Azure Sentinel vs Splunk vs Datadog: SIEM Compared

Choosing a SIEM (Security Information and Event Management) platform is one of the most expensive and consequential decisions a security team makes. The platform you select will determine how your analysts investigate threats, how quickly you detect breaches, and how much you spend on security data storage for the foreseeable future. Switching SIEMs after deployment is painful — it means rewriting detection rules, rebuilding integrations, retraining analysts, and migrating historical data.

Microsoft Sentinel (formerly Azure Sentinel), Splunk Enterprise Security, and Datadog Security Monitoring represent three fundamentally different approaches to the SIEM problem. Sentinel is cloud-native and deeply integrated with the Microsoft ecosystem. Splunk is the industry incumbent with the most flexible data platform. Datadog started as an observability tool and has expanded into security monitoring with a unified approach to logs, metrics, and traces.

This guide compares all three across the dimensions that matter most — features, pricing, deployment complexity, integration ecosystems, compliance support, and organizational fit — so you can make an informed decision rather than a vendor-influenced one. For organizations evaluating their broader cloud security posture, our guide on zero trust security provides the strategic framework that these SIEM platforms help enforce.

For context on building a security operations capability from scratch, see our guide on NOC vs SOC: key differences you need to know.

Feature Comparison

Core SIEM Capabilities

Capability	Microsoft Sentinel	Splunk Enterprise Security	Datadog Security Monitoring
Log collection	200+ native connectors, custom via Logstash/Fluent	Universal forwarder, HEC, 1000+ add-ons	Datadog Agent, 750+ integrations
Detection rules	KQL-based analytics rules, ML-based anomalies	SPL-based correlation searches, risk-based alerting	Threshold, anomaly, and log detection rules
SOAR (orchestration)	Built-in (Logic Apps playbooks)	Splunk SOAR (separate product)	Workflow automation (Datadog Workflows)
Threat intelligence	Microsoft Threat Intelligence, TAXII/STIX feeds	Splunk Intelligence Management, TAXII/STIX	Third-party TI integration via API
UEBA	Built-in entity behavior analytics	Splunk UBA (separate product)	Limited behavioral analytics
Investigation	Investigation graph, Notebooks (Jupyter)	Investigator dashboard, Splunk ES investigator	Security Signals explorer, correlated traces
Case management	Incidents with task assignment	Notable events with workflow	Security Signals with triage workflow
Compliance dashboards	Workbooks for CIS, NIST, PCI	Compliance add-ons (CIM-based)	Compliance monitoring (CSPM)

Detection and Analytics

Microsoft Sentinel uses Kusto Query Language (KQL) for detection rules. Microsoft’s Sentinel documentation provides a full overview of the platform’s architecture and capabilities. KQL is powerful and well-documented, but it is specific to the Microsoft ecosystem — analysts coming from other platforms will need training. Sentinel’s strength is its built-in analytics rules. Microsoft publishes hundreds of pre-built detections in the Content Hub that cover common attack patterns, and Fusion ML correlates low-fidelity alerts across multiple data sources to surface multi-stage attacks.

// Example: Detect brute force attempts against Azure AD
SigninLogs
| where ResultType == "50126" // Invalid username or password
| summarize FailedAttempts = count(),
    DistinctAccounts = dcount(UserPrincipalName)
    by IPAddress, bin(TimeGenerated, 1h)
| where FailedAttempts > 50
| where DistinctAccounts > 5

Splunk uses Search Processing Language (SPL), which is the most flexible query language of the three. SPL can handle virtually any data transformation, and Splunk’s risk-based alerting (RBA) model is particularly mature — it assigns risk scores to entities (users, hosts, IPs) and triggers alerts when an entity’s cumulative risk exceeds a threshold. This significantly reduces alert fatigue compared to rule-per-rule alerting.

// Example: Detect brute force attempts in Splunk
index=auth sourcetype=azure:signin ResultType=50126
| stats count as FailedAttempts dc(UserPrincipalName) as DistinctAccounts by src_ip
| where FailedAttempts > 50 AND DistinctAccounts > 5

Datadog uses a simpler detection rule framework with threshold-based, anomaly-based, and log detection rules. Detection rules are defined through a UI-driven workflow rather than a query language, making them more accessible to teams without dedicated SIEM engineers. However, this simplicity limits the complexity of detections you can build compared to KQL or SPL.

SOAR and Automation

Sentinel has the tightest SOAR integration because it uses Azure Logic Apps for playbooks. Playbooks can automatically enrich alerts (look up IP reputation, check user risk score), contain threats (disable a compromised user account, block an IP in the firewall), and manage case workflows (create tickets in ServiceNow, send Slack notifications). Logic Apps’ 400+ connectors provide out-of-the-box integration with most enterprise tools.

Splunk SOAR (formerly Phantom) is a separate product with its own licensing. It is arguably the most capable SOAR platform on the market, with a visual playbook editor, a large library of pre-built playbooks, and deep integration with security tools. However, it adds significant cost and operational complexity.

Datadog offers Workflow Automation, which can trigger actions based on security signals (notifications, webhooks, API calls). It is less mature than Sentinel’s Logic Apps or Splunk SOAR but sufficient for basic automation (notify on-call, create Jira ticket, trigger a remediation script).

Pricing Models

Pricing is often the deciding factor, and the three platforms use fundamentally different models.

Microsoft Sentinel Pricing

Sentinel charges based on data ingestion volume into the Log Analytics workspace:

Tier	Price (per GB/day)	Effective Monthly Cost (1 TB/day)
Pay-As-You-Go	~$2.76/GB	~$84,180/month
100 GB/day commitment	~$1.96/GB	~$59,780/month
500 GB/day commitment	~$1.68/GB	~$51,240/month
1 TB/day commitment	~$1.50/GB	~$45,750/month

Cost advantages:

• Free ingestion for Microsoft 365 audit logs, Azure Activity logs, and Azure AD sign-in logs (through the free data sources benefit)
• Microsoft Defender data ingestion is included if you use the Microsoft 365 Defender integration
• 90 days of interactive data retention included, with up to 12 years of archival retention at reduced rates

Cost risks:

• Ingestion costs scale linearly with data volume — unexpected log sources can spike costs
• KQL queries on large datasets consume compute resources that can drive additional costs
• No built-in cost cap — a misconfigured data source can generate significant charges before it is detected

Splunk Pricing

Splunk offers multiple pricing models:

Model	How It Works	Best For
Ingest-based	Price per GB/day ingested	Predictable log volumes
Workload-based (SVCs)	Price per Splunk Virtual Compute unit	Variable data volumes with heavy search
Entity-based	Price per monitored entity (host, container)	Infrastructure-heavy environments

Splunk’s ingest-based pricing is typically $1,800-$2,400 per GB/day/year for Enterprise Security, making it significantly more expensive than Sentinel at scale. However, the workload-based model can be more cost-effective for organizations that ingest large volumes but search infrequently.

Cost risks:

• Splunk is consistently cited as the most expensive SIEM in analyst reports
• License compliance is complex — exceeding your daily ingest limit triggers warnings and potential enforcement
• Enterprise Security, SOAR, and UBA are separately licensed add-ons

Datadog Security Monitoring Pricing

Datadog charges per analyzed log (indexed and processed for security):

Component	Price
Security Monitoring	~$0.20/GB analyzed logs
Cloud SIEM	~$0.10 per analyzed log (per million)
CSPM (Cloud Security Posture)	~$7.50/host/month
CWS (Cloud Workload Security)	~$12/host/month

Cost advantages:

• Significantly cheaper per GB than Sentinel or Splunk for pure log analysis
• Unified billing with observability (combine APM, infrastructure monitoring, and security on one bill)
• No separate license for automation capabilities

Cost risks:

• Security-specific features (CSPM, CWS) are add-ons with per-host pricing that can add up in large environments
• Retention costs increase with longer retention periods
• Less mature cost management tooling compared to Sentinel’s Azure Cost Management integration

Deployment Complexity

Microsoft Sentinel

Deployment model: Cloud-native (SaaS). No infrastructure to manage.

Setup process:

1. Create a Log Analytics workspace in Azure
2. Enable Microsoft Sentinel on the workspace
3. Configure data connectors (Microsoft sources are one-click, third-party sources require Syslog/CEF forwarders or API configuration)
4. Enable analytics rules from the Content Hub
5. Configure automation playbooks

Time to first detection: 1-3 days for Microsoft data sources, 1-2 weeks for a fully configured deployment with custom detections.

Operational overhead: Low. Microsoft manages the infrastructure, scaling, and patching. Your team focuses on detection engineering, incident response, and tuning.

Splunk Enterprise Security

Deployment model: Self-managed (on-premises or cloud VMs), Splunk Cloud (SaaS), or hybrid.

Setup process:

1. Deploy indexers, search heads, and forwarders (or provision Splunk Cloud)
2. Configure data inputs (universal forwarder on endpoints, HEC for API-based sources, Syslog for network devices)
3. Install and configure Splunk Enterprise Security app
4. Customize correlation searches and notable event workflows
5. Deploy Splunk SOAR if automation is required

Time to first detection: 2-4 weeks for a production deployment. Splunk Cloud reduces infrastructure setup time but configuration complexity remains.

Operational overhead: High. Splunk requires dedicated administrators for index management, search optimization, capacity planning, and upgrades. Even on Splunk Cloud, you need Splunk-skilled staff for detection engineering and platform management.

Datadog Security Monitoring

Deployment model: Cloud-native (SaaS). Datadog Agent deployed on monitored hosts.

Setup process:

1. Deploy Datadog Agent on endpoints and servers
2. Configure log collection and forwarding
3. Enable Security Monitoring in the Datadog console
4. Configure detection rules (built-in rules are enabled by default)
5. Set up notification channels and workflows

Time to first detection: 1-2 days. Datadog’s strength is rapid deployment, especially for organizations already using Datadog for observability.

Operational overhead: Low to medium. Infrastructure is managed by Datadog. Detection rule management and tuning are the primary ongoing tasks.

Integration Ecosystems

Microsoft Ecosystem Advantage

Sentinel has a decisive advantage for organizations in the Microsoft ecosystem:

• Microsoft 365 Defender: Unified incident queue across endpoint, email, identity, and cloud app security
• Azure AD / Entra ID: Native sign-in and audit log collection, conditional access integration
• Microsoft Defender for Cloud: Cloud security posture management and workload protection
• Microsoft Purview: Data loss prevention and information protection
• Microsoft Intune: Device compliance signals for conditional access

If your organization runs Microsoft 365, Azure, and Windows endpoints, Sentinel provides the most integrated security experience with the least configuration effort.

Splunk’s Breadth

Splunk’s integration ecosystem is the broadest of the three:

• Splunkbase: 2,500+ apps and add-ons covering virtually every security tool, network device, and cloud platform
• Multi-cloud: Equal support for AWS, Azure, GCP, and hybrid environments
• ITSM integration: Deep integration with ServiceNow, Jira, PagerDuty
• Third-party security tools: Pre-built integrations with CrowdStrike, Palo Alto, Fortinet, Zscaler, and hundreds more

Splunk is the strongest choice for multi-cloud, multi-vendor environments where no single ecosystem dominates.

Datadog’s Observability Convergence

Datadog’s integration strength is the convergence of security and observability:

• APM correlation: Security signals correlated with application traces
• Infrastructure metrics: Security events enriched with host-level metrics
• Container and Kubernetes security: Native integration with containerized workloads
• CI/CD pipeline visibility: Security insights into build and deployment processes

Datadog is strongest for DevSecOps teams that want security monitoring integrated into their existing observability workflow rather than as a separate tool.

Compliance Support

For organizations with regulatory requirements, SIEM compliance capabilities matter. For a deeper look at compliance frameworks, see our guide on navigating IT compliance standards.

Compliance Need	Sentinel	Splunk	Datadog
Pre-built compliance dashboards	Workbooks for NIST, CIS, PCI DSS, HIPAA, SOC 2	CIM-based compliance add-ons	CSPM benchmarks (CIS, PCI)
Data residency	Azure region selection (data stays in selected region)	Self-managed: you control data location. Cloud: limited region options	Datadog region selection (US, EU)
Audit logging	Azure Activity logs, diagnostic logs	Splunk audit logs (self-managed)	Datadog audit trail
Retention	90 days interactive, 12 years archive	Configurable (self-managed), 365 days (Cloud)	15 days standard, extended retention available
Certifications	SOC 2, ISO 27001, FedRAMP High, HIPAA BAA	SOC 2, ISO 27001, FedRAMP (Cloud)	SOC 2, ISO 27001, HIPAA BAA
GovCloud	Azure Government (IL5, FedRAMP High)	Splunk Cloud for Government	Limited government-specific offerings

The NIST Cybersecurity Framework provides a useful lens for evaluating how each SIEM supports your compliance program. Sentinel has the strongest compliance story for government and regulated industries due to Azure Government’s FedRAMP High authorization and IL5 (Impact Level 5) certification. Splunk’s self-managed deployment gives you complete control over data residency but shifts compliance responsibility to your team.

Best Fit by Organization Size

Small to Mid-Market (50-500 employees)

Recommended: Microsoft Sentinel

Reasoning:

• Free ingestion for Microsoft 365 and Azure data sources covers the bulk of SMB security data
• Cloud-native deployment eliminates infrastructure management
• Built-in analytics rules and Content Hub provide out-of-the-box detection without a dedicated SIEM engineering team
• Lower TCO than Splunk at smaller data volumes
• Integrates with the Microsoft 365 ecosystem that most SMBs already use

For SMBs building incident response capabilities, see our guide on how to create a cybersecurity incident response plan.

Mid-Market to Large Enterprise (500-10,000 employees)

Recommended: Microsoft Sentinel or Splunk Enterprise Security

The decision depends on ecosystem:

• Microsoft-heavy environments: Sentinel provides better integration, lower cost, and less operational overhead
• Multi-cloud or multi-vendor environments: Splunk provides broader integration coverage and more flexible data handling
• Existing Splunk investment: If you already have Splunk deployed and a team skilled in SPL, the switching cost to Sentinel may not be justified

Large Enterprise (10,000+ employees)

Recommended: Splunk Enterprise Security or Microsoft Sentinel

At enterprise scale:

• Splunk offers the most mature risk-based alerting, the most flexible query language, and the deepest integration ecosystem. The higher cost is offset by the platform’s ability to handle complex, multi-source correlation at scale.
• Sentinel is increasingly competitive at enterprise scale with commitment tier pricing, and its cloud-native architecture eliminates the infrastructure management burden that large Splunk deployments require.

DevOps-Centric Organizations

Recommended: Datadog Security Monitoring

For organizations where DevOps and security teams overlap:

• Unified observability and security platform reduces tool sprawl
• Security signals correlated with application and infrastructure data
• Familiar interface for teams already using Datadog for monitoring
• Lower barrier to entry than Sentinel or Splunk for security-specific use cases

Migration Considerations

If you are migrating between SIEM platforms, plan for these factors:

Detection Rule Translation

Detection rules do not translate 1:1 between platforms. KQL, SPL, and Datadog’s rule syntax are fundamentally different. Budget time for:

• Auditing existing rules and identifying which ones are still relevant
• Rewriting critical detections in the new platform’s language
• Validating that rewritten rules produce equivalent results
• Building new detections that take advantage of the target platform’s unique capabilities

Data Migration

Historical log data is generally not migrated between SIEMs. Instead:

• Run both platforms in parallel during the transition period (typically 90 days)
• Maintain read-only access to the old platform for historical investigations
• Archive historical data to cold storage (S3, Azure Blob, GCS) for long-term retention and compliance

Team Training

Each platform requires different skills:

• Sentinel: KQL, Azure ecosystem knowledge, Logic Apps for automation
• Splunk: SPL, Splunk administration, Splunk SOAR for automation
• Datadog: Datadog query syntax, Datadog Agent management, Workflow Automation

Budget 2-4 weeks of training for analysts and 4-6 weeks for SIEM engineers when switching platforms. For organizations building their monitoring infrastructure alongside SIEM, our guide on building Azure dashboards covers complementary observability tooling. Exodata’s security and compliance team can help evaluate which platform fits your organization and assist with migration planning.

Frequently Asked Questions

Can I use Sentinel and Splunk together?

Yes. Some organizations use Sentinel as their primary SIEM for Microsoft data sources and forward non-Microsoft data to Splunk (or vice versa). However, this approach increases complexity, cost, and the risk of detection gaps between platforms. A single SIEM with comprehensive data coverage is generally preferable.

Is Datadog a real SIEM?

Datadog Security Monitoring provides core SIEM capabilities — log collection, detection rules, incident triage, and compliance monitoring. However, it lacks some features that traditional SIEM platforms offer (advanced UEBA, deep forensic investigation, complex correlation rules). It is best suited for organizations that want security monitoring integrated with observability rather than a standalone SOC platform.

How much data does a typical organization ingest into a SIEM?

Ingestion volumes vary widely. A 500-person organization with Microsoft 365, Azure infrastructure, and endpoint security typically ingests 10-50 GB/day. A 5,000-person enterprise with on-premises infrastructure, multiple cloud platforms, and network security devices can ingest 200-1,000 GB/day. Network flow data and verbose application logging are the largest contributors to data volume.

Does Sentinel work for multi-cloud environments (AWS, GCP)?

Yes. Sentinel has data connectors for AWS (CloudTrail, GuardDuty, Security Hub) and GCP (Cloud Audit Logs, Security Command Center). However, the integration depth is not as seamless as with Azure-native data sources. For organizations with significant AWS or GCP footprints, Splunk or Datadog may provide a more balanced multi-cloud experience.

What is the minimum team size needed to operate each platform?

• Sentinel: 1-2 analysts for a small deployment (leveraging built-in rules and automation). 3-5 for a mature SOC.
• Splunk: 2-3 analysts plus 1-2 dedicated Splunk administrators. 5-10+ for an enterprise deployment.
• Datadog: 1-2 analysts for security monitoring alongside existing Datadog operators. 3-5 for dedicated security operations.

How do these platforms handle zero-day threats?

All three platforms can detect zero-day threats through behavioral analytics and anomaly detection rather than signature-based rules. Sentinel’s Fusion ML and Microsoft Threat Intelligence provide automated zero-day detection. Splunk’s risk-based alerting surfaces unusual entity behavior that may indicate novel attacks. Datadog’s anomaly detection identifies deviations from baseline behavior. No SIEM detects all zero-day threats — detection coverage depends on data source breadth, rule quality, and analyst skill.

Next Steps

Choosing the right SIEM platform does not have to be overwhelming. Exodata’s security and compliance team can help you evaluate Sentinel, Splunk, and Datadog against your organization’s specific requirements, data sources, and budget. Talk to an engineer today — no sales pitch, just answers.