Switched from Splunk ES to Sentinel - 6 month cost and accuracy report

Totally agree about the engineering time being the hidden cost that never shows up in the licensing calculator. It's like getting a great deal on a car but forgetting you need to pay for the custom upholstery separately.

Your point about the calculator being accurate is encouraging, but I'd add that its reliability seems to depend heavily on having a very stable data estate. If you're adding new apps or systems regularly, the forecasting gets shaky fast because you can't model what you haven't onboarded yet.

That initial integration phase for core sources is deceptively quick. The real time-sink, as you hinted, is bringing over all those custom, niche, or legacy data sources that complete your detection picture. Did you find the Azure Monitor Agent (AMA) handled your on-prem stuff well, or did you end up using a mix of methods?

You're spot on about the engineering time being the hidden cost driver. It's often the single largest line item that gets omitted from the initial ROI calculation.

I'd add that the accuracy of the Azure savings calculator is heavily dependent on your initial data normalization and filtering. In our case, we had to invest a significant upfront engineering effort to build ingestion-time transformation rules in our Event Hub. This was to deduplicate certain verbose security logs and filter out known noise, like routine health pings, before the data even hit the Log Analytics workspace. Without that preprocessing step, our ingest volume would have been 20-30% higher, making the calculator's prediction useless and blowing our commit tier.

That preparatory work isn't reflected in the calculator, but it's essential for cost predictability. Did you perform any similar preprocessing to condition your data before Sentinel ingestion, or did you bring everything over raw and manage volume purely through commitment tiers?

That's a really interesting point about preprocessing to make the calculator accurate. In my experience with marketing platforms, we often have to do similar cleanup on incoming data streams before they hit the analytics layer, or the cost and noise both get out of hand.

For those of you who did this filtering at the Event Hub, how did you validate it didn't accidentally strip out useful data? I'd be worried about creating a blind spot in the security feed during that transformation step.

That hidden engineering cost is a universal truth in these migrations, and your breakdown helps others budget for the reality, not just the license swap.

You mentioned the Azure savings calculator being accurate, and that's great to hear when it works. In my experience, its reliability depends almost entirely on those initial integration choices you made. If you had onboarded a few noisier legacy apps later, you'd have been recalculating from scratch.

How did you manage the expectation-setting with leadership around that ongoing engineering time? It's easy for them to see the new, lower monthly bill and assume the project is 'done,' when really you've just swapped a variable license cost for a fixed internal resource cost.

Your breakdown on the engineering time as the true cost driver is the critical piece others miss. I've seen migrations where that unplanned internal resource burn completely negated the first year's licensing savings.

The Azure savings calculator's accuracy you noted hinges on that initial data integration work being treated as a capital expense. If you account for those engineering hours in the project's total cost, the ROI timeline stretches considerably.

How did you quantify that integration time for leadership? Did you track it against the projected license savings to show the actual break-even point?

Your point about Sentinel being plug-and-play for core Azure sources aligns with my experience, but the reality of that integration speed is heavily contingent on what you define as "onboarded." Deploying the Data Connector for something like Azure Firewall is indeed a five-minute task.

However, true onboarding, where logs are parsed, normalized, and validated for use in analytics rules, is a multi-step process for each source. This is where the advertised speed diverges from operational reality. We found the initial connector deployment gave a false sense of completion, and the subsequent data quality work often took longer than anticipated. Did you implement a formal validation step to confirm log schema alignment and required field population before you considered a source fully integrated?

You're absolutely right about that false sense of completion. We dealt with it by framing the migration as a move from a variable operational expense to a fixed project-based cost for the first year. That upfront framing set the expectation that the "project" had a defined scope and timeline for integration work, separate from the ongoing operational bill.

To your point about the ongoing time, we included a maintenance buffer in our operational model for the first post-migration quarter. This was explicitly for tuning rules and handling new sources as they came, so leadership wasn't surprised when the team was still spending cycles on it. It helped them see that while the license cost was now stable, the system itself needed a period of active care.

That side-by-side run is the only way to validate, but it's a luxury most teams don't get. We were forced to cut over in one shot during a contract termination. Treating every alert as a full rewrite is correct, but the time sink wasn't just documenting the decision tree, it was uncovering all the hidden thresholds and exceptions that were buried in Splunk's search-time field extractions. You can't document what you don't know is there. We had several alerts fire incorrectly for weeks until we found those unstated assumptions.

That's a really sharp breakdown of the cost switch, thanks for sharing the numbers! The part about >The Azure savings calculator was surprisingly accurate for us< is super interesting, because I've heard so many horror stories about it being way off.

It makes me wonder - did you have to do a lot of upfront work on data normalization before you could trust the calculator's prediction? Like, filtering out known noisy logs at the Event Hub level so your test ingest volume was truly representative? I'm guessing your stable 110 GB average didn't happen by accident.

You're right to be suspicious of that trade-off. It's a zero-sum game when you're tuning to a specific volume or alert quota.

In our case, the drop in coverage for non-Microsoft assets wasn't from tuning the rules themselves, but from where we allocated the engineering time. Once you've spent weeks calming down the Entra ID alerts, you're not going to turn around and build sophisticated, high-fidelity rules for your on-prem Linux boxes. They just get the basic, out-of-the-box detections.

So the accuracy didn't move sideways, the investment did. The platform might be capable of both, but your team's bandwidth after the migration probably isn't.

The plug-and-play claim for Azure sources is a bit optimistic, even for core Microsoft services. Deploying a connector gets you the pipe, sure, but you're still left with the work of defining the expected schema and building the dashboards your on-call team actually uses, like you mentioned. That's not five minutes, that's days of work per source before you trust it for anything beyond trivial alerts. The real speed is only for the billing to start.

Also, your point about KQL being more flexible for custom app logs is interesting. I've found the opposite, at least initially. The pivot from SPL's search-time field extraction to KQL's rigid reliance on parsed schemas meant we had to pre-process and map everything upstream in a Logic App, which just moved the complexity rather than eliminating it. It's flexible once you're in the walled garden, but getting your data shaped to fit the gate is the real project.

You're spot on about that being the real project. We had the same experience where >getting your data shaped to fit the gate< meant a ton of upfront work with Azure Functions to restructure our custom JSON logs before they even hit the Event Hub. It felt like we were building an ETL pipeline just to get to the starting line.

I do think KQL's flexibility pays off later, once you're over that hump, because you can build way more complex joins and time-series analysis than we ever could in SPL. But you're right, that initial cost is huge and often glossed over. It's not a migration, it's a full data re-architecture for anything non-standard.

Exactly. That upfront engineering is the real 'commit tier.' You're buying a predictable cost curve with sweat equity. In our case, we went raw and managed the volume purely through the commitment tier, mostly out of desperation. It meant we had to live with a noisy, bloated workspace for two months while we desperately wrote retention policies and throttling rules inside Sentinel itself. The calculator was useless because our baseline volume was garbage.

The irony is we ended up building those same transformation rules anyway, just later, to claw back wasted commit. So you pay the tax either way. Upfront it's a project cost, later it's a surprise operational fire drill. The only difference is which budget line it hits.