Choosing the right logging pipeline is essential to observability and operational efficiency. In AWS-based stacks, teams often consider FluentD and CloudWatch Logs for centralized log aggregation β but they serve different roles and come with tradeoffs.
π FluentD Overview
- Open-source log collector with powerful routing and filtering
- Supports custom parsing, transformation, and enrichment
- Can forward logs to S3, Elasticsearch, Datadog, Loki, and more
- Runs as a daemonset in Kubernetes or as an agent on EC2
π CloudWatch Logs Overview
- Fully managed AWS-native logging service
- Integrated with AWS services, IAM, and CloudWatch metrics
- Built-in dashboards, metric filters, and retention policies
- Easy to connect to Lambda, ECS, and API Gateway
π‘ When to Use FluentD
- You need to route logs to multiple backends (S3 + Loki + Datadog)
- You want full control over log formats and filtering
- You run self-managed Kubernetes and prefer sidecar/daemonset architecture
- You are building a vendor-agnostic observability stack
π‘ When to Use CloudWatch Logs
- You want simple integration with AWS-native services
- You need basic logs + metrics + alerting in one place
- Youβre in a serverless or ECS/Fargate environment
- You want to avoid managing agents or infrastructure
π Cost Considerations
Factor | FluentD | CloudWatch Logs |
---|---|---|
Ingestion Cost | Free, but depends on destination (e.g. S3, Loki) | Paid per GB ingested |
Retention | Configurable via destination | Paid per GB/month stored |
Alerting | Requires additional tooling (e.g., Prometheus, Loki) | Built-in via Metric Filters & Alarms |
Operational Overhead | Higher β must deploy and manage FluentD | Low β fully managed by AWS |
π Best Practice
Use CloudWatch Logs by default in AWS-native or low-scale environments. Switch to FluentD when you need flexibility, advanced routing, or are managing multicloud workloads.
In many cases, teams run both, CloudWatch for operational visibility, and FluentD to forward logs into a centralized external platform.