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IBM QRadar vs Microsoft Sentinel

QRadar is the mature, rule-and-offense SIEM with an on-premises option; Sentinel is cloud-native for organizations already standardized on Azure.

Side by side

IBM QRadar Microsoft Sentinel
Vendor IBM Microsoft Corporation
Pricing model Quote only Usage-based
Free tier No No
Deployment Cloud, Self-hosted Cloud
Open source No No
Best for Large enterprises and MSSPs with mature SOC processes built around correlation rules and offense management. Organizations already on Azure/Microsoft 365 wanting a natively integrated, elastically scaled SIEM.
Pricing

Quoted per events-per-second (EPS) or flow capacity; no published self-serve pricing.

Pricing has not been verified yet — see the vendor's site.

Analytics tier bills per GB ingested/day (pay-as-you-go or discounted commitment tiers up to 52% off); a separate Data Lake tier bills per GB for low-cost long-term storage. No flat dollar figures are published; use Microsoft's calculator for a region-specific rate.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Features
  • Real-time correlation rules across log and network flow data
  • User and entity behavior analytics (UEBA)
  • Integrated SOAR case management and playbooks
  • Network flow analysis alongside log data
  • SaaS or on-premises appliance deployment
  • Threat intelligence feed integration
  • Analytics and Data Lake ingestion tiers for cost-tiered retention
  • Prebuilt connectors for Microsoft 365, Entra ID, Defender and third-party sources
  • AI-assisted investigation via Copilot for Security
  • Automated response playbooks (SOAR)
  • User and entity behavior analytics (UEBA)
  • Commitment-tier discounts up to 50,000GB/day

Verdict

IBM QRadar and Microsoft Sentinel are both full SIEMs with correlation, user and entity behavior analytics, and SOAR-style response built in, but they come from different eras and architectures. QRadar has been the enterprise standard for over a decade, historically deployed as dedicated appliances and increasingly offered as SaaS, with a rule-and-offense model that mature SOC teams have often built years of process around. Sentinel is cloud-native from the start, built directly on the Azure Monitor Log Analytics workspace, which gives it elastic scale without customers managing their own indexing infrastructure, and splits ingestion into a full-price Analytics tier and a cheaper Data Lake tier for logs kept for retrospective search rather than real-time alerting.

The deciding factor for most buyers is less about detection quality and more about where you already live. QRadar remains the stronger fit for organizations with an established SOC built around its offense model, or that need the on-premises appliance option Sentinel doesn't offer. Sentinel's natural advantage is integration depth with Microsoft 365, Entra ID and Defender — if your identity and productivity stack is already Microsoft, Sentinel's connector catalog and AI-assisted investigation (Copilot for Security) plug in with far less integration work than bringing that same telemetry into QRadar.

Choose IBM QRadar if

  • You need an on-premises or appliance deployment option, not only SaaS.
  • Your SOC's processes, playbooks and analyst training are already built around QRadar's correlation-rule and offense model.
  • You want tight integration with IBM's broader security portfolio, including QRadar SOAR.

Choose Microsoft Sentinel if

  • Your organization is already substantially on Azure and Microsoft 365, and native, low-friction ingestion of that telemetry matters.
  • You want a two-tier ingestion model (Analytics for real-time alerting, Data Lake for cheaper long-term retention) to control cost as volume grows.
  • You want AI-assisted investigation tooling from the same vendor as your identity and productivity stack.

The honest caveat

Neither vendor publishes flat pricing — QRadar is quoted per events-per-second or flow capacity, and Sentinel's per-GB rate depends on region and requires Microsoft's calculator or a quote — so a side-by-side cost comparison requires sizing both against your actual ingest volume, not list prices. See anomaly detection and data lake.

Last reviewed September 22, 2026

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