We’ve been running multiple analytics data pipelines on AWS for the past year, and our VPC Flow Logs costs have grown significantly. Currently capturing all traffic across 12 VPCs supporting our data lake infrastructure.
I’m interested in hearing how others handle VPC Flow Log filtering strategies and retention policies for analytics workloads. Our current setup captures everything at 1-minute intervals, which gives us detailed network visibility but the storage and analysis costs are becoming problematic.
Specifically looking at:
- Practical filtering approaches that balance visibility with cost
- Retention policies that work for compliance while managing storage
- Any cost optimization techniques you’ve implemented successfully
What filtering rules have you found most effective for analytics pipeline monitoring? Are there specific traffic patterns you’ve found safe to exclude without losing critical insights?
Cost optimization for Flow Logs really comes down to three levers: filtering, sampling, and retention. We implemented a policy where logs are automatically analyzed after 30 days to identify patterns that could be excluded. Found that roughly 40% of our captured traffic was routine and predictable - things like scheduled data transfers, backup jobs, and monitoring agents. By excluding these known patterns, we maintain security visibility while significantly reducing volume. Also recommend using S3 Intelligent-Tiering for log storage rather than managing lifecycle policies manually.
One approach we’ve used successfully is creating separate Flow Log configurations for different purposes. We have a high-detail capture for security monitoring (reject logs only, kept for 90 days) and a separate sampled configuration for general analytics pipeline performance monitoring. This lets us optimize retention and detail level based on actual use case. For analytics workloads specifically, we found that capturing only traffic to/from our data lake S3 endpoints and EMR clusters gives us what we need without the noise from internal cluster communication.
We faced similar challenges last year. Started by implementing custom filtering to exclude internal health checks and routine service-to-service traffic within our analytics clusters. This alone cut our log volume by about 35%. For retention, we use tiered storage - keep detailed logs for 7 days in S3 Standard, then move to Glacier for 90 days of compliance retention. The key is identifying which traffic patterns actually matter for your analytics monitoring versus what’s just noise.