After managing a 12 TB/day Splunk deployment for five years, our leadership mandated a 40% reduction in SIEM operational costs. We evaluated the usual suspects (Elastic, Azure Sentinel, a brief look at Panther) but settled on Exabeam due to its bundled UEBA and a pricing model that appeared favorable for our scale. The migration was completed six months ago. This post details the technical journey, the actual cost outcomes, and operational observations for those considering a similar path.
**Deployment Architecture & Tooling**
We run in AWS, so we deployed Exabeam's Cloud Platform. The core components:
* **Ingestion:** Exabeam Data Collectors (EDCs) deployed as EC2 instances in an Auto Scaling Group behind an NLB.
* **Parsing & Normalization:** Exabeam Parsing Engines (EPEs) as EC2 instances.
* **Platform:** Exabeam Cloud (SaaS) for analytics, UEBA, and case management.
We used Terraform to manage all AWS resources (VPC endpoints, S3 buckets for cold storage, IAM roles, instance configurations). A critical design decision was leveraging AWS PrivateLink for all communication between our VPC and Exabeam's cloud services, eliminating public internet exposure.
```hcl
# Example Terraform snippet for the VPC Endpoint for Exabeam Log Ingestion
resource "aws_vpc_endpoint" "exabeam_ingest" {
vpc_id = aws_vpc.siem.id
service_name = "com.amazonaws.vpce.us-east-1.vpce-svc-0a1b2c3d4e5f6g7h"
vpc_endpoint_type = "Interface"
subnet_ids = [aws_subnet.ingest_subnet.id]
security_group_ids = [aws_security_group.vpce_sg.id]
private_dns_enabled = true
}
```
**Migration Process & Challenges**
1. **Log Source Onboarding:** This was the most time-intensive phase. Exabeam's parser library is comprehensive, but we had several custom CEF and JSON-formatted sources from legacy appliances that required custom parser development. The parser debugging tools were adequate but less intuitive than Splunk's field extractor preview.
2. **Historical Data Load:** We opted for a 90-day warm retention window. We re-ingested from our S3 archive using Exabeam's bulk ingestion API. This process was bandwidth and time-intensive but predictable.
3. **Rule Migration:** Translating Splunk SPL correlation searches to Exabeam's rule logic was a conceptual shift. We found Exabeam's session-based analytics and peer grouping reduced the need for many complex, threshold-based rules we had in Splunk. Example: A rule for "unusual AWS CLI activity" became simpler by leveraging the built-in UEBA user risk score instead of manually defining all permutations.
**Cost Analysis (Actuals vs. Projection)**
| Cost Component | Splunk (Final 6mo Avg) | Exabeam (Current 6mo Avg) | Notes |
| :--- | :--- | :--- | :--- |
| Licensing & Consumption | $215k/month | $148k/month | Includes platform & committed ingestion tier. |
| Infrastructure (AWS) | $42k/month | $18k/month | Significant reduction from managed Splunk indexers to lighter EDC/EPE VMs. |
| Operational Overhead | ~3.5 FTE | ~2 FTE | Reduced need for indexer cluster management and tuning. |
| **Total Effective Cost** | **~$290k/month** | **~$185k/month** | **~36% reduction.** Target met. |
**Operational Pitfalls & Insights**
* **Observability Gap:** The platform provides less granular system metrics than Splunk's monitoring console. We had to instrument our own CloudWatch dashboards for EDC/EPE health, queue depths, and ingestion latency.
* **API Rate Limiting:** The Cloud APIs for data export and case management have strict rate limits. Any automation scripts must implement robust backoff and retry logic.
* **Cold Search Performance:** Searches against data in S3 cold storage are noticeably slower than Splunk's frozen bucket search. This necessitates more careful warm/cold retention planning.
* **UEBA Value & Adjustment:** The UEBA engine initially generated many "benign true positives" as it learned our baseline. We had to fine-tune entity scoring thresholds and create watchlist exclusions for noisely service accounts, which took about two months to stabilize.
**Conclusion**
The migration achieved its primary cost-saving objective. The integrated UEBA has provided tangible value in identifying stealthy threats that our old rule set missed. However, the platform feels less "open" and requires accepting Exabeam's way of doing things. For teams deeply invested in SPL and expecting the same level of system transparency as Splunk's on-prem deployment, this will be a significant cultural and operational shift. For our use case—where reducing operational burden and total cost were paramount—the outcome has been positive, albeit with a steeper than anticipated learning curve for the SOC analysts.
I run a 5 TB/day hybrid K8s environment for a financial services company, managing both Elastic (for app logs) and a licensed Splunk Cloud instance for our core security logs. I've done two major SIEM migrations in the last five years.
- **Real pricing:** The big win is predictable cost per GB/day ingestion, but watch for UEBA license minimums. They were high at my last shop, a hard floor of ~$100k/year. The apparent 40% savings often comes from cutting historical search depth and shifting cold data to cheap S3.
- **Deployment effort:** You got it right. Using Terraform and PrivateLink is the only sane way. The EDC/EPE config is brittle; expect 2-3 weeks of tuning parsers and regex to match Splunk's field extractions. Their out-of-the-box parsing is weaker.
- **Where it wins:** The bundled UEBA and built-in timeline for analysts is good if your team is small. It's a more opinionated, SOC-centric workflow than Splunk's 'build-your-own'. For standard use cases (account anomalies, lateral movement), you get value faster.
- **Where it breaks:** Ad-hoc big data investigation is gone. Searches over >30 days of hot data are slow, and complex correlation outside their rule system is painful. It's not a data lake. At our scale, we hit EPC scaling limits and had to batch some log types.
I'd stick with Splunk if you need deep, fast historical search across all data. Choose Exabeam if your main goal is UEBA-driven alerting with a modern SOC workflow and you can live with limited, slower ad-hoc exploration. Tell us your hot data retention and how many analysts do custom searches.
Ship it, but test it first