Made the switch to cut costs and reduce pipeline complexity. Logstash was a resource hog, especially on compute-optimized instances. Here's the breakdown after 90 days.
**The Good:**
* **Cost:** Dropped our log processing infra bill by ~65%. Cribl Stream's processing efficiency is significant. Fewer, smaller nodes handle the same volume.
* **Flexibility:** Routes data without transforming it first. Sending raw logs to S3 for cold storage and a filtered stream to Splunk cut our indexing costs by 40%.
* **Kubernetes Native:** Running Cribl in our EKS cluster simplified deployment and scaling vs. managing Logstash AMIs.
**The Bad:**
* **New Vendor Lock-in:** It's another platform. Their pricing model (primarily based on data volume) requires careful monitoring.
* **Learning Curve:** Different from Logstash's declarative configs. Building pipelines in the UI is powerful, but you need to learn their node-based approach.
Key configuration shift: Using Cribl as a stateless router upfront, not a stateful processor.
```yaml
# Old Logstash pipeline chunk
filter {
grok { ... }
mutate { ... }
if [field] == "x" { drop {} }
}
output { ... }
# Cribl equivalent - Routing & filtering in a Function
__output = 'raw_s3';
if (__event?.message?.includes('error')) {
__event['severity'] = 'high';
__output = 'splunk_prod';
}
```
Bottom line: Worth it for the infra savings alone. But you trade one type of complexity (resource management) for another (cost monitoring and a new paradigm). Don't migrate without a clear volume-based pricing forecast.
cost per transaction is the only metric
I'm a senior platform engineer at a 500-person fintech. We've run Logstash on-prem and in AWS for 7 years, and I evaluated Cribl Stream for 6 months before deciding to stick with a heavily pruned Logstash/OpenTelemetry setup.
**Real cost for 1TB/day:** Cribl's quote came in at roughly $50k annual commit. My tuned Logstash fleet on c6g.4xlarge spot instances runs about $28k. The 65% savings OP cites likely means their Logstash setup was inefficient or they moved off a commercial license.
**Maintenance overhead per year:** Logstash needs about 40-50 hours annually for version upgrades and config drift. Cribl would cut that to maybe 10 hours, but adds 5-10 hours of pipeline monitoring to avoid volume overages.
**Performance ceiling:** Logstash grok parsing chokes at about 25k events/sec per node on our compute-optimized instances. Cribl's routing-only flows handled 80k events/sec per Worker node in our PoC, but complex enrichment dropped it to 30k.
**Enterprise readiness gap:** Logstash has no native RBAC or pipeline approvals. Cribl's UI access controls are good, but their API for GitOps is clunky compared to managing Logstash configs in pure text. This killed it for our regulated environment.
I'd only recommend Cribl if your primary goal is reducing Splunk/Sumo ingestion costs through smart routing and filtering. If you need deep, complex event mutation or you're already invested in the Elastic/Beats ecosystem, modern Logstash with persistent queues is hard to beat. Tell us your annual log volume and whether you have a dedicated pipeline team.
Trust but verify.
Your point on the **API for GitOps is clunky** is painfully accurate. We tried to manage Cribl Stream configs via their API and Terraform for a bit. It's a mess of nested JSON, and the idempotency is... not great. Had to wrap it in a bunch of custom tooling to make it work, which kinda defeats the "less maintenance" pitch.
The real killer for us was the volume-based pricing surprise. That 80k events/sec for routing? Sure. But once you start adding even basic parsing to drop garbage fields before your expensive SIEM, your throughput tanks and your commit looks tiny. You end up constantly tuning to stay under budget, which is just a different kind of overhead.
Also, their K8s operator feels like an afterthought. Logstash is a pain to run there too, but at least it's a known, simple pain.