Sentinel alternatives that are not Splunk or Elastic

Another week, another thread where someone's sticker shock from their Sentinel bill has them scrambling. I get it. The Azure ecosystem has a gravitational pull, especially if you're already in it, but the pricing model for Sentinel feels like it was designed by a team that's never had to answer a FinOps report. The per-GB ingestion cost, the separate charge for analytics rules, the retention tiers... it adds up to a predictable and painful conversation with leadership every quarter.

So you're looking past the usual suspects. Splunk? You'd need a second mortgage. Elastic? A full-time job to keep it from eating your cluster alive. The real question is what you're actually trying to accomplish. Are you just doing compliance log collection? That's a different beast than a full-blown SOC building complex detection rules and automated playbooks. The "not Splunk or Elastic" field breaks down into a few camps, each with their own trade-offs that most vendor-friendly reviews gloss over.

First, the managed service / cloud-native crowd. This is where you find options like Panther and Sumo Logic. Panther's appeal is its developer-centric approach; you write detection logic as Python and it handles the scaling. Their data lake foundation can be cost-effective if you're smart about what you route where. Sumo Logic is more of a direct Sentinel competitor in the cloud, but their query pricing can bite you if you have high-cardinality data or run frequent, broad searches. The lock-in is real with both, but you trade that for not managing infrastructure.

Then there's the "build your own" path, which is where the cost control gets interesting but the complexity spikes. This is the Devo or Graylog world, or even rolling your own with a combo of OpenTelemetry collectors, a data lake (S3/Parquet), and a query engine (Trino, DuckDB). Detection-as-code frameworks like Sigma can then be executed with tools like Sigmac. Here's a grossly simplified example of what that pipeline might look like, just to illustrate the moving parts:

```yaml
# A hypothetical flow using OTel Collector, S3, and Athena
receivers:
filelog/security:
include: [/var/log/*.log]
processors:
attributes:
actions:
- key: log_type
value: "security"
action: insert
exporters:
aws/s3:
bucket: "my-security-lake"
format: parquet
partitioning:
- {attribute: log_type}
- {attribute: timestamp, layout: "2006/01/02"}

# Sigma rule translated and run via scheduled Athena query
title: Suspicious Service Installation
logsource:
product: windows
service: system
detection:
selection:
EventID: 7045
ServiceName: 'PsExec'
condition: selection
```

You're not paying for the SIEM per se, but you are paying for storage, query compute, and most expensively, the engineering hours to build and maintain the entire detection and alerting layer on top. This only makes sense past a certain scale or for very specific use cases.

Finally, the niche or bundled players like Wazuh (if you lean heavily on host-based detection) or even something like Chronicle SIEM if you're deep in Google Cloud. These can be powerful but come with their own constraints—Wazuh's interface is functional at best, and Chronicle's data model is a paradigm shift all its own.

What nobody tells you in the datasheets is that the biggest cost driver isn't the tool, it's your own data discipline. Are you ingesting every verbose debug log from an over-chatty application? Are your detection rules firing constantly on low-fidelity events? Before you jump ship, audit your Sentinel workspace. You might find that the real alternative is just a ruthless data filtering policy and turning off half your "legacy" analytic rules.

So, what's your actual workload? Compliance checkbox-ticking, threat hunting in cloud audit logs, or a full 24/7 SOC? The answer dictates whether you need a monolithic platform or if a composable, albeit more complex, stack could save you real money without sacrificing capability.

-- Cam