The license cost comparison between open source and commercial SIEM platforms is the easiest number to put in a spreadsheet and the least representative of the actual decision. What genuinely differs at small scale isn’t primarily features — most SIEM platforms, open source or commercial, can ingest logs and run correlation rules — it’s who ends up doing the engineering work the platform doesn’t do automatically for you.
What a SIEM actually needs to do, regardless of vendor
Collect logs from disparate sources, normalize them into a queryable format, run detection rules against that normalized data, and surface alerts with enough context to actually investigate. Every SIEM, open source or commercial, needs all four of these working correctly to provide real value — a platform that ingests logs but has poorly tuned detection rules, or one with great detection logic but incomplete log coverage, provides a fraction of its potential value regardless of how much it costs.
Open source (the Elastic Stack, Wazuh, and similar): lower license cost, higher engineering cost
A note on terms: “open source” is used loosely here for platforms whose core can be self-hosted without a license fee. Licenses differ by project and version, so check the current license of whichever product you evaluate. The comparison in this article is editorial analysis based on general industry practice, not a benchmark or a price survey; it does not cite vendor price lists, and costs vary by vendor, contract and data volume.
Open source SIEM platforms have no licensing fee (or a much lower one for a supported distribution), which is the number that shows up first in any comparison. What doesn’t show up in that number: detection rule tuning, log source integration, dashboard building, and ongoing maintenance are largely work your team does itself, rather than work a vendor has already done and packaged. A commercial SIEM typically ships with a substantial library of pre-built, vendor-maintained detection rules and integrations for common log sources; an open source deployment often starts closer to a blank canvas, with detection logic and integrations built and maintained by whoever on your team owns the platform.
For a small team, this means the real cost isn’t the software — it’s the engineering time of whoever configures and maintains it, and that time has to come from somewhere, usually from a team member who doesn’t have “SIEM administrator” as their sole job title.
Commercial SIEM: higher license cost, more built-in coverage
Commercial platforms (Splunk, Microsoft Sentinel, and similar) typically include maintained detection rule libraries, pre-built integrations for common log sources, and vendor support when something isn’t working correctly. This meaningfully reduces the engineering burden of getting to a working, reasonably effective detection posture — you’re paying, in part, for work that’s already been done by the vendor’s team rather than needing to be done by yours.
The cost model for several major commercial platforms scales with data volume ingested, which is worth understanding specifically before committing — a small team’s data volume today may not reflect volume a year from now as logging coverage expands, and costs that seemed reasonable at initial scale can grow faster than expected as more log sources get added.
Where the real tradeoff sits for a small team specifically
Time available versus budget available is the actual axis, not feature comparison. A team with meaningful budget but no spare engineering time toward security tooling is generally better served by a commercial platform’s built-in coverage, even at higher license cost, because the alternative (an open source deployment with nobody properly maintaining it) often ends up providing less real detection value than its zero license cost suggests.
A team with more available engineering time than budget can get genuine value from an open source deployment, but should budget that engineering time honestly as a real cost — “free” software that requires meaningful ongoing configuration and tuning work isn’t actually free, it’s differently priced.
Data volume growth trajectory matters for commercial platforms specifically. If your logging needs are likely to expand significantly (more services, more detailed logging, longer retention), understand how a commercial platform’s pricing scales with that growth before committing, since volume-based pricing can turn an initially reasonable cost into a much larger one as coverage expands.
A middle path worth considering
Managed or hosted versions of open source platforms (a hosted Elastic deployment, for example) split the difference — you get the open-source platform’s flexibility and lower core licensing cost, without taking on the full operational burden of running and maintaining the infrastructure yourself. This is often a reasonable middle ground for a small team that wants more control than a fully commercial platform but doesn’t have the engineering capacity for a fully self-managed open source deployment.
The practical starting question
Before comparing specific products, the more useful question is: who on the team will actually own tuning detection rules, maintaining log source integrations, and responding to the alerts this generates, on an ongoing basis — not just at initial setup. If there’s no clear answer to that question, the platform choice matters less than the fact that whichever one is chosen risks becoming shelfware, generating either too much noise to be useful or too little coverage to catch what matters, regardless of its license cost or feature list.
