How to choose a SIEM

A SIEM (security information and event management platform) centralizes log and event data from across an organization's systems, correlates it to find signs of an attack, and gives a security team a place to investigate and respond. Almost every organization with a formal security function needs one; the differences that decide which one fit are less about whether a platform can detect a given attack technique — most competent SIEMs can, given the right content — and more about deployment model, how detection logic is authored and maintained, and how storage economics behave as retention grows.

Deployment: cloud-native, on-premises, or self-hosted

Microsoft Sentinel, Exabeam, Securonix, Devo, Google Security Operations, Panther and Rapid7 InsightIDR are all cloud-native SaaS with no self-hosted option, built on elastic cloud storage rather than customer-managed indexing infrastructure. IBM QRadar is the exception with a genuine on-premises appliance option alongside SaaS, which matters if your organization has a hard requirement to keep security telemetry off a public cloud. Wazuh is the other end of the spectrum entirely — free, open-source and self-hosted by default, with an optional paid managed-cloud tier from the vendor rather than a SaaS-first product.

If a no-cloud or air-gapped requirement is fixed, that alone narrows the field to QRadar or Wazuh. If cloud is acceptable, the deployment question resolves quickly and the real decision moves to detection approach and storage economics.

How detection logic is written and maintained

This is where the platforms diverge most in daily practice, not just in architecture.

• Behavioral baselining (UEBA) platforms build a model of normal behavior per user or asset and surface deviations automatically. Exabeam makes this its core differentiator with Smart Timelines that auto-assemble related events into a narrative, reducing manual correlation work. Securonix pairs the same approach with a large library of prebuilt behavior-analytics content specifically for insider threat and cloud threat detection.
• Rule-and-correlation platforms rely on hand-written or vendor-supplied correlation rules evaluated against incoming events. IBM QRadar's offense model is the longest-established example, and many mature SOCs have built years of process around exactly that workflow.
• Detection-as-code is a distinct philosophy: Panther writes detections as version-controlled Python rather than point-and-click rules, aimed squarely at engineering-led security teams that already run CI/CD for infrastructure and want detections tested like software.
• AI-assisted investigation is increasingly layered on top rather than replacing the above: Microsoft Sentinel's Copilot for Security and Google Security Operations' Gemini-based assistance both aim to speed up an analyst's investigation and detection-rule authoring, on top of whichever core detection model the platform uses.

Ask which model matches how your team actually works today — a UEBA-first platform handed to analysts trained on manual correlation rules, or a code-based platform handed to a team with no engineering background, both under-deliver regardless of the underlying detection quality.

Storage architecture and retention economics

Traditional SIEMs that index everything get slower and more expensive as retention windows extend, which several vendors here are explicitly built to avoid. Devo is built to keep query performance fast even at petabyte-scale volume, positioning that directly against SIEMs that degrade with retention length. Securonix and Panther both decouple storage from the analytics layer using a data-lake architecture (Securonix on Snowflake, Panther exposing SQL-based retrospective search over its own lake), enabling long retention without index-everything cost. Microsoft Sentinel splits ingestion explicitly into a full-price Analytics tier for real-time alerting and a cheaper Data Lake tier for high-volume logs kept for retrospective search only. Google Security Operations takes a different approach — flat-rate ingestion with long retention built into Google-scale infrastructure, rather than charging per query or scanned byte.

If your compliance or investigative needs require multi-year retention, ask specifically how query performance and cost behave at that retention window, not just at 30 or 90 days.

Pricing model

SIEM pricing rarely publishes a usable rate card, and the metering unit itself varies: IBM QRadar quotes by events-per-second or flow capacity; Microsoft Sentinel bills per GB ingested per day with commitment-tier discounts but no flat published dollar figure; Rapid7 InsightIDR prices per protected asset rather than data volume, which it markets as more predictable than per-GB billing; Exabeam, Securonix, Devo, Google Security Operations and Panther are all quote-only with no published self-serve tiers. Wazuh is the clear exception: the core platform carries no license fee at all, with revenue coming from optional support contracts and managed cloud. Because ingest volume, asset count and event rate all move independently, get every vendor to size a quote against your actual estate rather than comparing headline pricing models.

A shortlist by situation

• If you're already on Azure and Microsoft 365, look at Microsoft Sentinel for native, low-friction ingestion of that telemetry.
• If you need an on-premises or appliance deployment, look at IBM QRadar.
• If your SOC wants behavioral detection over hand-written rules, look at Exabeam or Securonix.
• If your log volume is very high and growing, and query speed at long retention is the priority, look at Devo.
• If you want Google-scale retention with curated threat intelligence built in, look at Google Security Operations.
• If your security team is engineering-led and wants detections as version-controlled code, look at Panther.
• If you want predictable, asset-based pricing tied to vulnerability-management context, look at Rapid7 InsightIDR.
• If you want a fully open-source SIEM/XDR with no per-GB licensing, and can operate it yourself, look at Wazuh.
• If you're already on CrowdStrike Falcon and want a unified, high-volume log backbone, look at Falcon LogScale.

Questions to ask vendors

1. What does this cost at our real events-per-second or GB/day, sized against our actual log sources, not a demo dataset?
2. How does query performance change as retention grows from 90 days to one year or longer?
3. How much detection content is prebuilt and maintained by the vendor versus something our team has to write and tune ourselves?
4. What's the actual onboarding time and effort to connect our current log sources — identity provider, endpoint, cloud, network?
5. If we needed to migrate away, how portable are our detection rules, historical data and case history?

Common mistakes

Choosing a SIEM based on detection-technique marketing (AI, UEBA, behavioral) without checking whether the team has the skills or workflow habits to actually use it day to day. Under-budgeting for the detection engineering effort every SIEM requires regardless of vendor — a SIEM with no tuned content is an expensive log store, not a security program. And picking a per-GB-priced platform without modeling log volume growth, since a security team's log sources tend to expand over time as more systems get onboarded, not shrink.

For two comparisons that cover the range from cloud-native enterprise to open-source self-hosted, see IBM QRadar vs Microsoft Sentinel and Rapid7 InsightIDR vs Wazuh. See every tool in this category.