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

Updated January 2026 to reflect AgentCore GA (October 2025) and latest features.

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

Important Clarification: Bedrock AgentCore is distinct from the older “Bedrock Agents” service. AgentCore is a modular, model-agnostic platform that works with any foundation model and any agent framework. It went generally available in October 2025.

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:

  • Fully managed, modular platform purpose-built for AI agents (GA October 2025)
  • Model-agnostic: works with OpenAI, Anthropic, Google, Bedrock models, self-hosted, etc.
  • Framework-agnostic: supports LangGraph, LlamaIndex, CrewAI, Strands Agents, custom code
  • Built-in memory (short-term, long-term, episodic), policy controls, observability, identity, tool gateway
  • Pay-per-use with no CPU charges during I/O wait (significant cost advantage for agentic workloads)
  • Long-running sessions up to 8 hours with session isolation
  • Best for: Production-grade agents needing memory, governance, observability without building infrastructure

ECS Fargate:

  • General-purpose serverless container orchestration
  • You manage application, AWS manages infrastructure
  • Complete control over runtime environment, dependencies, architecture
  • Lower base compute cost (~$0.04/vCPU-hour vs ~$0.09 for AgentCore)
  • Best for: Existing containerized systems, cost-optimized steady workloads, maximum flexibility

Decision Framework:

  • Use AgentCore if building production agents with memory, tools, policy needs—saves months of infrastructure work
  • Use ECS Fargate if you need maximum control, have existing container infrastructure, or run steady high-utilization workloads

Understanding the Options

Amazon Bedrock AgentCore

Bedrock AgentCore is AWS’s purpose-built managed platform for deploying, operating, and monitoring AI agents at scale. Unlike the older “Bedrock Agents” service (which is declarative and Bedrock-model-only), AgentCore is a modular, framework-agnostic, model-agnostic platform that went GA in October 2025.

Core Concept: AgentCore provides modular services that work together or independently:

  • Runtime: Secure serverless execution environment for agent code (containers or direct code upload)
  • Memory: Short-term, long-term, and episodic memory management
  • Gateway: Tool discovery and integration (supports Lambda, APIs, MCP servers)
  • Identity: Authentication and authorization for agent-to-tool and agent-to-agent interactions
  • Observability: Built-in monitoring, tracing, dashboards (CloudWatch + OpenTelemetry)
  • Policy (Preview): Natural language or Cedar-based deterministic controls for agent behavior
  • Evaluations: Automated quality monitoring for correctness, safety, and goal success
  • Browser Tool: Built-in web browsing capability
  • Code Interpreter: Built-in code execution sandbox

Architecture: Serverless model where AWS provisions compute on-demand. Sessions support up to 8 hours of execution with full session isolation. Bidirectional streaming enables real-time voice/conversational agents. Agent-to-Agent (A2A) protocol support for multi-agent systems.

Model Support: Fully model-agnostic. Use any foundation model—Bedrock (Claude, Llama, Titan), OpenAI (GPT-4), Google (Gemini), Anthropic direct, Azure OpenAI, self-hosted models, or any combination.

Framework Support: Fully framework-agnostic. Use LangGraph, LangChain, LlamaIndex, CrewAI, Strands Agents, custom code, or any framework. Supports Model Context Protocol (MCP) for tool integration.

Integration: Native AWS service integration plus external APIs. Gateway wraps any API/Lambda into agent-compatible tools with IAM or OAuth authorization.

Reference: AWS Bedrock AgentCore 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. It’s a general-purpose compute service, not specifically designed for AI agents.

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, runtime environment, and dependencies.

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. Need memory? Build it yourself. More work, but complete flexibility.

Recent Updates (2025-2026):

  • Capacity Provider Updates (June 2025): Change compute configuration between Fargate and EC2 without recreating services
  • Weekly Event Windows (December 2025): Schedule task retirements during specific windows (e.g., weekends only)
  • Platform Version Deprecation: Linux platform 1.3.0 retiring March 2026—services must migrate to 1.4.0
  • Stop Timeout: Maximum 120 seconds for graceful shutdown (can be limiting for long AI operations)

Reference: AWS ECS Fargate Documentation

Architecture Comparison

Deployment Model

AgentCore:

Agent Code (Python/Node.js)
    ↓
Option A: Direct Code Upload (zip) ← Faster iteration
    OR
Option B: Container Image → ECR → AgentCore Runtime
    ↓
AgentCore Services (Runtime + Memory + Gateway + ...)
    ↓
Automatic Scaling & Session Isolation
    ↓
Response to Caller

You deploy your agent code directly or via containers. AgentCore provides the agent-specific infrastructure (memory, tools, identity, observability) as managed services. Direct code upload (new in late 2025) enables faster iteration without container builds.

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: memory, tools, identity, observability

You build, push, and deploy containers. AWS runs them, but you implement all agent-specific capabilities (memory, tool integration, policy enforcement, etc.) yourself.

Scaling Model

AgentCore:

Automatic scaling with session isolation. Each agent session gets dedicated compute with security isolation. Scale from 0 to thousands of concurrent sessions without configuration. Supports both low-latency interactive workloads and long-running sessions (up to 8 hours).

Cost model: Pay for active CPU usage + peak memory. Key differentiator: no CPU charges during I/O wait. When your agent waits for LLM responses, tool calls, or external APIs, CPU isn’t billed. This is significant since agents spend 60-80% of time waiting.

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). Container restart timeout limited to 120 seconds.

Cost model: Pay for container runtime (vCPU-seconds + GB-seconds) regardless of utilization. Idle or waiting containers still cost money. Lower per-unit rates but constant billing.

State Management

AgentCore:

Built-in AgentCore Memory service provides comprehensive state management:

  • Short-term memory: Conversation context within sessions
  • Long-term memory: Persistent memory across sessions
  • Episodic memory: Agent learns from past experiences and outcomes
  • Shared memory: Memory stores accessible across multiple agents

Sessions support up to 8 hours of continuous execution. For longer workflows, Memory persists state across sessions.

ECS Fargate:

Containers can maintain state in memory during task lifetime. For persistent state, you must implement and manage external stores (DynamoDB, Redis, RDS).

Long-running tasks supported up to task definition limits. Suitable for workflows that span hours if container stays running. No built-in agent memory—you build it.

Cost Comparison

Bedrock AgentCore Costs

AgentCore uses modular, consumption-based pricing. Each service is billed independently.

AgentCore Runtime Pricing:

  • vCPU: ~$0.0895 per vCPU-hour (active CPU time only)
  • Memory: ~$0.00945 per GB-hour (peak memory)
  • Key feature: CPU not charged during I/O wait (waiting for LLMs, APIs, tools)

Other AgentCore Services:

  • Memory: Per-event storage and retrieval pricing
  • Gateway: Per-invocation for tool calls
  • Identity, Observability, Policy, Evaluations: Separate pricing per service

Model Inference (separate):

  • Model costs depend on which models you use (Bedrock, OpenAI, etc.)
  • Claude Sonnet ~$3/million input, $15/million output tokens
  • Use any model—costs billed by that provider

Example Cost (AgentCore Runtime):

  • 10 million agent sessions/month
  • Average 60 seconds per session
  • 70% I/O wait time (waiting for LLM responses, tool calls)
  • Estimated cost: ~$7,235/month for Runtime alone

Cost Characteristics:

  • ✅ No CPU cost during I/O wait (agents spend 60-80% waiting)
  • ✅ Zero cost when idle (no sessions)
  • ✅ Modular—pay only for services you use
  • ⚠️ Higher per-unit compute rate than Fargate (~2x)
  • ⚠️ Model costs separate and can dominate total cost

Reference: AgentCore Pricing

ECS Fargate Costs

Components:

  1. vCPU: ~$0.04048 per vCPU-hour (Linux x86, US East)
  2. Memory: ~$0.004445 per GB-hour
  3. Ephemeral Storage: Additional charges beyond 20GB
  4. Data Transfer: Standard AWS rates

Pricing Notes:

  • Billed per second with 1-minute minimum
  • Charged for allocated resources regardless of utilization
  • Idle containers waiting for LLM responses still incur full cost

Example Cost:

  • 4 vCPU, 8 GB memory per task
  • 10 tasks running 24/7 for high availability

Monthly compute cost:

  • vCPU: 4 × $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/month (compute only)
  • Plus model costs (billed separately to whatever provider)

Cost Characteristics:

  • ✅ Lower per-unit compute rates (~$0.04 vs ~$0.09/vCPU-hour)
  • ✅ Predictable costs for steady workloads
  • ⚠️ Continuous cost even when idle or waiting
  • ⚠️ Must maintain minimum capacity for availability
  • ⚠️ You build infrastructure (memory, tools, observability)—development cost

Reference: AWS Fargate Pricing

Cost Winner: Depends on Usage Pattern

The key differentiator is I/O wait time. AI agents typically spend 60-80% of their execution time waiting for LLM responses, API calls, and tool executions.

AgentCore wins when:

  • High I/O wait ratio (typical for agents calling LLMs)
  • Bursty traffic with idle periods
  • Need agent-specific infrastructure (memory, tools, observability)
  • Development time/cost is a factor (AgentCore provides these services)

ECS Fargate wins when:

  • Steady, high-utilization workloads (constantly processing)
  • Already have agent infrastructure built
  • Need maximum control over runtime environment
  • Can optimize container utilization to minimize idle time

Breakeven Analysis:

Consider an agent that runs for 60 seconds per session:

  • 30% active CPU (18 seconds): Processing, orchestration
  • 70% I/O wait (42 seconds): Waiting for LLM, tools

AgentCore cost: Billed only for 18 seconds of CPU Fargate cost: Billed for full 60 seconds of CPU

At these ratios, AgentCore’s higher per-unit rate (~$0.09 vs ~$0.04) is offset by billing only for active CPU. AgentCore becomes cheaper when I/O wait exceeds ~55%.

Development cost factor: Building memory, tool integration, observability, and policy enforcement from scratch on Fargate can cost weeks to months of engineering time. Factor this into TCO.

Operational Complexity

AgentCore Operations

Setup Complexity: Low to Medium

  1. Write agent code using your preferred framework (LangGraph, LlamaIndex, etc.)
  2. Configure AgentCore services (Runtime, Memory, Gateway, etc.)
  3. Deploy via direct code upload (faster) or container image (more control)
  4. Connect tools via Gateway (APIs, Lambda, MCP servers)

Ongoing Operations: Minimal

  • No infrastructure patching (AWS handles)
  • No scaling configuration (automatic with session isolation)
  • Built-in observability with CloudWatch + OTEL
  • Built-in memory management
  • Policy and Evaluations for governance (Preview)

Development Workflow:

# Option 1: Direct code upload (faster iteration)
aws bedrock-agentcore deploy-code \
  --runtime-id RUNTIME_ID \
  --code-zip agent-code.zip

# Option 2: Container deployment
docker build -t my-agent .
docker push $ECR_REGISTRY/my-agent:latest
# Update AgentCore runtime configuration

Operational Burden: 🟢 Low

Note: Direct code upload (new late 2025) significantly speeds up iteration vs container-only deployment.

ECS Fargate Operations

Setup Complexity: Medium to High (for agents)

  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
  9. Build agent infrastructure: memory system, tool integration, observability, policy enforcement

Ongoing Operations: Higher

  • Dependency management (library updates, security patches)
  • Container image rebuilds and deployments
  • Scaling policy tuning
  • Manual health check configuration
  • Custom monitoring setup
  • Memory system maintenance
  • Tool integration updates
  • Platform version migrations (e.g., 1.3.0 → 1.4.0 by March 2026)

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 (compute) / 🔴 High (full agent stack)

Winner: AgentCore for Agent Workloads

For AI agents specifically, AgentCore significantly reduces operational overhead by providing memory, tools, observability, and policy as managed services. If you’re running general containers (not agents), ECS Fargate remains simpler since you don’t need agent-specific infrastructure.

Flexibility and Control

AgentCore Flexibility (Major Update)

Model Agnostic (No Lock-in):

  • ✅ Use ANY foundation model: Bedrock, OpenAI, Anthropic direct, Google Gemini, Azure OpenAI
  • ✅ Self-hosted models supported
  • ✅ Combine multiple providers in one agent
  • ✅ Switch models without infrastructure changes

Framework Agnostic:

  • ✅ Use LangGraph, LangChain, LlamaIndex, CrewAI, Strands Agents
  • ✅ Custom frameworks and code supported
  • ✅ Full control over orchestration logic
  • ✅ MCP (Model Context Protocol) support for tools

Integration Flexibility:

  • Gateway wraps any API, Lambda, or MCP server into agent-compatible tools
  • IAM and OAuth authorization supported
  • Direct API calls from agent code
  • Custom tool implementations

Constraints:

  • ⚠️ Runtime environment managed by AWS (less OS-level control)
  • ⚠️ Some features still in Preview (Policy, Evaluations)
  • ⚠️ Session limit of 8 hours (longer workflows need session chaining)
  • ⚠️ Regional availability (9 regions as of October 2025)

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
  • Full OS-level control in containers

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:

  • Unlimited session duration
  • Complex state machines and workflows
  • 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: Tie (Different Trade-offs)

AgentCore is now model-agnostic and framework-agnostic, eliminating the previous flexibility gap. Choose based on:

  • AgentCore: When you want managed agent infrastructure (memory, tools, observability) without building it
  • ECS Fargate: When you need OS-level control, unlimited sessions, or multi-cloud portability

Performance Characteristics

AgentCore Performance

Latency:

  • Cold start: Session initialization varies; direct code deployment faster than containers
  • Warm sessions: Low overhead for request routing
  • Bidirectional streaming: Supports real-time voice/conversational agents
  • Total: Competitive with Fargate for interactive workloads

Session Duration:

  • Up to 8 hours per session
  • Session isolation provides security boundaries
  • Long-running workflows supported natively

Throughput:

  • Concurrent session limits (account-level quotas)
  • Automatic scaling to thousands of concurrent sessions
  • Request quota increases through AWS support

Optimization:

  • Use direct code upload for faster iteration
  • Leverage built-in memory to reduce context reconstruction
  • Use streaming for faster perceived response
  • Session persistence avoids repeated cold starts

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 latency regardless of compute platform
  • Total: 10-50ms overhead when warm, plus model latency

Session Duration:

  • Tasks can run indefinitely
  • Stop timeout limited to 120 seconds for graceful shutdown
  • Must implement session management yourself

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 with proper capacity

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 agents: AgentCore provides session isolation and built-in features
  • High sustained load: Fargate with proper capacity planning
  • Long-running (>8 hours): Fargate if single-session required; AgentCore with session chaining
  • Bursty traffic: AgentCore scales more elastically
  • Voice/streaming: AgentCore’s bidirectional streaming is purpose-built

Development Experience

AgentCore Development

Local Development:

  • Your agent code runs locally (you control your framework code)
  • AgentCore services (Memory, Gateway) require AWS connection for full testing
  • Can mock AgentCore services for unit testing

Testing:

  • Unit testing for your agent logic (standard testing)
  • Integration testing against AgentCore services
  • Built-in Evaluations for production quality monitoring
  • AgentCore Observability provides tracing and metrics

Debugging:

  • Standard debuggers for your agent code
  • CloudWatch logs and OpenTelemetry traces
  • Observability dashboard for agent workflow visibility

Iteration Speed:

  • Direct code upload: Fast iteration without container builds (new feature)
  • Container deployment: Slower (build → push → deploy cycle)
  • Recommendation: Use direct code upload during development, containers for production

CI/CD:

# Example GitHub Actions for AgentCore
- name: Deploy Agent Code
  run: |
    zip -r agent-code.zip src/
    aws bedrock-agentcore deploy-code \
      --runtime-id $ \
      --code-zip agent-code.zip

# Or container-based deployment
- name: Build and Deploy Container
  run: |
    docker build -t my-agent .
    docker push $ECR_REGISTRY/my-agent:$
    # Update AgentCore runtime configuration

Learning Curve: Medium (AgentCore concepts + your framework)

ECS Fargate Development

Local Development:

  • Run containers locally with Docker
  • Full development environment on laptop
  • Faster iteration without cloud dependencies
  • Must build all agent infrastructure locally too

Testing:

  • Standard unit testing for all code
  • Integration tests with mocked services
  • Contract testing for external APIs
  • Full control over test environment
  • No built-in agent evaluation—build your own

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 + building agent infrastructure)

Winner: Depends on What You’re Building

  • Agent-specific features needed: AgentCore’s built-in Evaluations, Memory, and Observability reduce development time
  • Maximum local control: Fargate allows full local development but you build everything
  • Rapid prototyping: AgentCore’s direct code upload now enables faster iteration

Security and Compliance

AgentCore Security

Built-in Security:

  • Session isolation between agent executions
  • IAM-based access control
  • VPC connectivity and PrivateLink support
  • Encryption at rest and in transit (managed by AWS)
  • CloudTrail logging of all API calls
  • Resource tagging for governance

AgentCore Identity:

  • Dedicated Identity service for agent-to-tool authorization
  • IAM and OAuth support for tool access
  • Least-privilege enforcement

AgentCore Policy (Preview):

  • Deterministic controls defined in natural language or Cedar
  • Intercepts tool calls in real-time
  • Enforces boundaries on agent behavior
  • Operates outside agent code (cannot be bypassed)

AgentCore Evaluations:

  • Automated monitoring for safety, correctness, goal success
  • Live sampling of agent behavior
  • Custom evaluators for domain-specific requirements

Compliance:

  • Inherits AWS compliance certifications (SOC2, HIPAA, etc.)
  • Data residency depends on AWS region
  • Available in 9 regions globally

ECS Fargate Security

Built-in:

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

Custom Implementation Required:

  • Implement authentication and authorization
  • Build guardrails and policy enforcement
  • Custom threat detection
  • Rate limiting and abuse prevention
  • Agent behavior monitoring

Compliance:

  • Full control over data handling
  • Can implement specific compliance requirements
  • Auditability through custom logging
  • Option to deploy to other environments

Responsibility:

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

Winner: AgentCore for Agent Security

For AI agents specifically, AgentCore’s built-in Policy, Identity, and Evaluations provide security features that would take significant effort to build on Fargate. Policy enforcement that operates outside agent code is particularly valuable—agents cannot bypass their own guardrails.

Use Case Recommendations

Use Bedrock AgentCore When:

Production AI Agents:

  • Customer support chatbots with memory across sessions
  • Internal knowledge assistants with tool integrations
  • Multi-agent systems with agent-to-agent communication
  • Voice/conversational agents requiring bidirectional streaming
  • Agents requiring governance, policy controls, and evaluation

Requirements:

  • Need agent-specific infrastructure (memory, tools, observability)
  • Want minimal operational overhead
  • Require built-in security, policy enforcement, and evaluation
  • High I/O wait workloads (typical for LLM-based agents)
  • Need session isolation and governance
  • Building with any model (AgentCore is model-agnostic)

Example: Enterprise data migration assistant that maintains context across sessions, integrates with multiple internal APIs via Gateway, has policy controls preventing destructive operations, and is monitored for accuracy via Evaluations.

Use ECS Fargate When:

Existing Container Infrastructure:

  • Already have significant ECS/Fargate investment
  • Agent is one component in larger containerized system
  • Need unified deployment pipeline with other services

Special Requirements:

  • Sessions longer than 8 hours without interruption
  • Need OS-level customization or specific dependencies
  • Multi-cloud or on-premise deployment required
  • Highly optimized, steady-state workloads where CPU utilization is consistently high
  • Full control over every aspect of runtime environment

Cost Optimization:

  • Very high volume, steady workloads where Fargate’s lower per-unit rate wins
  • Can achieve >55% CPU utilization (above this, Fargate beats AgentCore’s I/O-wait savings)

Example: High-throughput document processing pipeline running 24/7 with consistent load, already integrated into existing ECS infrastructure, where the team has already built memory and tool integration systems.

Hybrid Approaches

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

Pattern 1: Agents + Services

  • AgentCore for AI agents (leverage memory, tools, observability)
  • ECS Fargate for backend services (APIs, data processing)
  • Communication via Gateway, SQS, or EventBridge

Pattern 2: Tiered by Requirements

  • Standard agents on AgentCore (get managed infrastructure)
  • Highly customized or long-running workloads on Fargate
  • Shared tool implementations (Lambda/APIs accessible by both)

Pattern 3: Migration Path

  • Start with AgentCore for faster time-to-production
  • Move specific workloads to Fargate only if needed (>8hr sessions, specific runtime requirements)
  • Many agents stay on AgentCore permanently (majority of cases)

Pattern 4: Multi-Agent Architecture

  • Orchestrator agents on AgentCore (leveraging A2A protocol)
  • Specialized processing agents on Fargate where needed
  • Unified observability via AgentCore + CloudWatch

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.