After evaluating Cribl Stream for a proof-of-concept last year, our team faced the inevitable build-vs-buy decision, which in Cribl's case translates to Cloud-vs-self-managed. We committed to a six-month parallel run, operating two identical processing pipelines: one on Cribl's Cloud platform and another self-managed on AWS EKS. The goal was to move beyond theoretical pricing calculators and gather real-world, month-over-month cost data. The results were more nuanced than expected, heavily dependent on specific architectural patterns.
Our environment processed an average of 1.2 TB/day of log and metric data, sourced from a mix of AWS services, containerized applications, and on-premises forwarders. The pipelines performed standard operations: filtering, sampling, PII masking, routing to three distinct destinations (Snowflake, S3 for cold storage, and a third-party SIEM), and real-time alerting.
**Self-Managed (AWS EKS) Cost Breakdown:**
The self-managed cluster ran three Worker Node groups optimized for different queue stages. Primary costs were:
* **Compute (EC2):** ~$1,850/month for a mix of `c6i.4xlarge` and `r6i.2xlarge` instances (including reserved instance discounts).
* **Kubernetes Management:** ~$300/month for EKS control plane, managed node groups, and associated load balancers.
* **Data Transfer & Storage:** ~$225/month for inter-AZ traffic, S3 API calls for Queues, and EBS volumes.
* **Operational Overhead:** This is the intangible but critical factor. A senior engineer spent approximately 12-15 hours per month on cluster maintenance, security patching, version upgrades, and scaling configuration. At a blended rate, this added an effective cost of ~$2,400/month.
**Cribl Cloud Cost Breakdown:**
Cribl Cloud pricing is primarily based on processed volume (GB) and number of Processing Units (PU). Our usage settled into:
* **Processing Capacity Fee:** $2,400/month for the committed PU pack.
* **Overage Fees:** Minimal (<$50/month) as our commitment aligned well with peak loads.
* **Notable:** No direct costs for compute orchestration, security patches, or high-availability configuration. The operational overhead for our team dropped to near zero for the platform itself.
**Comparative Analysis Table (Monthly Averages):**
| Cost Component | Cribl Cloud | Self-Managed on AWS | Notes |
| :--- | :--- | :--- | :--- |
| **Direct Infrastructure** | $2,450 | $2,375 | Cloud PU fee vs. AWS compute/storage. |
| **Operational Overhead** | ~$0 | ~$2,400 | Engineer time for platform management. |
| **Total Effective Cost** | **~$2,450** | **~$4,775** | |
| **Resilience & Scaling** | Managed, automatic | Manual configuration required | Cloud handled spike to 2.1 TB/day seamlessly. |
| **Feature Lag** | ~2 weeks | Immediate | Self-managed can upgrade on day of release. |
| **Administrative Burden** | Very Low | High | |
**Conclusion and Recommendation:**
For our organization, the financial case for Cribl Cloud became clear when factoring in the fully burdened cost of engineering time. The self-managed deployment, while offering ultimate control, effectively doubled the cost. The break-even point for self-managed would require operational overhead to fall below 4-5 hours per month, which our team found unattainable given the pace of Cribl's release cycle and security requirements.
However, I must note a critical caveat: this analysis holds for standard, cloud-centric deployments. If you require air-gapped environments, specialized hardware, or have existing underutilized Kubernetes capacity, the self-managed equation could shift dramatically.
-- elliot
Data first, decisions later.