setup
  • Sporadic traffic (business hours)

Results:

  • Deployed in 2 weeks
  • $800/month in costs
  • 99.5% uptime without operational overhead

Case Study 2: Research Analysis Pipeline

Company: Healthcare analytics startup
Use Case: Medical literature analysis with custom models
Choice: ECS Fargate

Why:

  • Needed domain-specific fine-tuned models
  • Complex multi-step workflows
  • Custom LangGraph orchestration
  • Required on-premise deployment option (compliance)
  • 24/7 processing of submitted documents

Results:

  • 6 weeks development + deployment
  • $2,500/month Fargate + model costs
  • Full control over processing pipeline
  • Portable architecture for customer installations

Conclusion

Neither Bedrock AgentCore Runtime nor ECS Fargate is universally better—they serve different needs.

Choose AgentCore Runtime for standard AI agent patterns where managed services, rapid development, and minimal operations are priorities. It’s excellent for chatbots, Q&A systems, and simple task automation.

Choose ECS Fargate for custom requirements, complex workflows, non-Bedrock models, or when you need portability and complete control. It’s essential for sophisticated applications that don’t fit managed service patterns.

Many organizations benefit from both: AgentCore Runtime for simple agents, Fargate for complex ones. Start with the simpler approach (usually AgentCore) and migrate to containers only when requirements demand it.

The best architecture is the simplest one that meets your requirements. Don’t deploy containers just because you can—but don’t force complex logic into managed services that weren’t designed for it.

Additional Resources

AWS Documentation

Pricing

Best Practices

Community

This comparison reflects AWS Bedrock and ECS Fargate as of November 2025. Check AWS documentation for the latest features and pricing.