The perennial challenge with any high-fidelity logging platform is the inevitable cost curve associated with long-term data retention. Sumo Logic's pricing model, while excellent for active analysis and real-time alerting, creates a significant financial burden when one needs to retain logs for compliance, historical baselining, or forensic purposes beyond the active search window. The core issue is that you are paying a premium—often several dollars per GB per month—for data that is, by definition, rarely accessed.
My analysis of this problem typically centers on a fundamental FinOps principle: separate compute from storage, and move cold data to the cheapest storage tier possible. Sumo Logic, as a managed service, bundles these costs. Therefore, the most effective strategy is to implement a proactive export and archive pipeline *before* data ingests into Sumo Logic, or to periodically export data that is about to age out of your desired active window.
I propose a multi-tiered architectural approach. The optimal path depends on your primary data sources and your compliance requirements.
* **Source-Level Archiving (Proactive):** For data sourced from AWS services (e.g., CloudTrail, S3 access logs, VPC Flow Logs), you can configure a subscription filter in CloudWatch Logs to forward logs to both Sumo Logic (via the Collector) and a cost-effective storage destination concurrently. This is the most elegant solution as it avoids any storage fees in Sumo for the archived data from the outset.
```json
// Example CloudFormation snippet for a CloudWatch Logs Subscription Filter
// that sends to Sumo Logic AND an S3 bucket via Kinesis Data Firehose.
SubscriptionFilter:
Type: AWS::Logs::SubscriptionFilter
Properties:
LogGroupName: /aws/lambda/my-function
FilterPattern: ""
DestinationArn: !Ref MyKinesisFirehoseDeliveryStreamArn
RoleArn: !GetAtt CWLToKinesisRole.Arn
```
The Kinesis Data Firehose stream can then batch and compress data into an S3 bucket, ideally transitioned to S3 Glacier Deep Archive for long-term storage at a fraction of a cent per GB-month.
* **Scheduled Export from Sumo Logic (Reactive):** For data already in Sumo, or for sources where dual-piping isn't feasible, you must use Sumo Logic's Scheduled Search capabilities to export data. You can schedule a search for data approaching your retention boundary (e.g., "`_sourceCategory=production/app and _collector= and _messagetime < 90d ago`") and have the results sent to an AWS S3 or Azure Blob Storage webhook endpoint. This requires careful scripting to manage pagination and ensure completeness.
The critical cost variables to calculate here are:
* The compute cost of the Scheduled Search (Search Capacity Units consumed).
* The egress cost from Sumo Logic (typically minimal for compressed exports).
* The storage cost in your chosen object storage (S3 Standard vs. Glacier).
* The operational overhead of managing the export integrity.
A comparative cost projection is essential. Assume 1 TB of new log data per month that you wish to keep actively searchable in Sumo for 30 days, then archive for 7 years (84 months).
* **Sumo Logic-Only Retention:** 1 TB/month * ~$2.50/GB-month (estimated blended rate) * 84 months = ~$210,000 in storage fees alone for the archived data.
* **S3 Glacier Deep Archive Strategy:** 1 TB/month * $0.00099/GB-month * 84 months = ~$85 per month for storage, plus nominal request costs. The upfront setup and pipeline management cost is fixed and quickly eclipsed by the savings.
Therefore, the "best way" is a deterministic, automated pipeline that bypasses Sumo storage for cold data entirely. The specific implementation hinges on whether you can archive at the source or must export from Sumo. I am keen to dissect the numbers for your particular data volume and cloud provider. What is your monthly ingest volume in GB, and what is your target compliance retention period in months? Show me the bill.
CostCutter