I've been evaluating observability pipelines for our startup environment (currently 10 servers, mix of AWS EC2 and Kubernetes). The goal is structured log collection with some transformation capabilities before data reaches our SIEM. After running both Cribl Stream and Graylog through their paces, here's my benchmark data.
**Throughput & Resource Usage**
- **Cribl Stream**: Processed 15,000 EPS (events per second) on a t3.large instance with 2 vCPUs, 8GB RAM. CPU hovered around 45% during sustained load.
- **Graylog**: Same instance specs, managed 9,500 EPS before hitting 85% CPU utilization. Required additional Elasticsearch nodes for comparable performance, increasing infrastructure footprint.
Key configuration difference: Cribl's built-in aggregation reduced outbound volume by ~40% using simple pipelines like:
```javascript
// Sample Cribl pipeline function
function reduce_nginx_logs(e) {
let status = e.status;
// Aggregate counts by status code per minute
aggregate({
groupBy: [status, timestamp_minute],
aggregations: { count: {count: '*' } }
});
}
```
Graylog achieved similar reduction but required separate Lambda functions or stream processing rules that added latency.
**Cost Analysis for 50GB Daily Ingest**
- Cribl Cloud (Pay-as-you-go): ~$1.80/GB processed, estimated $2,700/month
- Graylog Open Source: $0 for software, but requires 3-node Elasticsearch cluster (~$600/month on AWS) plus operational overhead
- Graylog Enterprise: Starts at $2,500/month for 50GB/day with support
**Operational Considerations**
- Cribl's pipeline configuration is purely YAML/JSON - integrates cleanly with our existing GitOps workflows
- Graylog requires more manual dashboard configuration, though its alerting system is more mature out-of-the-box
- Both handle common log formats (syslog, JSON, Windows Event Log) equally well
For startups needing maximum flexibility in data routing (S3, Datadog, Splunk, etc.), Cribl's pipeline model seems superior. Graylog presents a more integrated solution if you need search/alerting immediately without assembling multiple tools.
Has anyone conducted similar benchmarks at scale? I'm particularly interested in how these platforms handle schema enforcement and partial parsing failures.
Numbers don't lie
I run a 50-person fintech's data stack (Airflow, dbt, Snowflake) and we handle observability pipelines for about 200 servers. We tested both before settling on Graylog three years ago.
1. **Target Audience & Hidden Costs:** Cribl is built for enterprise teams with dedicated data engineers and a budget to match. List pricing starts ~$50k/year. Graylog is SMB/mid-market; their open core model means you can run the free version until you need SSO or support, then it's $2-4k/year for their enterprise features on your own infra.
2. **Deployment Friction:** Graylog is a packaged appliance (VMs or containers). You can have it ingesting logs in an afternoon. Cribl is more flexible but expects you to build and maintain pipeline logic. Initial setup took us a week to get routing right.
3. **Performance Ceiling:** Your benchmarks match my experience. Graylog's single-node performance falls off around 10k EPS. To get to 15k EPS, you *need* that separate Elasticsearch cluster, which adds 3 more nodes. Cribl does more with less compute, but you pay for that efficiency in licensing.
4. **The Real Limitation:** Cribl's power is its streaming transformations. If you just need to collect, parse, and route, it's overkill. Graylog's built-in extractors and streams are simpler but cover 80% of use cases. Where Cribl clearly wins is reducing egress costs to your SIEM by filtering/aggregating upstream.
I'd pick Graylog for a 10-server startup. It's cheaper, simpler, and your scale doesn't justify Cribl's complexity or cost. Switch only if your SIEM costs skyrocket and you need advanced pre-aggregation to throttle volume.
SQL is enough