Total Cost of Ownership (TCO) for a data platform includes every cost associated with storing, processing, governing, and operating the platform over a multi-year horizon, not just the software licensing fee or the cloud service bill for a single month. Comparing a proprietary cloud data warehouse to an open Data Lakehouse on a TCO basis frequently produces a different conclusion than comparing only the headline compute prices, because the architectures have very different cost structures across the different cost categories.
Storage Costs
Proprietary cloud warehouses store data in their own internal formats, typically charging a blended per-terabyte-per-month rate that covers both storage and a base level of maintenance. Snowflake charges for compressed on-disk storage. BigQuery charges per terabyte of data stored in its internal format. These rates are generally higher per raw byte than cloud object storage, but the comparison is not direct because the warehouse handles compression and automatic optimization internally.
An open lakehouse stores data as Parquet files in S3, ADLS, or GCS at object storage pricing rates (approximately $0.023/GB/month for S3 Standard). The organization is responsible for compression (Parquet typically achieves 3-10x compression over raw CSV), file organization, and compaction maintenance. When these are done well, the storage cost advantage of the open lakehouse is substantial for large data volumes.
Compute Costs
Proprietary warehouses charge for compute in proprietary units (Snowflake credits, BigQuery slots). The relationship between these units and underlying cloud compute resources is opaque by design. Open lakehouse compute is priced directly in cloud VM terms (EC2 instances, Azure VMs), making costs transparent and enabling the use of spot or preemptible instances that are 60-90% cheaper than on-demand pricing for tolerant workloads.
Dremio Cloud's autoscaling engines that scale to zero during idle periods can dramatically reduce compute TCO compared to a warehouse cluster that idles at minimum capacity 16 hours a day. For workloads with variable demand patterns, this can represent a 50-70% reduction in effective compute spend.
Operational and Hidden Costs
The TCO categories that are most often missed in initial comparisons:
- Data egress: Proprietary warehouses charge data egress fees when data is exported. At scale, these fees can be significant. Open lakehouse data in S3 is accessible to any engine without egress within the same region.
- Seat licensing: Some warehouse vendors charge per-analyst seat fees for BI tool access. Open lakehouse query access through standard JDBC/ODBC drivers does not carry seat-based fees.
- Vendor lock-in premium: Organizations locked into a single vendor's ecosystem lose negotiating leverage over time. The open lakehouse preserves the ability to run multiple query engines and switch vendors for compute without migrating data.
- Iceberg maintenance overhead: The open lakehouse does require engineering time for table maintenance (compaction, snapshot expiration) that proprietary warehouses handle automatically. This operational cost should be included honestly in any TCO comparison.
The Realistic Verdict
For organizations with data volumes above 10 TB and significant analytical workloads, the open lakehouse typically achieves 40-70% lower TCO than an equivalent proprietary warehouse over a 3-year horizon, primarily driven by lower storage costs, transparent compute pricing, and the absence of egress and seat fees. Below 10 TB, the operational simplicity of a managed proprietary warehouse may offset the cost savings, making a pure proprietary solution a reasonable choice for smaller organizations.
