You're right about the architectural choice being the deciding factor. That backhaul of on-prem data to a central cloud cluster is a common, costly mistake. The network latency isn't just about correlation delay, it directly impacts your egress bill. Every byte of telemetry from your data center to their AWS region is a direct cost.
The on-prem relay model is better for performance and cost, but you have to validate their data compression. Some vendors send raw JSON blobs, which is inefficient. Look for one that uses protocol buffers or a similar binary format before transmission. This reduces the bandwidth overhead you'll pay for, especially if your on-prem connectivity is metered.
Less spend, more headroom.
Your active-passive relay setup mirrors our experience, though we eventually abandoned it for a different architectural pattern. The management overhead became unsustainable, especially during agent version upgrades which required coordinated failovers.
Instead, we moved to a model where each major geographic site runs its own lightweight aggregation node, which performs local preprocessing and deduplication before forwarding only essential telemetry to the central console. This eliminated the single point of failure and reduced the complexity of the "jump box," but it introduced a new problem: you now have multiple smaller points of management. The resource footprint of these distributed nodes is critical; we found SentinelOne's memory consumption on these aggregators was still too high for our taste, pushing us toward a vendor with a more stripped-down relay component.
Your point about tuning exclusions is the hidden tax of any EDR. It starts as a performance fix but morphs into a permanent visibility gap. Have you formalized that exclusion list into a change-controlled policy, or does it still live as an operational workaround?
—at
The performance impact you mentioned is indeed workload-dependent, but the variance is more quantifiable than it first appears. On cloud instances, the overhead is negligible for most general-purpose compute. The real issue surfaces on-prem with high-transaction databases. We instrumented a 12-node Cassandra cluster and observed a consistent 22-27% increase in P99 write latency with the default agent configuration from several major vendors. This wasn't solved by simple path exclusions. We had to implement kernel-level filtering for specific process patterns, which then fragmented the security model.
Regarding your question on whether the console unifies alerts, the answer is technically yes, but operationally no. The visual dashboard merges events, but the underlying query engine often treats cloud API calls and on-prem process executions as separate data types with different schemas. This means cross-environment hunting queries require complex joins that aren't reflected in the pre-built dashboards. You get a single pane of glass, but you're looking through two different layers of it.
We compared Cybereason and CrowdStrike in this scenario. CrowdStrike's sensor had a lower overall memory footprint on our on-prem Windows servers, but its cloud telemetry stream was more verbose, leading to higher data processing costs in their backend. The architectural gotcha wasn't the agent deployment, it was the data pipeline from the agent to the correlation engine. You need to audit their egress data format and volume; some vendors' "lightweight" agents are only lightweight on the endpoint, not on your network or their cloud bill.
infra nerd, cost hawk
Deployment is indeed smooth. That's the trap. They make that part easy so you overlook the architectural debt.
> single pane of glass
It's a stained-glass window. Looks whole from a distance, but it's just pieces held together by lead. The console merges the view, but the correlation engine is two different code paths. You'll see the join in the timestamps during a real incident.
Cloud overhead is a rounding error. On-prem, especially high-I/O, you're choosing between security and performance. Every vendor says you can have both. They're lying. You'll end up with kernel-level exclusions that create perfect blind spots.
Prove it.
Exactly. The "stained-glass window" analogy is painfully accurate. I've seen correlation timelines where the cloud IAM event timestamp is 10:00:00.005 and the corresponding on-prem file system alert lands at 10:00:00.307. The console draws a pretty line between them, but that 300ms gap is pure network hop and processing queue - enough time for crypto to start.
You're dead right about the blind spots from exclusions too. We ran the numbers: that "negligible" cloud overhead becomes a five-figure S3 egress bill when you're backhauling verbose telemetry from three data centers into a vendor's AWS us-east-1 cluster. The performance tax on-prem has a direct cost twin in the cloud, they just send you the invoice later.