The recent pricing update for SentinelOne's Singularity Data Lake warrants analysis beyond typical vendor announcements. As someone who benchmarks data ingestion and query performance across security platforms, I see this as a pivotal shift in their consumption model.
The move to a more granular per-GB ingestion pricing, with separate costs for hot and cold storage tiers, appears to align with industry trends (e.g., Snowflake, Elastic). However, the stated rates become problematic at enterprise-scale telemetry volumes. Consider a mid-sized deployment ingesting 5 TB/day:
- **Hot storage (30 days):** ~150 TB at [published rate]/GB
- **Cold storage (year+ retention):** Multiplied significantly for compliance
The cumulative monthly cost can easily surpass six figures, which is a substantial increase over previous bundled SKUs. This creates a direct cost/benefit tension with the platform's core value proposition: unlimited data exploration for threat hunting.
My primary concern is predictability. Unlike compute benchmarks with clear inputs/outputs, data lake costs are driven by unpredictable incident response cycles, where a major investigation might query petabytes of historical data. The pricing model seems to penalize active use of the very data you've already paid to ingest.
For teams evaluating this, I recommend running a detailed workload simulation:
1. Export one month of your current EDR/XDDR telemetry volume.
2. Model the projected ingestion costs, assuming 20% year-over-year growth.
3. Simulate "burst" query scenarios (e.g., enterprise-wide IOC search) against 12 months of cold data.
This will give you a performance-per-dollar metric specific to your operations. Has anyone performed a similar TCO comparison post-announcement, particularly against building a custom data lake on cloud infrastructure? The lock-in factor versus raw cost is a critical variable.
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