I’m currently evaluating Cribl Stream as a potential intermediary layer between our various data sources and our primary observability vendor, which happens to be Data Dog. The vendor’s value proposition of controlling egress volume and optimizing data before it hits the ingest pipeline is conceptually sound. However, I am inherently skeptical of claims that don't include detailed, quantifiable financial outcomes. Adding another tool inevitably introduces its own licensing and operational overhead, so the net cost-benefit analysis must be rigorously positive to justify procurement.
I am seeking concrete, granular before/after analyses from community members who have implemented Cribl specifically in a Data Dog context. Vendor case studies tend to gloss over the nuances I need to scrutinize.
My specific areas of inquiry include:
* **Ingest Cost Reduction:** What was the actual percentage reduction in your Data Dog ingest costs post-Cribl implementation? Please differentiate between reductions achieved via:
* Filtering out low-value events/metrics (e.g., DEBUG logs, verbose health checks).
* Trimming high-cardinality fields or PII *before* ingest.
* Adjusting sampling rates dynamically.
* **Cribl's Own Cost Structure:** How does your Cribl licensing cost (considering Worker, Leader, and potential Cloud commitments) compare to the achieved Data Dog savings? Was the ROI calculation straightforward, or were there hidden costs?
* **Performance & Fidelity Impact:** Did any downstream analytics, alerting, or dashboarding in Data Dog suffer due to the data manipulation? For instance, if you sampled or aggregated data, did it affect the reliability of anomaly detection or root cause analysis?
* **Architectural & Operational Overhead:** What was the true operational cost of standing up and maintaining the Cribl fleet? Did you require additional compute/storage resources, and how did you model the total cost of ownership?
A breakdown of the types of data you process (application logs, infrastructure metrics, APM traces, security events) would provide essential context. The devil is in the details, particularly around how Cribl's processing rules are constructed and their long-term maintenance burden versus the static cost of simply ingesting everything into Data Dog.
I am less interested in high-level testimonials and more interested in the structural economics of the decision. Any insights into contractual negotiations with either party post-Cribl introduction would also be valuable, as vendors often adjust pricing models when they see a change in data flow patterns.
Your skepticism is correct. Our ingest cost reduction was 38% month one. But you're missing the bigger cost: operational overhead.
The licensing and maintenance for Cribl's own infrastructure nearly ate our entire Data Dog savings for the first six months. The real win was downstream, not on the ingest bill. It forced us to define what "low-value data" actually was, which had been a compliance blind spot.
Without that internal governance work first, you'll just be moving bytes from one expensive pipeline to another. The tool is secondary.
Trust, but audit.
Exactly. The phrase "operational overhead" is vague, but it translates directly to engineering hours. In our case, it meant a dedicated half-time SRE just for Cribl pipeline logic and keeping its internal Kafka cluster healthy. That's a salary, not a SaaS fee.
You're dead right about governance being the prerequisite. We attempted the same and failed the first quarter because we tried to filter and sample data without a *written* retention and classification policy. Legal and security teams blocked every change because we couldn't prove the data we were dropping wasn't required.
The tool only works if you've already done the politically difficult work of defining what you don't need. Otherwise, you're just building a very expensive, complicated pipe.
Yep, that "half-time SRE" cost is the hidden tax on a lot of these middleware platforms. The governance point is crucial, because even *after* you get the policies in place, that pipeline logic becomes a permanent maintenance sink. Every time a log format changes or a new data source spins up, you're back in there tweaking routes and filters. It's never a one-time setup.
ship it
You're absolutely right about the ongoing maintenance cost, it's a permanent line item in the TCO. I've seen teams treat it as a project with an end date and get surprised when the operational reality hits.
That said, I think the key is to reframe it. That "permanent maintenance sink" becomes the single, governed control plane for all your observability data routing. Without it, those log format changes and new sources would still create work, just scattered across different teams and scripts. The question is whether centralizing that complexity into one maintained system is better than the fragmented alternative.
—daniel
Reframing ongoing cost as a "control plane" is just vendor-speak for accepting the permanent tax. The fragmented scripts you mention are usually owned by the teams generating the data. Centralizing it in Cribl just shifts that operational load onto a platform team, creating a bottleneck and a single point of failure.
It's only a better alternative if you have the headcount to own the control plane as a dedicated service. Most don't.
Trust, but audit.
You're asking for the right data, but I haven't seen a clean before/after that isolates those specific reduction techniques. Most analyses lump all filtering together.
In our POC, we achieved a 42% ingest reduction, but the breakdown was telling:
* Filtering low-value events (DEBUG, verbose traces) gave us about 22%.
* Trimming high-cardinality fields pre-ingest was another 15%, but this required reconfiguring dashboards that relied on those dimensions.
* Sampling for high-volume, low-signal data streams accounted for the remaining 5%.
The critical nuance is that the PII stripping didn't reduce our Data Dog costs at all. It was a compliance requirement we were already handling via Data Dog's processors, so moving it upstream to Cribl just shifted the compute cost. Your baseline matters immensely - if you're already paying for Data Dog's processing, you're just comparing those compute costs to Cribl's.
Data is the source of truth.