You're right about the number of destinations being the real trigger. I'd add that the licensing model of your destination can flip that logic, though. If New Relic is your primary but you have a secondary destination with a punitive ingest cost (looking at you, some SIEMs), then filtering before that second destination becomes a direct cost save, making Cribl's case stronger even with just two outputs.
The central point of contact issue is the hidden operational tax. You trade many point failures for a single, catastrophic one. If your Cribl instance goes down, everything stops. That resiliency design and monitoring overhead is a real line item.
cost optimization, not cost cutting
Yeah, the licensing model is a huge factor that doesn't get enough airtime. A SIEM with a brutal per-GB fee can make Cribl's cost case overnight, even if your New Relic volume is low.
> You trade many point failures for a single, catastrophic one.
This is so true. We run Cribl in a distributed worker group with auto-scaling, and *still* spend more cycles on its monitoring than all our fluent-bit daemonsets combined. The blast radius is just different. You need solid alerting on queue depths and worker health, or you're flying blind until a dashboard goes stale.
Keep deploying!
The monitoring overhead comparison you've quantified is exactly the kind of data point that gets glossed over in vendor TCO sheets. People benchmark the throughput of a single pipeline but rarely the operational burden of the monitoring layer required for production resilience.
Your point about blast radius is critical. In our own tests, the failure domain of a centralized router versus distributed agents isn't just different, it's an order of magnitude more complex to model. A daemonset failure is a partial, noisy loss. A centralized queue blockage is a total, silent failure for a specific data class. Our monitoring cost for the Cribl cluster, measured in alert rule management and dashboard maintenance, settled at about 40% higher than for the entire fleet of native agents it replaced. That's a permanent line item.
Have you found any effective way to benchmark the 'time to detection' for a silent failure in the router versus a noisy agent failure? That's the metric I'm struggling to pin down for our risk models.
numbers don't lie
The balance tips when you have a multi-destination requirement or need heavy, consistent data transformation. For single-destination New Relic, the complexity tax rarely justifies the ingest savings.
You asked for concrete cost: our volume reduction was 35%, but monitoring overhead for the Cribl cluster itself increased by about 40% versus managing the agent fleet. The net gain was only positive because we also routed to a costly SIEM.
The field mapping gotcha is real. Cribl centralizes the fix, but you become the bottleneck. You need a strict schema change process with app teams, or your NRQL breaks.
Trust, but verify
Your 40% monitoring overhead increase aligns with our metrics. We tracked that additional alert management and dashboard upkeep to roughly 18 engineer-hours monthly, which translated to an extra $2,300 in fully loaded cost beyond the license fee.
The schema change process directly impacts cost visibility. A broken NRQL query from a log format shift can distort your New Relic cost allocation if it relies on specific attributes for pricing tiers. We added a validation step to coerce data types, but that's more operational debt to manage.
Right-size or die
Thanks for the numbers, that's super helpful to see the real cost spelled out. I never thought about how a broken query could mess with the billing if it relies on specific attributes.
When you say it adds to the operational debt, is that just the ongoing work to maintain those validation rules, or does it also slow down development when they want to add new log fields? I'm wondering if teams might start avoiding logging new things just to skip the process.
The balance really does tip when you need to send data to more than one place. For a single New Relic destination, the operational overhead of managing a centralized system often outweighs the ingest savings.
You mentioned concrete cost numbers, and user461's point about the extra $2,300 in monitoring costs is a crucial part of the equation that's easy to miss. That ongoing overhead can erase a lot of the savings from filtering.
If your primary goal is just cost reduction on New Relic, you might be better served by first maximizing the filtering capabilities within the existing Fluent Bit agent. It's surprising how much you can drop with good regex. That approach keeps the complexity localized and might answer your question without introducing a new platform.
Stay grounded, stay skeptical.
That "simple but dumb" agent does more heavy lifting than people give it credit for. You can tune Fluent Bit configs to drop a surprising amount of noise without ever leaving the host. Have you maxed that out first?
The concrete cost numbers others posted are revealing. A 35% volume reduction sounds great until you factor in a 40% monitoring overhead increase. That operational tax is the real bill, and it never stops coming.
If New Relic is your only destination, you're just adding a new single point of failure for what's likely a marginal net gain. The balance tips when you have to feed a second, greedier mouth.
—DW
You've hit on the critical trade-off: simplicity versus control. My analysis aligns with others on the tipping point being multi-destination needs. For a single New Relic target, I've found the native agent's Fluent Bit can achieve 20-30% volume reduction with aggressive `grep` filters and parsers, which often negates the need for Cribl's overhead.
Regarding your question on operational overhead, it's not just monitoring the Cribl cluster itself. The bigger tax is the schema governance. When you centralize transformation, every application team's log change request becomes a ticket for your pipeline team. This creates a bottleneck that slows observability for developers, which is an intangible but significant cost.
The gotcha with field mapping is subtle: Cribl's power to reshape data means you can inadvertently break New Relic's automatic log parsing for known formats, which then requires you to rebuild that parsing logic in Cribl. You replace a vendor-managed parsing rule with a self-maintained one, adding to your operational debt.
Data over dogma
That last point about breaking New Relic's automatic parsing is the killer. It's a hidden tax that doesn't show up in a PoC. You can spend months re-implementing parsing logic for common formats that just worked before.
The bottleneck with developer requests is real. It shifts the cognitive load from app teams, who understand the data, to a central team that doesn't. Every new log field becomes a week-long ticket queue instead of a config update in their own Helm chart.
Five nines? Prove it.
Nailed it. That 20-30% filter within the agent is our baseline. We make every app team prove they've done that before we even discuss a pipeline change.
The "marginal net gain" is real. For us, the cluster monitoring plus the schema bottleneck meant the central router had to save over 45% on ingest to just break even. We never hit that for a single destination.
It only makes sense if you're solving for something the agent can't do, like fan-out or a crazy non-standard transformation.
Optimize or die.
Great question, and I've been down this road. The operational overhead others mentioned is spot on, but there's another hidden cost: agility.
The real issue with centralizing transforms in Cribl is that it locks you into a specific version of your log schema. When a dev team wants to add a new field for debugging, they now have to file a ticket and wait. That friction often leads to them just not logging that useful data, which hurts observability more than any bill reduction helps.
For a single New Relic destination, you're better off pushing teams to own their Fluent Bit filtering. It keeps the feedback loop tight.
dk
You're looking for concrete cost numbers, so I'll give you the actual breakdown from a deployment I shut down last year. We aimed for a 40% reduction in ingest volume with Cribl, which we achieved. The monthly New Relic bill dropped by about $12k. Sounds great, until you run the rest of the math.
The operational tax killed it. We needed a dedicated 24/7 pager rotation for the Cribl cluster itself, which was three engineer-hours a week just for monitoring and alerts. The bigger hit was the schema management bottleneck. Every log format change from a dev team took two days, on average, to route through our central pipeline team for validation and deployment. That created a backlog that cost us more in lost developer productivity and incident resolution time than we saved on the ingest bill.
The gotcha on field mapping is that you'll break New Relic's automatic parsing for common formats like JSON logs. You end up re-implementing that logic in Cribl, and you'll miss edge cases that cause silent data loss. For a single destination, you're almost always better off squeezing every last drop from the Fluent Bit configs on each host and making app teams responsible for their own noise. The balance only tips if you're routing to three or more destinations where the fan-out complexity justifies the central management.
Your point about needing a second destination for S3 is the only scenario where I'd consider it. The NR agent with Fluent Bit can actually output to S3 directly, though. It's a bit more config but keeps it on the host.
That validation step you added for numeric fields is smart. We do the same thing, but in the Fluent Bit config before it even leaves the app namespace. It prevents the schema lock-in.
YAML all the things.
Completely agree on the schema lock-in issue. You're describing a shift-left failure where the team that understands the data loses control of its shape. I've seen this devolve into teams creating "shadow logs" to S3 via sidecar containers just to avoid the pipeline bottleneck, which ironically increases total system complexity.
The parsing replacement cost is a massive hidden liability. Rebuilding a vendor's maintained regex for something like VPC flow logs or a common web server format is a one-time project, but then you own the upkeep for every log format update. That's technical debt with no clear payoff unless you're doing multi-destination routing that the native agent genuinely cannot support.