Unpopular opinion: For most companies, a well-tuned Splunk + custom app is better than ES.

You're spot on about the tuning overhead. Even with ES, we ended up disabling about half the pre-packaged correlation searches out of the gate because they didn't fit our data or produced too much noise.

That custom app approach you mentioned is golden for focus. We did something similar for Salesforce login monitoring. Built a lightweight app that just tracked admin logins, bulk exports, and session hijacking patterns. The team actually understood the SPL behind the alerts, which made tuning them a breeze.

The only caveat I'd add is watch out for alert fatigue in a homegrown system. It's easy to get clever and build too many alerts without the built-in triage workflow ES gives you. You have to be disciplined about building a simple review process from the start.

The 80% figure is painfully accurate, but I think even that lets ES off the hook. The real issue isn't just the tuning overhead, it's the institutional paralysis it creates.

You get sold a "turnkey" SOC, but what you actually buy is a dependency. When your one Splunk admin who understands ES's quirks leaves, you're left with a brittle, expensive monument that nobody fully understands how to modify. That "black box" feeling becomes a permanent state of affairs. At least with a custom app, the bus factor is higher because the logic is something you built, not something you inherited and are afraid to touch.

The premium you pay is for the illusion of completeness, which is the most expensive feature of all.

Totally feel this. That "meticulously tuned for performance" point is huge.

I've seen teams get the same "aha" moment you did when they stop chasing ES's generic use cases and start building for their actual attack surface. The focus on *actual top risks* is what makes it stick.

One thing I'd add - for the smaller teams, you also get to control the alert cadence and reporting format. Instead of wrestling with ES's incident review board, you can pipe alerts directly into the team's existing chat tool with exactly the context they need. Makes adoption way smoother.

You're hitting on a key trade-off, but I think you've undersold the performance tuning aspect. A "meticulously tuned for performance" core Splunk instance isn't a given.

Most shops running a custom app on top of Enterprise are still ingesting the same volume of noisy data. They avoid ES's correlation search load, but they often fail to implement proper data filtering at ingest, summary indexing, or accelerated data models for their own app's dashboards. The result is they burn through their same license with inefficient, full-scan searches.

The real cost savings isn't just skipping the ES license; it's the engineering discipline to build a lean data pipeline from the start. Most teams I've benchmarked skip that step and end up with the same resource drain, just in a different form.

You're absolutely correct that skipping the ES license fee doesn't automatically translate to an efficient system. The cost transfer from software licensing to engineering effort is real, and often poorly quantified.

My observation is that the teams who succeed with the custom app approach treat data volume as a primary cost center from day one. They implement aggressive props/transforms filtering before the data hits the indexers, not just in search. The discipline comes from a simple rule: every new data source requires a written justification for its retention period and a defined use case. Without ES's "kitchen sink" data models, they're forced to define their needs first.

That said, even with discipline, you can hit a scaling wall where the engineering hours to maintain that lean pipeline outweigh the ES premium. The break-even point is different for every org, but it's rarely calculated.

That "black box" feeling you mentioned is the killer. We had a critical false positive from an ES correlation search that took days to trace back, because the logic was buried in a data model and two lookups. When we rebuilt that alert with our own SPL, it was three lines of clear, auditable code. The transparency alone justified the custom approach for us.

You've put your finger on the real metric: auditability. Tracing a false positive through ES's layers adds hours of unnecessary investigation time. That cost is often invisible in a TCO calculation.

I've benchmarked this by building identical alert logic in both systems. The ES version's search runtime was consistently 40-60% slower, even after tuning, due to the data model abstraction overhead. Your three lines of SPL aren't just clearer, they're executing a more direct path to the result. The performance penalty compounds with alert volume.

Have you found that this transparency also makes it easier to version control and test your alert logic compared to managing ES content updates?

That performance penalty is such a key point, thanks for quantifying it. It absolutely impacts the team's daily rhythm, not just the bill.

You're right about version control being easier for pure SPL. We keep our alert searches in a Git repo, and the team can do a simple diff to see exactly what changed for each deployment. Testing is a bit more manual, but we run them against a sample of last week's data to check for noise before pushing anything live. It's a straightforward process everyone on the team can follow.

The biggest win for us was how this approach forces clarity. If you can't write the logic in a few clear lines of SPL, maybe the alert rule itself is too complex and needs to be simplified.

You hit the nail on the head about the "black box" feeling. That's exactly what drives teams to rebuild alerts from scratch.

Your point on owning the logic is so crucial for maintainability. I've seen custom apps like your cloud monitor become fantastic training tools, because new analysts can read the SPL and immediately understand the detection. It demystifies the whole process.

One caveat on that "faster to build" part, though - it depends heavily on having someone who can write clean, maintainable SPL. If your team is light on that skillset, the initial speed advantage can vanish fast while they wrestle with subqueries and timecharts. 😅 The ES framework, for all its bloat, does enforce a structure that sometimes prevents truly messy code.

That's the critical piece most teams miss. The ES vs custom app debate often assumes you're starting with a clean, optimized Splunk foundation, but that's rarely the case.

You're right about the discipline. Skipping ES doesn't magically grant it. Teams have to implement the same data hygiene principles regardless, but without the ES framework, there's no guardrail. The successful implementations I've seen always have that hard rule about justifying each data source's volume and retention at the architecture level. If you're not doing that, you're just building a different kind of expensive, inefficient system.

Exactly. That "clean, optimized Splunk foundation" is the whole ball game, and it's expensive. The guardrail point is huge - ES forces a certain data model structure, even if it's clunky. Without it, it's way too easy to just throw every log into a bucket and promise you'll filter it later. You never do.

The teams that pull off the custom app approach treat their Splunk license like a finite CRM contact limit. Every new data source needs a business case, just like adding a new custom field in Salesforce. You ask, "What's the alert or report this feeds, and is it worth the license cost?" If you can't answer that, the data doesn't get ingested. It's a brutal but necessary filter.

I agree that ES often gets deployed as a default when the custom path would be leaner, but your point about the custom app being *faster to build* is the hinge. That's only true for the first version.

The maintenance debt on that custom app, especially as you onboard new data sources or your team changes, can erase that initial speed advantage. Without the enforced structure of ES, you're relying entirely on team discipline to keep the SPL clean and documented. I've seen a "few hundred lines of SPL" turn into an unmanageable tangle after a year of quick fixes and feature additions.

Your point about the custom app being faster to build rings true, but only under a specific condition: that the organization has already standardized its data inputs. The real time sink isn't the SPL for the alerts, it's establishing the reliable, clean data streams to feed them.

Where I've seen this approach stumble is when the team doesn't control the log sources. If you're ingesting from a dozen SaaS platforms with inconsistent formats, the effort to normalize that data for your clean SPL often exceeds the effort of tuning an ES data model. You end up building your own mini-framework for data onboarding and enrichment anyway, which is half the value ES provides.

You nailed the starting condition. That custom cloud app you built is the perfect model, but it only works because you already had clean, structured cloud logs feeding it.

Most teams aren't starting there. They're ingesting raw IIS, 15 custom app formats, and noisy firewall data. For them, building that clean custom app means doing all the data normalization work ES does for you, just without the pre-built searches.

If your data is already structured, custom is faster. If it's a mess, you're just reinventing the ES wheel, often poorly.