AWS Architecture Blog·July 17, 2026Cost Optimization and Well-Architected Principles for Eclipse Dataspace Components on AWS
This article focuses on cost optimization strategies and applying the AWS Well-Architected Framework pillars to Eclipse Dataspace Components (EDC) deployments on AWS. It provides cost estimations for business-critical and non-critical workloads, highlighting major cost drivers like database and compute resources, and offers practical advice for improving performance efficiency, cost optimization, and sustainability in data space architectures.
Read original on AWS Architecture BlogDeploying distributed systems like Eclipse Dataspace Components (EDC) on cloud platforms like AWS involves significant infrastructure cost considerations. This article, the third in a series, provides a deep dive into cost optimization, performance efficiency, and sustainability within the context of EDC connector deployments, aligning with the AWS Well-Architected Framework.
Understanding EDC Cost Drivers and Reference Architecture
Data spaces enable secure, sovereign data sharing between independent organizations. Infrastructure costs are primarily driven by performance and reliability requirements, as well as data volume and velocity. The article focuses on the participant-side EDC connector deployment, not the central Dataspace Governance Authority. The reference architecture, detailed in Part 2 of the series, leverages AWS services such as Amazon ECS with Fargate for compute, Amazon Aurora PostgreSQL for data storage, Network Load Balancer, AWS Secrets Manager, Amazon Cognito, Amazon ECR, Amazon API Gateway, and Amazon S3.
Cost Estimation Scenarios
The article presents two distinct cost scenarios to illustrate optimization potential:
- Business-critical workloads: Designed for high availability, performance, and reliability, supporting critical business functions. This scenario uses robust, always-on resources like `db.r6g.large` Aurora instances and continuous Fargate tasks.
- Non-critical workloads: Suitable for development, testing, or less critical production environments that tolerate interruptions. This scenario reduces costs significantly by rightsizing resources (e.g., `db.t4g.medium` Aurora) and utilizing cost-effective options like AWS Fargate Spot capacity. The article demonstrates up to a 58% cost reduction in this setup.
| AWS Service | Business-Critical Monthly Cost (USD) | Non-Critical Monthly Cost (USD) |
|---|
Key Cost Takeaways
The primary cost drivers in both scenarios are database (Amazon Aurora) and compute (Amazon ECS with AWS Fargate). Load balancing (Network Load Balancer) also contributes notably. Services like Amazon S3, API Gateway, and basic data transfer are marginal contributors at typical volumes, indicating the architecture scales efficiently with increased usage without proportional cost spikes for these components.
Well-Architected Pillars: Performance, Cost, and Sustainability
Beyond cost, the article discusses applying the AWS Well-Architected Framework's Performance Efficiency, Cost Optimization, and Sustainability pillars to EDC deployments:
- Performance Efficiency: Emphasizes right-sizing compute resources by starting small and scaling based on metrics, using AWS Fargate for serverless container management, leveraging Aurora's read replicas for database scaling, and optimizing network performance by selecting appropriate instance types and minimizing cross-AZ traffic.
- Cost Optimization: Focuses on rightsizing resources, utilizing managed services to offload operational overhead, implementing serverless solutions like AWS Fargate Spot for non-critical workloads, and considering reserved instances or Savings Plans for predictable usage.
- Sustainability: Encourages efficient resource utilization, choosing energy-efficient instance types (e.g., Graviton processors), leveraging managed services, and designing for elasticity to scale down during low demand to minimize environmental impact. Data lifecycle management in S3 is also crucial for sustainability.
These strategies collectively aim to reduce both financial expenditure and environmental footprint while maintaining operational excellence and reliability for data space architectures.