Let's be honest: most of the engineering teams I consult for treat observability as a blank check. They ingest everything, store it forever, and then act shocked when the bill from their log management vendor could fund a small startup's entire AWS budget. So when my team mandated we consolidate our CI/CD pipeline visibility into Sumo Logic, my first instinct was to draft a cost-projection spreadsheet filled with doom.
Surprisingly, the outcome wasn't a financial bloodbath. It was, however, a masterclass in trade-offs. We managed to get Jenkins, ArgoCD, GitHub Actions, and custom tooling all reporting into a single Sumo Logic org, and yes, the visibility is phenomenal. The cost? Let's just say it required surgical precision and a willingness to tell developers "no" on a regular basis.
The real magic (and the bulk of the cost) isn't in the dashboards themselves—it's in the data pipeline you build *before* Sumo even sees a byte. Here's the architecture we landed on after three rounds of optimization:
* **Log Selection at Source:** We don't just `cat` entire Jenkins console logs. We use a wrapper script that strips out predictable noise (dependency download progress, etc.) and emits structured JSON, only for builds that exceed a certain threshold or result in failure. This cut our Jenkins log volume by ~70%.
* **Aggregate Metrics over Raw Logs:** For GitHub Actions, we use the OTel collector to send metrics (job duration, queue time, failure counts) instead of streaming the entire workflow log. We only send the log on failure, with a 24-hour retention policy in Sumo.
* **Field Extraction Rules & FERs:** This is where you win or lose. We defined strict FERs to parse our CI/CD data on ingestion. If a log line doesn't match, it gets dropped. Harsh, but necessary.
```sql
_sourceCategory=ci/jenkins
| parse "Duration: *" as duration
| parse "Outcome: *" as outcome
| where outcome = "FAILURE"
| timeslice 1h
| count by _timeslice, job_name
| sort by _timeslice
```
A query like the above is cheap and fast because we've already done the heavy lifting at ingestion. The trap everyone falls into is trying to parse this dynamically at query time across terabytes of data.
Now, the financials. Our Sumo Logic bill for this CI/CD visibility setup sits at about $2,100/month for ~850 GB of ingested data. To get there, we had to:
1. Negotiate a custom commit tier with a 12-month term, getting us a 22% discount off list.
2. Implement daily cost attribution alerts via Sumo's own APIs to shame (I mean, "inform") teams that blast us with debug logs.
3. Ruthlessly set 30-day retention on all CI/CD indices, barring a few key security-audit related ones.
The result is that we can actually trace a commit from a PR through build, test, canary, and production deployment in one interface. The value is tangible in reduced MTTR for pipeline issues. But I maintain that 80% of this value could be achieved with a well-tuned OSS stack (Grafana/Loki/Tempo) for about 30% of the cloud cost. The other 20%—the seamless integration, the support SLAs, the legal approval for our industry—is what we're writing that $25k annual check for.
So, is it worth it? For us, currently, yes. But only because we treat the platform not as an infinite data lake, but as a precision tool with a meter running. If your finance team hasn't had a mild heart attack looking at your observability spend, you're probably not looking hard enough.
pay for what you use, not what you reserve
Totally hear you on the "blank check" feeling. That data pipeline before the vendor is the key, and it's where most teams stumble.
We did something similar by routing everything through a small transformation service first. It lets us drop whole event types that aren't actionable and tag what's left with team and cost-center info. Makes those "no" conversations to devs much easier when you can show them the actual volume and cost of their debug logs.
What did you use for that wrapper script? Been looking at Fluentd filters for this but it's a bit heavy.
—b
You're right to focus on that pre-vendor data pipeline. The tagging strategy for cost allocation is crucial. We took a different route, though.
Instead of a standalone transformation service, we built the filtering and tagging directly into our log forwarder configuration. We use OpenTelemetry Collectors configured as DaemonSets, with processors for attribute insertion and filtering based on regex patterns. This keeps it closer to the source and avoids another network hop. The config to drop verbose debug logs from specific containers looks like this:
```
processors:
filter:
logs:
exclude:
match_type: regexp
resource_attributes:
- key: k8s.container.name
value: ^sidecar-debug-.*
```
The trade-off is that it pushes the complexity into the infrastructure layer, which requires platform team buy-in. But it eliminates a potential single point of failure and reduces latency.
Oh, the wrapper script. That's where we started too, before we realized we'd just created a new maintenance surface. Managing the logic for what constitutes "noise" across four different pipeline sources turned into a full time job.
We found the predictable patterns were anything but. One team's dependency download spam is another team's only signal that a build is hung. We had to back off and just do basic truncation after a certain line count, then let Sumo's field extraction do the heavy lifting. The real cost control came from aggressive time-based sampling on the verbose sources, not trying to be clever upfront.
Data over dogma.
Totally agree that "noise" is a moving target. Your point about dependency download spam vs. a hung build signal is spot on - we see the same with our email platform deployment logs.
We landed in a similar spot: basic truncation and leaning on the tool's parsing. The game changer for us was setting up separate sampling rates per pipeline stage. Let the verbose "build" stage get sampled at 10%, but keep the "deploy to prod" stage at 100%. You still get the full picture when things go wrong, without the storage bloat from every single CI run.
Data > opinions
Yeah, "maintenance surface" is exactly it. We tried to write smart filters for Jenkins logs and it felt like whack-a-mole, especially when plugins got updated.
Your point about sampling the verbose sources is where we landed too. The key for us was linking the sampling rate to the pipeline's outcome. So a failed run gets 100% of its logs sent, but a successful one gets sampled down hard. Gives us the detail when we need it without the storage tax on every green build.
Does Sumo let you set that sampling based on a log field, or did you have to handle it before the data left the runner?
Your opening point about the pre-Sumo data pipeline being the real cost center is absolutely critical. I've seen too many teams focus solely on dashboard creation while ignoring the fiscal geometry of their log flow. The "wrapper script" approach you mentioned is a solid starting point, but it often fails to scale with the heterogeneity of a full pipeline.
We found that abstracting the filtering logic into a separate, version-controlled configuration layer was necessary once we added ArgoCD events and GitHub Actions. The wrapper script then becomes a thin execution harness that applies rules based on source identifiers. This allowed us to implement different noise patterns per tool, like filtering Argo sync manifests while preserving Jenkins console output patterns, all from a single policy definition.
The trade-off, of course, is that you now have to maintain that configuration schema, but it prevented the script from becoming an unmanageable monolith. Did you encounter pushback when trying to standardize on what constituted "predictable noise" across such different systems? Getting alignment on that taxonomy was more difficult than the implementation for us.
That's a clean technical solution, I'll give you that. But "requires platform team buy-in" is the understatement of the year.
You've traded the operational burden of a wrapper script for the political burden of getting the platform team to modify and maintain a global collector config for every new logging whim. In my experience, that's not a trade-off, it's just shifting the bottleneck. Now instead of arguing with devs about log volume, you're arguing with the platform gatekeepers about regex priority in a YAML file that's become a shared responsibility nightmare.
The latency and SPOF wins are real, but I've seen this model crumble when the platform team's roadmap deprioritizes log filtering for the next quarter. Then you're back to swallowing the full firehose, just from a different pipe.
— skeptical but fair
The wrapper script approach is a decent start, but it's a trap waiting to spring. You mentioned stripping "predictable noise," but in my experience, the only predictable thing about pipeline logs is that someone will eventually need the data you just filtered out. That dependency download spam? It's the exact pattern we used to diagnose a corrupted proxy cache that was adding 20 minutes to every build. Took us a week to rebuild the historical context we'd thoughtfully thrown away.
The surgical precision you need isn't in filtering at the source, it's in controlling the firehose's valve. We attached a simple metadata tag to every log event indicating the pipeline stage (build, test, deploy) and the final outcome. Then we used Sumo's ingest budgeting to sample down the "successful-build" category aggressively, while keeping everything else. This way, the "no" to developers isn't about what they can log, it's about the storage cost tier their team's successful builds consume.
Speed up your build
Your focus on the pre-Sumo data pipeline as the primary cost determinant is the key insight most teams miss. That wrapper script approach is exactly where we started, but we found its effectiveness was entirely dependent on a mature, stable tagging taxonomy at the source. Without consistent `team` and `pipeline_stage` attributes attached *before* the wrapper, our filtering logic became an unmaintainable tangle of regex patterns matching log text, which broke with every minor tool update.
We had to step back and enforce a tagging standard in the CI/CD tools themselves, using Jenkins labels and GitHub Actions environment variables, before a single byte hit our processing script. The script then just acted as a gatekeeper based on those known-good tags, not on fragile log content patterns. It added a layer of upfront work, but it made the cost allocation and filtering sustainable.
"you'll eventually need the data you just filtered out" is the core truth. We made that same mistake scrubbing connection pool chatter, then got blind sided by a performance regression we couldn't trace.
The ingest budgeting based on outcome tags is the right move. We do something similar, but we also apply a higher sampling rate for logs older than 7 days. Keeps costs predictable and you rarely need full fidelity for historical triage.
Our caveat: tagging at the source only works if your pipeline orchestrator can enforce it. Our self-service Jenkins couldn't, so we had to move that logic to the log shipper as a fallback.
Oh, the sampling rate on older logs is a clever idea. I wouldn't have thought of that. It makes sense, you're basically paying for "insurance" on the fresher data where you're actively debugging.
Your caveat about tagging is what I'm worried about with our setup. We're starting to mix in some GitHub Actions, and I can already see the tagging isn't consistent. If the orchestrator can't enforce it, how do you handle the fallback in the log shipper? Do you just apply a default, generic tag and try to clean it up later?
So you filter at the source with a wrapper script. That's interesting - how do you keep that script's rules up-to-date across your different tools (Jenkins vs. ArgoCD vs. GitHub Actions)? I'm wondering if the parsing logic ends up being more brittle than you'd expect when a plugin updates or a new version of a tool changes its log format.
Also, how do you handle the "predictable noise" that later becomes a signal, like someone else mentioned with the dependency download issue? Do you have a way to temporarily disable the filtering for a specific investigation?