The core problem is that both platforms can bankrupt you if you treat them as a data lake. The key difference is in how you architect control.
Splunk's cost is driven by ingest volume. You pay per GB/day. Control is primarily through:
* Forwarder-side filtering (`props.conf`, `transforms.conf`)
* Index-time routing to lower-cost indexes (frozen -> cold -> warm)
Example `props.conf` on a heavy forwarder:
```
[source::.../app.log]
TRANSFORMS-drop_noisy_events = drop_debug_logs
TRANSFORMS-route_to_cold = route_to_cold_index
[transforms-drop_debug_logs]
REGEX = .*DEBUG.*
DEST_KEY = queue
FORMAT = nullQueue
[transforms-route_to_cold_index]
SOURCE_KEY = _raw
REGEX = .*(ERROR|FATAL).*
DEST_KEY = _MetaData:Index
FORMAT = cold_index
```
Elastic's cost is driven by the underlying Elasticsearch cluster resources (storage, compute). Control is about reducing resource consumption:
* Ingest pipeline filtering/dropping before indexing.
* Data stream lifecycle policies to move data to less performant tiers.
* Aggressive use of data rollups (historical) or downsampling (real-time).
Example ingest pipeline node:
```
{
"description": "Drop debug logs and sample INFO",
"processors": [
{
"drop": {
"if": "ctx.message.contains('DEBUG')"
}
},
{
"sample": {
"if": "ctx.message.contains('INFO')",
"ratio": 0.1
}
}
]
}
```
The verdict: Elastic gives you more low-level, infrastructure-centric knobs if you self-manage. Splunk's licensing model forces a more upfront, volume-centric discipline. For pure cost control, Elastic on modest hardware can be cheaper for high-volume, low-value data. Splunk's predictability can be an advantage in strictly licensed environments, but you must be ruthless at the forwarder.