Cut Your EDC Connector Costs by 58% on AWS A Practical Guide

Why EDC Connector Costs Matter

Deploying Eclipse Dataspace Components (EDC) connectors on AWS enables secure, sovereign data sharing across organizations. But without clear cost benchmarks, you risk over-provisioning or unexpected bills. This guide breaks down the cost drivers and shows you how to optimize your deployment using AWS Well-Architected principles.

We'll compare two scenarios: business-critical (high availability) and non-critical (cost-sensitive). You'll see exactly where your budget goes and how to cut it by up to 58% without sacrificing data sovereignty.

Key insight: The biggest cost drivers are database (Aurora PostgreSQL), compute (ECS Fargate), and load balancing—these are baseline infrastructure costs, not usage-based charges. Optimizing them yields the highest impact.

Cost Breakdown: Business-Critical vs Non-Critical

Here's a side-by-side comparison based on a realistic baseline (5 GB data, 20 GB/month network traffic, 100K API calls/month, single region eu-south-2).

What this tells you:

• Aurora PostgreSQL is the #1 cost driver in both scenarios. Rightsizing from db.r6g.large to db.t4g.medium cuts database cost by 60%.
• Switching from on-demand Fargate to Fargate Spot reduces compute cost by nearly 70%.
• Network Load Balancer is a fixed cost—optimize data transfer patterns to get more value.
• Other services (S3, API Gateway, etc.) are marginal at these volumes, meaning your architecture scales efficiently as usage grows.

# Example: Estimate monthly cost with AWS Pricing Calculator API (pseudo-code)
# This illustrates how you might programmatically compare configurations

def estimate_edc_cost(instance_type, fargate_spot=False):
    aurora_cost = {
        'db.r6g.large': 276.00,
        'db.t4g.medium': 110.00
    }[instance_type]
    
    fargate_cost = 26.00 if fargate_spot else 83.00
    
    total = aurora_cost + fargate_cost + 20.00 + 4.00 + 2.25 + 1.00 + 0.40 + 0.10
    return total

print(f"Business-critical: ${estimate_edc_cost('db.r6g.large'):.2f}")
print(f"Non-critical: ${estimate_edc_cost('db.t4g.medium', fargate_spot=True):.2f}")

For a deeper dive into the architecture patterns behind these numbers, see our production-ready EDC deployment guide.

Well-Architected Optimization Strategies

Performance Efficiency

• Right-size compute: Start small and scale based on CloudWatch Container Insights metrics. Don't over-provision.
• Use Aurora Serverless v2 for variable workloads—it auto-scales capacity and eliminates peak provisioning.
• Optimize data transfer: Use S3 Transfer Acceleration for large geographic transfers and compress data where possible.

Cost Optimization

• Fargate Spot for fault-tolerant workloads: Save up to 70% for non-critical environments, batch jobs, and dev workloads. Implement graceful shutdown to handle Spot interruptions.
• S3 Lifecycle policies: Transition infrequently accessed data (e.g., historical logs) to S3 Intelligent-Tiering or Glacier Instant Retrieval.
• Monitor with AWS Cost Explorer and Budgets: Tag EDC resources consistently to allocate costs and detect anomalies.
• Savings Plans: For predictable, steady-state usage, lock in discounts on Aurora and Fargate.

Sustainability

• Maximize utilization: Use auto-scaling and shut down non-production environments outside business hours.
• Choose Graviton instances: r6g and t4g families offer better price-performance and energy efficiency than x86 equivalents.
• Minimize data movement: Cache frequently accessed catalog data locally using the Federated Catalog and batch operations to reduce network round trips.

Critical caveat: These estimates assume a single region and no cross-region replication. If you need disaster recovery with cross-region replication, costs will increase significantly. Always validate with the AWS Pricing Calculator for your specific region and usage.

Key Takeaways and Next Steps

• Identify your cost drivers first: Aurora PostgreSQL, ECS Fargate, and NLB account for over 90% of baseline costs. Focus optimization efforts there.
• Match configuration to workload criticality: Non-critical environments can run on Fargate Spot and smaller Aurora instances, cutting costs by 58%.
• Leverage AWS Well-Architected pillars: Performance efficiency, cost optimization, and sustainability are not afterthoughts—they're design decisions.
• Start small and iterate: Use the AWS Pricing Calculator to model your specific data volumes and regions.

Next steps:

1. Assess your workload criticality (business-critical vs non-critical).
2. Use the AWS Pricing Calculator to estimate your costs.
3. Explore the Dataspace Connector on AWS project for an end-to-end reference implementation.

For related insights, check out our LLM evaluation infrastructure guide and our SAP + Microsoft AI enterprise trends.