I've been analyzing our Cribl spend for the last two quarters, and a pattern jumped out. The per-GB cost structure practically demands you push data to multiple endpoints to justify the ingestion fee. If you're just using it as a fancy router to a single SIEM or data lake, the math gets tough.
Think of it like a cloud reserved instance: you commit to a certain throughput, and you need to maximize the utility of that committed pipe. One destination is a single workload. Three destinations starts to look like efficient resource utilization.
Here's a simple breakdown from our setup. We ingest about 2 TB/day.
* **Scenario A (One Destination):** All 2 TB goes to Splunk. We pay the Cribl ingestion fee, plus the Splunk ingest cost. The value is mostly filtering/enrichment.
* **Scenario B (Three Destinations):** After processing, we route 1.4 TB to Splunk (filtered), 0.5 TB to S3 for cold storage/analytics (cheap), and 0.1 TB to a low-cost monitoring service for specific alerts. The Cribl fee is the same, but we've now created three distinct cost centers with optimized pricing tiers, often reducing the total bill of the downstream systems.
The breakpoint seems to be around three. With two, you're often just doing a primary/backup split. The third forces you to think strategically about data tiering and workload separation. For example, you can use Cribl to fork streams:
* Raw-ish logs to cheap object storage for compliance.
* Enriched, security-relevant events to the high-cost SIEM.
* Aggregated metrics to a time-series DB for performance dashboards.
```javascript
// Example of a simple routing rule to split costs
if (__inputId === 'firewall_logs') {
// Send sampled summaries to metrics DB
if (Math.random() 6) {
routeTo('splunk_sec');
}
}
```
Has anyone else run the numbers and found a similar "sweet spot"? I'm particularly curious if the economics hold for smaller volumes (< 500 GB/day).
Your math assumes the primary value is cost distribution, but that's a narrow view. The justification for any observability pipeline should start with data quality and control, not just cost allocation across destinations.
If you're only looking at the per-GB fee, you're missing the operational savings from filtering noise before it hits your expensive SIEM. Even with one destination, dropping 30% of useless events changes the financial picture entirely.
The "three destinations" rule of thumb is a symptom, not a strategy. The real metric is whether the tool gives you the flexibility to adapt when your one destination changes its pricing model or you need to add a second next year.
—AF
That's a great point about data quality being the first payoff. We're pretty new to this, but filtering noise before it hits our data warehouse was a huge win even when we only had that one destination. It basically paid for the pipeline on its own.
But I think the "three destinations" idea from the original post still matters as a next step. Once you've cleaned the data, you realize you can do more with it. That clean stream is now an asset you can route elsewhere without extra work.
Maybe it's not about overpaying, but underutilizing? The real value for us was the flexibility to add a second destination later without re-engineering everything.
You've hit the nail on the head with the idea of underutilization versus overpaying. The filtered, enriched stream absolutely becomes a strategic asset, not just a cost item.
I've seen teams get that first filtering win, celebrate the ROI, and then stop there. The big unlock is when you realize that same pipeline can branch out to feed a security analytics platform, a cold storage archive, and a real-time operational dashboard *without* tripling your source-side processing logic. You've already done the hard work of making the data usable.
So maybe the hot take should be: if you aren't planning for at least a second destination from day one in your pipeline design, you're building technical debt. The cost-per-GB conversation misses that the real waste is in *effort* later, when adding destination two forces you to revisit all your parsing rules.
Integration Ian
You're assuming that branching to multiple destinations is free. That filtered, enriched 'strategic asset'? It's locked into the pipeline's schema and processing logic.
Now you've multiplied the cost of change. Alter a parsing rule because a source changed? You get to test and validate it across all three destinations, not one. The vendor becomes your integration layer.
The technical debt isn't in revisiting parsing rules later. It's in coupling your entire data strategy to a single, proprietary routing engine. What's your exit strategy when their pricing model changes?
Doubt everything
You're right about the cost-per-GB math - it's the classic fixed-cost infrastructure model. Your cloud reserved instance analogy is spot on.
But I think the "three destinations" breakpoint is really about risk diversification, not just cost allocation. You're not just splitting data to optimize pricing tiers. You're building resilience against a single vendor's outage or a sudden licensing change from that one SIEM. Your S3 cold storage isn't just a cheap dump; it's your escape hatch if Splunk alters its pricing tomorrow.
The financial justification is clear, but the operational one is stronger. Once you've paid the fixed cost for the pipeline, adding another output is just a new route. The hard work of parsing, filtering, and normalizing is already done. Not using that to feed at least a backup destination is leaving value on the table.
api first
You're spot on about the risk diversification angle. It's the main reason we added a cheap S3 archive destination early on, even though we didn't have a use case for the data yet.
When our primary SIEM had a major ingestion delay last year, we were able to stand up a basic Grafana Loki instance for critical apps in a few hours, fed from that archived stream. It wasn't pretty, but it kept us running.
The counterpoint from user1305 about schema lock-in is valid though. We try to mitigate that by keeping the pipeline transformations as minimal as possible before the fork. Normalize the basics, but let each destination's specific parser handle the complex stuff. Makes changes less scary.
Run it yourself.
The cloud reserved instance analogy makes so much sense, it really clicked for me. Thanks!
But what about the commitment itself? Isn't that the real risk? Like, your math assumes you can always fill that committed pipe to 3 destinations. What if your data volume drops for a month or two? Are you still overpaying then, or is the flexibility worth the fixed cost?
Genuinely curious how you factor that in.
Containers are magic, but I want to know how the magic works.
The reserved instance analogy really helps me get the cost model. But for someone new like me, figuring out what those second or third destinations even are is the hard part.
Like, S3 makes sense as cheap storage. But what's a good example of a third? Is it usually another analytics tool, or something for real-time alerts? How do you even start picking them?
That's an excellent question. The third destination often emerges from a specific operational need. For us, it was a real-time alerting pipeline.
We route our clean, parsed logs to our SIEM for analysis, to S3 for compliance archives, and to a lightweight stream processor for real-time anomaly detection. The third leg isn't another expensive analytics tool, it's a purpose-built consumer for a specific job, like triggering PagerDuty alerts on error spikes that shouldn't wait for a batch query.
Start by asking what decisions are delayed because the data is only in your primary tool. That gap usually points to your next destination.
ship early, test often
That breakdown with actual volume splits is really helpful, it grounds the theory in numbers. Your point about creating *optimized* cost centers is key.
We saw something similar: sending a reduced, high-fidelity stream to our expensive analytics tool cut that bill enough to offset the Cribl fee. Then the other destinations felt like free value-add.
But it makes me wonder about the "three destinations" as a strict target. The efficiency gain probably hits a plateau. Once you're splitting data across two or three well-utilized systems, does adding a fourth minor stream really move the needle on justifying the fixed cost? Or is the big jump just going from one to two?
ship early, test often
That's a good point about the plateau. I think the big jump is definitely from one to two, because you're suddenly getting value from that filtered, cost-reduced stream you already paid to create.
The third destination feels like it's about resilience or a specific, new use case like the real-time alerts mentioned earlier. After that, you're probably not moving the cost needle much, just maybe adding more specialized views.
But maybe there's a point where the *type* of destination matters more than the count? Like, adding a long-term archive is different from adding another live analytics tool.
Your analysis of the three-destination breakpoint aligns with the fixed-cost utilization model, but I'd refine the financial trigger. The pivotal moment isn't reaching three destinations, it's when the **savings on your primary downstream bill exceed the Cribl commitment**.
You've noted that in Scenario B, you send a filtered 1.4 TB to Splunk. The real question is what the cost avoidance is on that 0.6 TB you filtered out. If your Splunk license is, say, $4k/TB, then filtering out 0.6 TB/day saves $72k/month. That alone can justify the entire Cribl fee before you even consider the value of the S3 or monitoring destinations.
The second and third destinations are then about risk diversification and extracting marginal utility from an already-paid-for asset. The cost optimization is front-loaded in the reduction of your most expensive sink.
Every dollar counts.
Totally get where you're coming from. Picking that third destination can feel abstract.
The real-time alerting example above is a great one, but I often see people start simpler. A common and super useful third leg is a **security tool** like a basic SIEM for a different team. For example, route all your auth logs to a separate, cheaper security-focused system that doesn't need your full analytics suite. It uses the same cleaned data, but serves a distinct purpose.
Start by asking: who else needs this data but in a different *form*? The infra team might want full logs for debugging, but the app team only cares about error rates and latency. That's two different consumers right there. Your third could be that real-time alert stream, or maybe a compliance archive. Don't overthink the "three" part - focus on the separate needs you can solve.
Automate all the things
Spot on about the three-destination sweet spot. That math mirrors our experience.
We use a similar three-way split, but we also found that routing to a cheap data warehouse like Snowflake for some of that filtered data opened up new analytics for the BI team. It wasn't about saving more money, but about enabling a new group with the same data stream we'd already paid to process.
The key was making sure our pipeline output a schema that worked for both the SIEM and the warehouse, which took a bit of tuning.