Microsoft Sentinel vs Splunk: Choosing a SIEM

Both tools do the same core job: collect logs, correlate events, and surface the alerts that matter. The differences show up in pricing model, query language, and how much infrastructure you're on the hook for.

What each product actually is

Microsoft Sentinel is a cloud-native SIEM built on Azure Log Analytics. There's no infrastructure to patch, no indexer cluster to size, and it uses Kusto Query Language (KQL) for everything from hunting to detection rules. It's billed per GB ingested into the workspace, with a few tiers (pay-as-you-go, commitment tiers starting around 100 GB/day) that change the per-GB rate.

Splunk started as an on-prem log platform and still runs that way for a lot of shops, though Splunk Cloud is now the default recommendation for new deployments. It uses SPL (Search Processing Language), which is older, more mature, and has a much bigger library of community apps on Splunkbase. Historically Splunk billed on ingestion volume too, but they've pushed customers toward workload-based pricing that charges for compute (search jobs, indexing) rather than raw data volume — worth checking current terms since this has shifted more than once.

Query language: KQL vs SPL

KQL reads like a pipeline of filters, similar to LINQ if you've touched C#:

SecurityEvent
| where EventID == 4625
| summarize FailedLogons = count() by Account, bin(TimeGenerated, 1h)
| where FailedLogons > 10

SPL does the same thing with a different syntax:

index=wineventlog EventCode=4625
| bucket _time span=1h
| stats count as FailedLogons by Account, _time
| where FailedLogons > 10

Analysts who've used SQL tend to pick up KQL faster. SPL has more built-in commands for things like transaction, eventstats, and machine learning toolkit integration, which matters if you're doing anomaly detection beyond simple thresholds. Neither language is objectively better — the real cost is retraining a team that already has years of muscle memory in one or the other.

Data ingestion and connectors

Sentinel has an edge if your estate is already Microsoft-heavy: native, low-friction connectors for Azure AD (Entra ID) sign-in logs, Defender for Endpoint, Office 365, and Azure activity logs. Piping in AWS or on-prem Syslog data works fine through the Azure Monitor Agent, but it's an extra hop compared to native Azure sources.

Splunk's connector ecosystem is broader in raw count because it's been around longer — Splunkbase has thousands of apps and add-ons, including community-maintained ones for niche products. If you're ingesting from a mixed environment (Cisco firewalls, legacy on-prem AD, random SaaS apps with no modern API), you'll likely find a pre-built Technology Add-on (TA) for Splunk before you find an equivalent Sentinel connector.

Detection rules and threat intelligence

Sentinel ships with analytics rule templates mapped to MITRE ATT&CK, and Microsoft's own threat intel feed (Microsoft Threat Intelligence) integrates directly. Fusion, Sentinel's correlation engine, links low-fidelity alerts into a single incident automatically, which cuts down on alert fatigue for smaller teams without a dedicated detection engineering group.

Splunk Enterprise Security (a separate paid add-on, not included in base Splunk) gives you Notable Events, risk-based alerting, and a more customizable correlation search framework. Risk-based alerting in particular — scoring entities over time rather than firing on single events — is one of the stronger detection patterns available in either platform, and Splunk has had it longer.

Cost and operational overhead

Sentinel's serverless model means no capacity planning for indexers or search heads, but ingestion costs can climb fast if you're logging verbose sources like DNS or firewall traffic without filtering first. Data Collection Rules (DCRs) let you filter and transform data before it hits the workspace, which is worth setting up early rather than after your first surprise bill.

Splunk on-prem gives you full control over retention and hardware sizing but means someone owns the indexer cluster, the license usage, and the upgrade cycle. Splunk Cloud removes most of that but you're still paying for compute-heavy searches under the newer pricing model, so badly written SPL queries hit your wallet more directly than in the old ingestion-based scheme.

Which one fits your environment

If you're already deep in Azure and Microsoft 365, Sentinel usually costs less to stand up and maintain. If you need broad third-party integrations, a mature app ecosystem, or your team already knows SPL, Splunk's flexibility pays off despite the higher operational lift. A lot of larger enterprises actually run both — Splunk for legacy on-prem sources, Sentinel for the Azure-native side — and forward summarized data between them rather than picking one exclusively.

For more on building detection rules and log pipelines, check out the related SIEM and Blue Team segments on Korra Studio.