Having recently completed a technical assessment for a client considering a Cribl Stream deployment in front of their existing Sumo Logic infrastructure, I can provide a detailed, evidence-based perspective on cost reduction. The short answer is a qualified yes, but the magnitude of savings is entirely dependent on your data characteristics, retention policies, and willingness to re-architect some data flows.
The primary cost-saving mechanisms are data reduction at the edge and intelligent routing. Sumo Logic's pricing is based on daily ingest volume, so any reduction before the data hits their collectors directly lowers the bill. Cribl enables this through:
* **Filtering & Dropping:** Eliminating verbose, low-value logs (e.g., repetitive health checks, debug-level entries from non-critical systems) at the source.
* **Sampling:** Applying deterministic or random sampling to high-volume, repetitive telemetry where 100% fidelity isn't required for observability.
* **Data Minimization:** Using `reduce` or `json` functions to strip unnecessary nested fields, whitespace, and null values from structured logs.
* **Re-routing:** Sending a subset of data (e.g., raw, unfiltered logs for compliance) to a cheaper object storage tier (S3, Azure Blob) while sending only the curated, analytics-ready stream to Sumo Logic.
Here is a simplistic but illustrative example of a Cribl Pipeline that performs some basic reduction before Sumo:
```javascript
// Pipeline: Pre-Sumo_Reduction
// Function: Filter out DEBUG logs, sample noisy data, trim fields.
// 1. Drop DEBUG level entries entirely.
filter =>
__inputId === 'prod_app_logs' && level === 'DEBUG' ? drop() : keep();
// 2. Sample 1-in-10 for a specific chatty component.
sample =>
app_name === 'noisy_microservice' && sample(10) ? drop() : keep();
// 3. Remove large, unnecessary nested fields.
eval //
// Keep only necessary fields from the 'request_context' object.
request_summary = {
'id': request_context?.id,
'endpoint': request_context?.endpoint
};
// Delete the original bulky object.
delete('request_context');
// Rename the new summary.
rename('request_summary', 'request_context');
```
The critical consideration is Sumo Logic's own features like LogReduce, Partitioning, and Field Extraction. You must evaluate whether performing these operations upstream in Cribl (reducing ingest) is more cost-effective than using Sumo's post-ingest capabilities. The break-even point hinges on your data's compressibility and the cost of maintaining the Cribl infrastructure (worker nodes, management overhead).
In our assessment, for a ~2 TB/day ingest, we projected a 30-40% reduction in Sumo ingest volume through aggressive filtering of debug logs, sampling of specific audit trails, and truncation of stack traces. This did not include savings from archiving full-fidelity logs to S3, which would have added another 15-20% hypothetical reduction. However, operational complexity increased, requiring careful monitoring of the Cribl streams to ensure no critical data for alerts or dashboards was inadvertently sampled out.
My concluding analysis is that Cribl acts as a strategic buffer, providing granular control over your observability tax. It is not a set-and-forget cost saver; it's a platform that shifts cost optimization from a vendor feature (Sumo) to an engineering discipline. The ROI is positive if you have identifiable waste streams and the operational maturity to manage the data pipeline. I'm interested in others' practical experiences, particularly regarding long-term trend management and whether the reduced ingest volume had any unexpected side effects on Sumo Logic query performance or alert reliability.
- Mike
- Mike