Hot take: You're overpaying for Cribl if you aren't using at least three destinations.

Completely agree with the 2 TB/day breakdown - that's the exact kind of math that makes the model click. The three-destination target isn't arbitrary, it's where you start seeing the operational logic fall into place.

Your third destination for low-cost monitoring is smart. We do something similar, but we found that making that third stream a *reduced schema* version of the data for a specific team (like app performance) forces you to think about data contracts and what each consumer actually needs. That refinement often lets you dial down the volume even more on your primary Splunk stream.

The plateau discussion later in the thread is real though. Once you've got your primary, your archive, and one real-time consumer, adding a fourth is usually about politics - making another team happy with 'free' data - not cost efficiency.

Your breakdown with the 2 TB/day split is exactly the mental model I needed a year ago. That's how we finally got buy-in to expand beyond just Splunk.

One nuance from our setup - you mentioned a low-cost monitoring service. We found that using a purpose-built stream for app performance (think Datadog APM traces, not raw logs) really hammered home the value of that third pipe. It turned filtered log data into proactive RUM metrics, which our product team now swears by.

So I'd say the magic of three isn't just about cost centers, it's about enabling entirely different workflows from the same committed pipe.

Exactly, framing it like a committed cloud pipe is the perfect analogy. That three-way split you outlined is almost identical to our setup, where the real unlocking happened with that third stream.

We found the same efficiency jump going from one to two, but the third destination forced us to really think about *data productization*. It wasn't just another route, it was about packaging a subset of that data for a specific team - in our case, marketing attribution events to a CDP. That created an internal chargeback model that actually funded part of the pipeline.

Do you track any internal usage metrics or chargeback for those different streams? I'm curious if that changes the value perception beyond just cost optimization.

Your point about the 2 TB breakdown is exactly the mental model that makes the business case. I'd add one operational caveat to that three-destination sweet spot, though.

Once you start managing those three distinct pipelines, the overhead shifts. It's not just about cost optimization, it's about pipeline governance. You now have three different teams relying on the data quality and uptime of their specific stream. A schema change or a parsing error in the main pipeline can break all three downstream systems simultaneously.

The real work becomes establishing clear data contracts for each destination and having the monitoring in place to know when a stream meant for the low-cost monitoring service suddenly starts getting the wrong event format. The cost justification is clear, but you're trading a simpler admin burden for a more complex orchestration one. Has that been your experience?

That's a crucial addition to the thread. You're absolutely right that the governance overhead becomes the new challenge. I'd call it the shift from a technical project to a service management one.

The "data contract" concept is key. We had a similar issue where a downstream consumer changed a field name in their schema, and because we weren't monitoring for contract drift, it broke their reports. The pipeline was fine, but the delivered data wasn't what they expected. We now treat each destination like a mini SLA.

It's a trade-off, but the alternative is having those teams build their own, duplicative pipelines, which creates even more governance chaos.

I like the reserved instance analogy, it frames the commitment correctly. Your 2 TB breakdown is a solid baseline for modeling.

The "breakpoint around three" you identified resonates. In our case, the third destination's value wasn't just another cheap sink, but enabling a *different pricing model* for the primary. By using Cribl to reshape logs into metrics for a time-series database, we converted a volume-based Splunk cost into a fixed-cost series for our primary monitoring. That often gets more savings than just adding a third cost center.

The governance overhead others mentioned scales with this, though. Each new destination isn't free operational labor.

The reserved instance analogy is good, but you're paying for compute capacity, not just a pipe. The real cost isn't just the Cribl ingest license, it's the worker fleet you have to provision to handle that 2 TB/day peak.

If you're only routing to one destination, you're probably also over-provisioning those workers because you aren't using their processing capacity for multiple output workloads. It's like buying a server that's idle 70% of the time. The math gets tough because you're paying for a transformation engine and using it as a simple fanout.

Your three-destination model forces you to actually use the CPU you're paying for, by having it apply different filters, schemas, and compressions for each downstream consumer concurrently. That's where the license cost gets justified, not just in splitting the data volume.

Great analogy with the reserved instance. That "three destinations for efficient utilization" idea makes a lot of sense, and I'm trying to apply that logic to our much smaller setup. We only process about 200 GB/day, but the concept still feels relevant.

A question, though. How do you handle the initial pipeline design? I'm worried that if we start with just one destination, the cost is hard to justify. But building a pipeline for three hypothetical destinations from day one seems like overkill. Is there a middle ground, like starting with one and having a clear plan for where streams two and three will go, or did you build all three at once?

Your 2 TB breakdown really nails the cost justification model. It's exactly what made the case for us to expand our pipeline.

I've found that third destination often emerges naturally from internal demand. You might start with just your primary SIEM route, but then another team hears you're filtering and asks if they can get a specific event feed. That becomes your second stream. The third usually comes when you realize you can archive the raw data cheaply or create a metrics stream for a different system.

The key is treating that first pipeline build as modular, so adding a new destination group is just configuring a new route and filter, not a full rebuild. That keeps the initial effort lower while leaving the door open for those two other workloads when the requests come in.

That's a bold claim on the "per-GB cost structure." I need to see the actual bill breakdown before I believe the savings. Everyone's environment is different.

You're using 2TB/day as your example, but that's already a massive commitment. The math falls apart at smaller scales. For teams doing 200GB/day, forcing three destinations often means inventing workstreams just to justify the tool, which is its own kind of waste.

And calling it "like a reserved instance" is close, but not quite right. You commit to the pipe, yes, but you also commit to the worker nodes. The real inefficiency is paying for a transformation engine and using it as a simple router. The CPU sits idle.

The reserved instance analogy is useful, but I think you're focusing on the wrong layer. The real waste isn't just the ingestion fee, it's the idle CPU cycles on the Cribl worker group you're paying for.

You commit to a fleet size to handle your 2 TB/day peak. If that fleet is only performing a single, relatively simple transformation and routing to one destination, you're leaving a massive amount of processing capacity on the table. That's the core inefficiency.

Your three-destination model works because it forces you to utilize that compute for concurrent, disparate workloads, each with its own parsing, filtering, and schema reshaping. The license cost gets amortized across the value of those multiple output data products. At one destination, you're not running a data pipeline, you're running a very expensive fanout cable.

Exactly. The idle CPU is the silent killer in the budget. You're provisioning for peak load on a single route, and then that expensive fleet just sits there, lightly filtering events.

But I've seen teams react to this by inventing "value-add" streams that nobody actually consumes, just to spike CPU usage. Suddenly you're enriching logs for a "future analytics project" that never materializes, burning engineer time to create a workload that justifies the cost. It's like digging a hole just to fill it back in.

The trick is finding that second or third stream that *already exists* as a separate, costly pipeline. If you're already routing Apache logs to Splunk *and* archiving raw logs to S3 with a Lambda function, you've got your workloads. Consolidating them onto one engine is the win. Creating them to justify the engine is a loss.