Switched from LogRhythm to Microsoft Sentinel - which is better for cloud-first?

I'm Brian H., the senior infrastructure lead at a mid-sized fintech where we've been Azure-native for about three years. We ran LogRhythm on-prem for five years prior to that and have now operated Sentinel in production for the last 18 months, managing around 2TB of ingest daily.

* **Native integration effort:** Sentinel's integration is indeed a game-changer for Azure-native assets. Enabling diagnostic settings on a resource and having logs flow into the Log Analytics workspace within minutes is a genuine efficiency. LogRhythm requires you to stand up a Windows-based agent (or use Syslog forwarding) which adds configuration and maintenance overhead. For a pure Azure environment, Sentinel reduces the integration tax by about 70% in my experience.
* **Total cost of ownership (TCO) for mid-market:** LogRhythm's traditional licensing model (based on EPS or data nodes) often results in more predictable, but higher, baseline costs. Sentinel's pay-as-you-go ingestion model (around $2.30/GB analyzed in Log Analytics) can start lower but requires diligent data filtering. The hidden cost is in the queries and analytics rules; without careful KQL optimization, you can run up significant compute charges. For our team of 8 analysts, Sentinel runs 25-40% cheaper per month than our old LogRhythm commitment.
* **Depth of analysis for heterogeneous logs:** This is where your colleagues' point has merit. LogRhythm's data normalization and its "AI Engine" provided out-of-the-box correlations for network and endpoint logs that felt more mature for on-prem infrastructure. Sentinel's built-in UEBA and ML (like Anomaly detection) are heavily optimized for Azure AD, Office 365, and cloud activity. For non-Microsoft sources, you'll spend more time writing KQL queries to achieve similar insights. It's not a deficit of capability, but a deficit of pre-packaged content.
* **Daily operational ease for a small team:** Sentinel's tight integration with Azure Active Directory for RBAC and its single-pane-of-glass within the Azure portal reduces context switching for cloud admins. Managing alerts and incidents is streamlined if your workflow lives in Azure. LogRhythm's web console, while powerful, is another separate system to manage and permission. For a team living in the Azure portal already, Sentinel's daily friction is lower, though its query language (KQL) has a steeper initial learning curve than LogRhythm's search syntax.

Given your description of a company migrating everything to Azure with a not-huge team, I'd recommend Sentinel. The efficiency gains from native integration and unified management outweigh the benefits of LogRhythm's more generalized analysis depth. To be completely sure, tell us the volume of non-Azure logs (like network firewalls or third-party SaaS) you need to analyze, and whether your team has anyone already proficient in Kusto Query Language.

That automatic integration is a trap. It encourages lazy logging where you just turn on every diagnostic stream "because you can."

You'll drown in noise and your bill will skyrocket. LogRhythm forces you to think about what you actually need to collect. Sentinel's default is everything, which is the opposite of least privilege for monitoring.

On the ML question, Sentinel's built-in features are basic. They're good for known Azure attack patterns but generic. For custom detections across hybrid sources, you're building it yourself. LogRhythm's ML engine is more mature out of the box for heterogeneous environments.

If you're all-in on Azure, you can make Sentinel work. But you have to govern it strictly, which most teams don't. Your colleagues are right to worry about depth for non-Microsoft sources.

You raise a critical point on governance. The default "all logs" posture is a significant operational shift. Teams moving from platforms like LogRhythm are accustomed to defining a collection profile upfront. With Sentinel, that discipline must be applied *after* ingestion, through careful log filtering and data transformation rules in the workspace. It's a reactive, not proactive, cost control.

Your note on ML maturity for heterogeneous data is accurate. However, Sentinel's advantage is its integration with the wider Azure ML and Logic Apps ecosystem. The "building it yourself" path is more accessible than it was, though it certainly requires a different skill set, more data engineering than pure security analysis. The out-of-the-box ML detectors are indeed basic correlation rules.

You're right about the skill set shift being the real blocker, not the tools. Most security teams aren't built to write KQL queries that filter or aggregate at ingestion, let alone maintain Logic Apps for data transformation. That "reactive" cost control becomes a full-time data engineering job.

We had to build a separate pipeline using Azure Data Factory just to prune and normalize non-Azure logs before they hit the Log Analytics workspace, because the native filtering was too limited. Sentinel's "ecosystem" just means you're now managing multiple Azure services instead of one SIEM. The TCO math gets ugly fast if you need that depth.

Oh, you've nailed the exact misery. Building a whole ETL pipeline just to keep your SIEM from bankrupting you is a special kind of irony. That "ecosystem" tax is real.

Your point about it being a full-time data engineering job is the kicker. The sales pitch is always about the security analyst suddenly wielding KQL like a wizard, but the reality is you're now paying for a data engineer who doesn't care about ATT&CK frameworks, and a security analyst who's debugging JSON schemas instead of hunting threats.

So you end up with two half-teams and a Frankenstein's monster of Azure services, all to approximate the curated data model you got out of the box with something like LogRhythm. The cost isn't just in the portal bills, it's in the constant context-switching.

That automatic integration is incredibly real, but so is the bill shock that follows. Your colleagues have a point about depth for non-Microsoft logs, but for a mostly Azure environment, I think you made the right call. The efficiency gains are massive for a smaller team.

Sentinel's daily management is simpler for pure Azure monitoring once you've tamed the log ingestion. The trick is to aggressively filter at the diagnostic settings level, not later in Log Analytics. Turn off *everything* as a baseline, then only enable the specific log categories you need for compliance and threat hunting. It's the opposite of LogRhythm's mindset, but it keeps costs predictable.

On the AI/ML, Sentinel's built-in features are good for spotting known, weird Azure behavior fast. For truly custom, cross-platform ML, you'd need to build it yourself using Azure Machine Learning, which is a whole other skillset. If your team isn't ready for that, LogRhythm's packaged ML might feel more powerful. But for your scenario, Sentinel's native detection will likely cover 80% of your needs. Just watch those third-party log costs 😅