The Data Warehouse TCO: A Guide to the True Costs of Snowflake, BigQuery, and Redshift
10 min readBY
Your dashboards are fast, your data is centralized, and your team is finally making data-driven decisions. Then, a five-figure surprise bill arrives from your cloud data warehouse.
This is a common story. Teams choose one of the three leading cloud data warehouses (Snowflake, Google BigQuery, or Amazon Redshift) for their promise of consumption-based pricing and massive scalability. The model seems simple: pay only for what you use. However, the reality is that advertised rates for compute and storage are just one part of a much larger cost structure.
A simple comparison of list prices is misleading because each of these platforms has a unique architecture and a distinct economic model. Your true Total Cost of Ownership (TCO) is determined by how well your specific workloads align with the architectural trade-offs inherent in each system. To budget accurately, you must understand these underlying models:
- Snowflake's TCO is driven by its credit-based model, where you pay for compute time consumed by virtual warehouses.
- BigQuery's TCO is most commonly driven by its on-demand model, where you pay for the terabytes of data your queries scan.
- Redshift's TCO is often rooted in its provisioned cluster model, where you pay per hour for a fixed set of resources, frequently with significant long-term discounts.
This guide provides a framework to calculate your true TCO by examining three key cost drivers across these platforms: nuanced compute billing models, standard cloud egress fees, and the necessary investment in personnel and administrative overhead.
| Cost Driver | Snowflake | Google BigQuery (On-Demand) | Amazon Redshift |
|---|---|---|---|
| Primary Compute Model | Credit-based (per-second after 60s min) | Scan-based ($/TB scanned) | Provisioned Cluster (per-hour) or Serverless (per-second after 60s min) |
| Key Financial Risk | "Death by a thousand cuts" from small queries with 60s minimum billing | A single "runaway query" scanning a large table can cost thousands | Paying for idle provisioned capacity or the 60s minimum on serverless |
| Admin Overhead | Medium: Focus on warehouse sizing, cost monitoring, and permissions | Low: Focus on query discipline and cost-control quotas | High (Provisioned): Requires deep expertise in cluster tuning, distribution keys, and VACUUM |
| Best Fit Workload | Predictable, long-running batch jobs; less ideal for high-concurrency, short queries | Ad-hoc exploratory analysis where infrastructure management is not desired | Stable, well-understood workloads where Reserved Instances can provide discounts |
Compute: The #1 Source of Unpredictable Data Warehouse Costs
The largest variable in your monthly bill is compute, and each platform bills for it differently. Understanding these models is the key to avoiding unexpected costs, whether from idle-but-billed resources or from a single, poorly written query.
The Cost of "Warm" Compute: Minimum Billing Increments
Both Snowflake and Amazon Redshift Serverless employ a 60-second minimum billing window for compute activation. For Snowflake, this occurs when a virtual warehouse resumes from a suspended state. For Redshift Serverless, it is when a workgroup becomes active. While billing becomes per-second after that minute, the initial 60-second charge is unavoidable for each activation.
This has a significant financial impact on common startup workloads like interactive business intelligence.
Consider a realistic scenario: Your Head of Sales loads a Tableau dashboard that runs 10 separate, highly optimized queries to render its visuals. Each query takes only 3 seconds to complete.
Let's do the math:
- Actual Compute Time Used: 10 queries x 3 seconds/query = 30 seconds
- Billed Compute Time (Snowflake/Redshift Serverless): 10 queries x 60-second minimum charge/query = 600 seconds, or 10 minutes
In this scenario, you paid for 20 times the compute you actually used.
The Architect's View: This 60-second minimum is not arbitrary. It represents a trade-off to keep compute resources "warm" and instantly available, avoiding the latency of a "cold start" that could take much longer. The cost is in service of a better, more responsive interactive user experience.
However, this cost can be actively managed. Best practices include implementing aggressive auto-suspend policies (e.g., 60 seconds) and using BI tools that can batch dashboard queries into a single request. For Snowflake, Resource Monitors can also be configured to automatically suspend warehouses when credit consumption exceeds a set budget, providing a financial safeguard.
BigQuery TCO and the "Runaway Query" Risk
BigQuery's on-demand pricing model presents a different kind of financial risk: the "scan-based pricing trap." In this model, you pay for the amount of data a query scans, typically around $6.25 per terabyte. This is incredibly powerful for ad-hoc analysis, but it can lead to large, unexpected bills from a single inefficient query.
For example, a new analyst running a SELECT * query on a multi-terabyte table without a WHERE clause can accidentally generate a bill for thousands of dollars in a matter of seconds.
The Architect's View: This model's trade-off is one of ultimate simplicity for power. It completely abstracts away all infrastructure management. There are no clusters to size or tune. The responsibility for cost control, therefore, shifts from infrastructure governance to query discipline.
This risk is highly manageable. Costs can be controlled by enforcing query best practices, such as always using a WHERE clause and leveraging BigQuery's partitioning and clustering features to reduce the amount of data scanned. Critically, organizations can implement hard financial guardrails by setting project-level and user-level quotas that cap the amount of data that can be processed daily.
The Cost of Data Movement: Standard Cloud Egress Fees
The second major cost driver is for moving data out of your cloud provider's network. These standard cloud egress fees are a reality of every major cloud platform. While loading data into the warehouse is almost always free, taking it out incurs costs that typically range from $90 to over $150 per terabyte.
For a growing company, this is not an edge case. It is a direct cost on everyday workflows:
- Multi-Cloud Tooling: If your BI tool is hosted in a different cloud or region than your data warehouse, every query pulling data to a dashboard is subject to egress fees.
- Data Science and Machine Learning: When a data scientist pulls a 100 GB dataset to their local machine to train a model, you could pay a $9 to $15 "data export" cost.
- Embedded Analytics: In a customer-facing application, every piece of data you send from your warehouse to your users' browsers can incur egress fees. A successful product with high user engagement can directly translate to a higher data warehouse bill.
Network egress is a fundamental component of cloud pricing that encourages keeping your data stack within a single vendor's ecosystem to minimize costs. This is an important architectural consideration, as it creates a financial incentive against the flexibility of using best-of-breed tools across different cloud providers.
The Cost of Human Capital: Personnel and Administrative Overhead
The cost that never appears on your monthly invoice is the fully-burdened salary of the data engineers required to manage, tune, and govern the warehouse. This personnel and administrative overhead is a necessary investment, and the amount required varies significantly based on the platform's architectural complexity.
Framing this as a hard cost is essential. If a data engineer with a fully-loaded salary of $150,000 per year spends just four hours a week on warehouse administration, you are paying $15,000 annually in personnel costs just for platform maintenance.
This "administration" involves different tasks on each platform:
- Amazon Redshift traditionally requires the most hands-on management. Engineers must become experts in physical data layout, selecting distribution keys and sort keys, and performing manual maintenance like
VACUUMandANALYZEto prevent performance degradation. Predictable workloads can see compute costs cut by up to 75% with Reserved Instances, but this requires upfront capacity planning and management. - Snowflake shifts the focus from physical tuning to logical governance. The primary administrative tasks involve "right-sizing" virtual warehouses for different workloads, managing a complex hierarchy of roles and permissions, and actively monitoring consumption to control costs.
- Google BigQuery, as a fully serverless platform, demands the least infrastructure administration. The focus is almost entirely on logical governance: managing IAM permissions, monitoring query patterns for inefficiency, and setting cost-control quotas.
This engineering time is your most valuable resource. The administrative burden of your chosen platform is a direct factor in your TCO and a tax on your team's ability to deliver new data products and business insights.
How to Calculate Your Data Warehouse TCO: A 4-Part Checklist
To avoid surprise bills, you need a financial model that accounts for these architectural nuances. Use this checklist to calculate a more realistic TCO:
1. Calculate Base Costs: What is your estimated monthly bill for compute credits or provisioned nodes and storage?
2. Model Architectural Costs: Based on your workload (e.g., spiky BI vs. steady ETL), what is the likely cost from minimum billing increments or accidental large scans? (e.g., (Avg. BI Sessions/Day * 60 seconds) * Cost/Second)
3. Estimate Egress Fees: How much data will you move to other clouds, regions, or local machines monthly? (GBs moved * $/GB Egress Rate)
4. Quantify Personnel Overhead: How many hours per week will your team spend on platform management? ((Engineer Salary / 2080 hours) * Hours/Week * 52)
Let's apply this to a hypothetical "SaaSCo," a 100-person company with a high-concurrency BI workload powered by 10 active users.
- Workload: Frequent, short-running queries on 500 GB of data.
- Personnel: One data engineer ($150k/yr) spends ~4 hours/week on management.
- Base Bill: Their estimated monthly bill for compute and storage is $2,000.
Calculating SaaSCo's True TCO
Scenario A: A Platform with Minimum Billing Increments (e.g., Snowflake)
- Direct Bill: $2,000
- Architectural Cost (Minimum Billing): The thousands of small BI queries incur the 60-second minimum charge, adding $1,500/month in billed-but-unused compute time.
- Egress Fees: Negligible for this internal workload, assume $20/month.
- Personnel Overhead: The engineer's time costs $1,250/month.
- Total True TCO: $2,000 + $1,500 + $20 + $1,250 = $4,770 per month
Scenario B: A Platform with Scan-Based Pricing (e.g., BigQuery On-Demand)
- Direct Bill: $2,000
- Architectural Cost (Scan Risk): The BI queries are efficient, but one unoptimized ad-hoc query from an analyst scans several terabytes, adding a one-time $500 charge for the month.
- Egress Fees: Negligible, $20/month.
- Personnel Overhead: Lower admin burden reduces this cost to $800/month.
- Total True TCO: $2,000 + $500 + $20 + $800 = $3,320 per month
SaaSCo's true cost can be more than double their initial budget, and the primary driver of that overage depends entirely on the platform's architecture.
Conclusion: Aligning Architecture with Your Workload
The most expensive part of a cloud data warehouse is not the data, it is the architecture. The leading platforms were designed for a different era, prioritizing massive scale and centralization for large enterprises. For startups and lean teams, this approach can introduce unnecessary complexity and overhead, forcing engineers to spend valuable time managing costs and tuning infrastructure rather than building products.
A modern, lean architectural approach eliminates these hidden costs by design. By embracing principles like true per-second serverless execution with no billing minimums and zero infrastructure administration, it is possible to achieve cost predictability. This new model is ideal for smaller, agile teams that need to iterate quickly without accumulating financial or performance debt. While this approach comes with its own set of trade-offs, it is fundamentally designed to align with the goals of a lean business.
See how a lean architecture provides predictable, transparent pricing. Read our Modern Data Warehouse Playbook to learn more.
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FAQS
What does data warehouse TCO mean?
TCO stands for Total Cost of Ownership. It represents the complete cost of using a data warehouse, including the direct costs for compute and storage, plus all indirect or "hidden" costs. These hidden costs include architectural inefficiencies (like paying for unused compute time), network egress fees for moving data, and the cost of engineering salaries for platform administration and governance.
Why is my Snowflake bill so unpredictable?
Snowflake's costs can be unpredictable due to its credit-based model combined with a 60-second minimum charge when a virtual warehouse "wakes up." For workloads with many short, frequent queries (common with BI dashboards), you may be billed for 60 seconds of compute for a query that only takes 3 seconds, causing costs to inflate by 20x or more.
What is the biggest financial risk with BigQuery?
With BigQuery's on-demand pricing, the biggest risk is the "runaway query." Because you pay for the amount of data a query scans (e.g., ~$6.25/TB), a single inefficient query without proper filters (WHERE clause) on a large table can accidentally generate a bill for thousands of dollars in a matter of seconds.
How can I reduce my cloud data warehouse costs?
You can reduce costs by enforcing query best practices, setting firm budget alerts and quotas (like Snowflake's Resource Monitors or BigQuery's user-level quotas), and co-locating your BI tools and data warehouse in the same cloud region to minimize egress fees. Most importantly, choose an architecture (like a lean data stack) that is designed to minimize waste and administrative overhead from the start.
