Alright, let's see if we can get past the usual "we migrated and everything is sunshine" vendor fluff. We moved our SOC off Splunk Enterprise Security to Microsoft Sentinel about six months ago. The driver was, predictably, the eye-watering Splunk bill. But I'm inherently suspicious of any migration story that doesn't detail the trade-offs.
On pure ingestion cost, Sentinel wins in a landslide. Our Splunk cloud commit was brutal, and the overages even worse. Sentinel's data ingestion model, tied into our existing Microsoft licensing, cut that line item by about 65%. That's the headline they want you to see. But cost is more than ingestion.
The performance hit is in the operational tempo. KQL is fine, but it's not SPL. Our complex correlation searches, the ones that took 30 seconds in Splunk ES, now often run for 2-3 minutes in Sentinel. We've had to re-architect several key detection rules to be less granular on the first pass and then drill down. The out-of-the-box analytics rules are... optimistic. Tuning them to reduce false positives without missing true positives has become a significant time sink for the team. The "cost" here is analyst hours, which isn't on the Azure invoice.
So the math is this: we traded direct licensing expense for increased operational overhead and a slower mean time to detection on some complex threats. Whether that's a net win depends entirely on how you value your analysts' time versus your cloud budget. For us, the financial pressure made the choice obvious, but calling it a pure "upgrade" would be disingenuous. The devil, as always, is in the reproducible details of daily use.
Data skeptic, not a data cynic.
We're a 15-person B2B SaaS, running everything on Azure. We went from Splunk Cloud for app analytics to Sentinel for security log retention about a year ago, now handle around 25GB/day.
Real cost: Sentinel was $3.50/GB ingested after the first 100GB free in our Azure commit. Splunk was over $10/GB on our old plan. But watch the analytics rules cost - each query runs on Log Analytics, and a poorly tuned scheduled alert can burn through resource credits fast.
Query speed: Our standard dashboard queries, simple time charts, are fine. But multi-table joins on historical data? Those took 15 seconds in Splunk now often hit the 30-second timeout in Sentinel. We had to pre-aggregate.
False positive tuning: The Sentinel out-of-the-box rules were noisier for our environment. We spent maybe 40 hours over two months tuning them down. Splunk's ES content felt more mature for our old on-prem stack.
Deployment lift: Connecting to Azure-native services is trivial. But for our non-Microsoft sources (a few AWS accounts, some SaaS apps), getting consistent parsing into Sentinel took more effort than expected.
Given the cost pressure you're under, I'd stick with Sentinel. The savings are real, but you trade cash for analyst time. If your team can handle the KQL learning curve and tuning work, it's the rational choice for a cloud-heavy shop on a tight budget. If you can't, go back to Splunk. Tell us your team size and if you're mostly Azure or hybrid.