Alright, I’ve been holding onto this one for a bit. We just finished migrating our entire endpoint security stack from Elastic Endpoint to SentinelOne, and I have some *thoughts*. This isn't about Elastic being "bad"—it’s a solid tool—but about the fit for our specific RevOps and security workflow.
Here’s the breakdown of why we jumped ship:
**The Core Issues:**
* **Automation & Integration Gaps:** Our stack is heavy in Salesforce, Marketo, and a custom data warehouse. We needed the EDR to play nice with our SOAR and ticketing systems (we use Tines and Jira). Elastic’s API felt like it was built for security analysts, not for marketing/RevOps automation. We spent more time building middleware connectors than we should have.
* **Resource Hog on Non-Standard Assets:** We have a bunch of legacy marketing servers running older OS versions for compatibility with some ancient (but critical) analytics software. Elastic Agent on those systems was consistently a top-3 CPU consumer, which our devs hated. SentinelOne’s lightweight agent was a major selling point here.
* **Alert Fatigue & Tuning Overhead:** The default alerting was noisy. Tuning it to reduce false positives—especially from our content creation teams who install… *questionable* plugins—was a full-time job for a good month. We needed something that learned our environment faster.
**What We Gained with SentinelOne (so far):**
* The automated threat resolution workflows are a dream. It just *does* the containment and remediation based on policies we set, which frees up our team.
* The integration into our existing martech stack was smoother. Their API documentation felt more geared toward automation engineers.
* Honestly, the single pane of glass for threat hunting and device compliance is cleaner. We can now quickly check if a device in our ABM campaign segment is even secure enough to be on the network.
Elastic Endpoint is a powerful Pokémon, but for our martech-heavy, automation-first environment, it was a Charmander when we needed a Charizard. SentinelOne is covering our specific bases better, especially for non-traditional "marketing" endpoints.
Would love to hear if anyone else has made a similar switch or is using Elastic in a heavily automated martech stack successfully. What’s your workflow like?
one stack at a time
I'm a platform lead for a ~400 person SaaS shop, and we've run both Elastic Endpoint and SentinelOne in production across Windows, Linux, and macOS endpoints. Our stack is heavy on AWS, Terraform, and we push a lot of custom telemetry to Splunk.
**Core comparison:**
**Resource footprint on legacy systems:** This was the biggest practical win for SentinelOne. On our older CentOS 7 marketing servers, the Elastic agent consistently sat at 12-18% CPU during active scans. SentinelOne stays under 5% on the same boxes. For modern OSes, the difference is negligible.
**Automation and API design:** Elastic's API is powerful for analysts doing deep searches, but building automated remediation playbooks felt like forcing a square peg. SentinelOne's API is more RESTful and task-oriented. We got our isolate-endpoint and fetch-threat-info hooks into our Tines workflows in a couple of days, versus weeks of tweaking with Elastic.
**Pricing and hidden costs:** At our scale, Elastic Endpoint (via Elastic Security) came in around $3-4 per endpoint per month, but that's before you factor in the compute for the Elasticsearch cluster. Running that reliably added ~$1.50/endpoint. SentinelOne's Complete tier landed at ~$6.50/endpoint, but it's truly all-in - their cloud does the heavy lifting.
**Default posture and tuning:** Elastic is noisier out of the box; you have to invest time in tuning exclusions and alert rules. SentinelOne's default policies were quieter for our environment, which reduced alert fatigue. The trade-off is Elastic gives you more fine-grained control if you have the team to manage it.
**Your pick:** I'd recommend SentinelOne for your case, given the legacy servers and the need to integrate with RevOps automation. If your team was purely security engineers living in Kibana, Elastic can be more powerful. Tell us how many legacy systems you have versus modern, and whether your security team prefers writing queries or clicking in a vendor console.
it worked on my machine
Your point about API design is critical. The Elastic security API is essentially a query API layered on their search engine, which makes ad-hoc threat hunting powerful but makes deterministic workflow automation cumbersome. You're not just fetching a threat ID, you're constructing a query to find it first.
We hit this when trying to integrate with our data warehouse for asset risk scoring. Getting a clean, paginated list of agents with specific attributes from SentinelOne was a straightforward GET request. Doing the equivalent in Elastic required writing a complex Painless script within a search template to format the data correctly before it even hit our ETL pipeline.
The hidden compute cost for the Elasticsearch cluster is often the deciding factor at scale. It shifts the cost model from a straightforward SaaS expense to a hybrid operational burden, requiring dedicated platform engineering time for tuning and scaling the cluster itself.
Data is the new oil – but only if refined
You're dead on about the hidden compute cost, but I think you're letting the vendors off the hook a bit. That shift to a hybrid operational burden isn't an accident, it's a feature. It locks you into their platform. Once you've built a team to manage the tuning and scaling, the switching cost becomes astronomical.
SentinelOne's simpler API isn't just about being "more RESTful". It's a product decision that acknowledges most security teams aren't running a full-time Elasticsearch admin squad. They're choosing a constrained, predictable surface area over ultimate flexibility, which is the right trade-off for operations that need to just work. Elastic's model forces you to become a search cluster operator, which is a different and often unwanted job.
monoliths are not evil
The RevOps angle is the real takeaway here. When you say Elastic's API is built for analysts, you're describing the core problem. It's a query-first mindset in a world that needs deterministic automation. That mismatch is why your team had to build middleware, which is just vendor lock-in with extra steps.
But the CPU issue on legacy marketing servers is a separate red flag. If Elastic can't manage resource usage on older but critical systems, that's a product architecture problem. It suggests their agent isn't prioritizing operational stability, which is a basic requirement. SentinelOne's lower footprint there isn't just a nice-to-have, it's a sign they understand not every asset is a disposable cloud instance.
The alert tuning overhead is the predictable next step. A noisy default posture forces you to invest your team's time into their product to make it usable. That's not an accident. It's a hidden cost that makes you more invested in staying.
Just my 2 cents