A NAT Gateway is the recommended AWS-managed service for this scenario. It is highly available within a single Availability Zone (and can be deployed across AZs for resilience), scales automatically, and requires no ongoing management, meeting the requirements for high availability, cost-effectiveness (compared to managing instances), and low operational overhead. While a NAT instance offers more control, it introduces management burden. Public IPs on private instances are a security anti-pattern. VPC Endpoints are excellent for AWS services but don’t help with public internet repositories.

For a fault-tolerant, interruptible, and time-flexible batch job like this, Spot Instances offer the lowest possible cost (often 70-90% off On-Demand). Using a diverse fleet of instance types across multiple Availability Zones increases the chance of acquiring the necessary capacity even if one instance type or AZ has limited Spot capacity, helping ensure the job completes within the window. On-Demand is more expensive. Reserved Instances are for steady-state workloads. Dedicated Hosts are for licensing or compliance needs, not cost-saving for this use case.

When instances are reported as healthy by the ALB but return 5xx errors, the issue is typically at the application layer after the health check passes. During a traffic spike, the application may exhaust resources (like database connections, threads, or memory), leading to timeouts or internal errors when the ALB tries to send it a request. This manifests as 5xx errors generated by the ALB. Checking instance-level CPU alone is insufficient; application performance metrics are key. An expired cert would cause connection failures, not sporadic 5xx. ALB capacity scales automatically.

SSE-KMS with a customer-managed key meets all requirements: encryption at rest is satisfied, KMS allows for annual rotation of the customer-managed key, and bucket policies can enforce access control based on IAM roles. S3 server access logging provides access logs for audit. SSE-S3 does not allow key rotation management. CloudTrail logs API calls but S3 server access logs are more specific for object access. Client-side encryption adds complexity and doesn’t integrate as seamlessly with bucket policies for access control based on KMS key permissions.

Amazon CloudFront is a Content Delivery Network (CDN) designed for this exact scenario. It caches content at edge locations worldwide (including in Europe), so subsequent requests for assets are served from the edge with low latency. It requires minimal to no application code change—just the asset URLs. It offers predictable costs via the CloudFront pricing model. S3 Transfer Acceleration optimizes the upload path to a single region, not global downloads. Cross-region replication or migration solves latency but is more disruptive, costly for storage duplication, and may require significant application reconfiguration.

AWS Fargate is a serverless compute engine for containers. When used with Amazon ECS, it allows the team to run their microservices as containers without managing the underlying EC2 instances (no patching, scaling, or securing of servers). ECS can automatically scale the number of tasks (containers) based on load. While Lambda is also serverless, it is for event-driven, short-running functions, not for long-running containerized microservices without significant refactoring. EC2 with ASG or Spot Fleets requires managing the server infrastructure.

Amazon RDS Read Replicas provide a dedicated, read-only copy of the primary database. They are ideal for offloading read-heavy traffic like reporting or analytical queries. The application can be configured to send read queries to the replica’s endpoint without code changes to the write path. While there is replication lag, analytical queries are often tolerant of slightly stale data. Increasing the primary size is costly and doesn’t separate workloads. RDS Proxy helps with connection management but doesn’t offload query compute. Manual snapshots are not a real-time solution.

This architecture leverages the strengths of multiple AWS streaming services. Kinesis Data Streams provides durable, scalable real-time data ingestion. Kinesis Data Analytics can process this stream in real-time (e.g., using SQL) to generate alarms. Kinesis Data Firehose can consume from the same stream to reliably batch and deliver the raw data to S3 for long-term storage and batch analysis. This is a canonical, fully managed IoT data pipeline. Kinesis Data Firehose alone doesn’t support complex real-time processing. SQS is for messaging, not high-throughput streaming. Amazon MQ is for legacy protocols, not typically for massive IoT ingestion.

AWS Network Firewall is a managed, stateful firewall service. You can deploy firewall endpoints in each Availability Zone for resilience. By creating a new subnet route table for the private subnets that sends internet-bound traffic (0.0.0.0/0) to the firewall endpoint’s network interface in that AZ, the traffic is inspected before being passed on to the NAT Gateway (which remains in place). This creates a highly available inspection layer. Using a NAT instance or self-managed proxy fleet introduces management overhead and potential single points of failure if not carefully architected.

A NAT Gateway is the recommended AWS-managed service for this scenario. It is highly available within a single Availability Zone (and can be deployed across AZs for resilience), scales automatically, and requires no ongoing management, meeting the requirements for high availability, cost-effectiveness (compared to managing instances), and low operational overhead. While a NAT instance offers more control, it introduces management burden. Public IPs on private instances are a security anti-pattern. VPC Endpoints are excellent for AWS services but don’t help with public internet repositories.0