📌 BigQuery Pricing Models
Google BigQuery separates costs into storage and compute. The two compute pricing models for running queries are:
On-Demand Pricing – pay per amount of data processed
Flat-Rate Pricing – pay a fixed cost for dedicated query capacity (slots)
Google Cloud
🧾 1. On-Demand Pricing (Usage-Based)
✔️ How It Works
You pay only for the queries you run.
Charges are based on the number of bytes processed by each query.
The first 1 TiB of data processed per month is free in the standard tier.
Query processing capacity is shared across users and can burst to ~2,000 slots for better performance.
Google Cloud
✔️ Pros
Cost-efficient for low or unpredictable usage — you only pay when you run queries.
Good for ad-hoc analysis or small teams just starting with BigQuery.
No long-term commitments or upfront costs.
❗ Cons
Costs can vary from month to month depending on data scanned.
For very large workloads, total cost may become high.
Complex queries scanning lots of data can become expensive.
Google Cloud
+1
📊 2. Flat-Rate Pricing (Capacity-Based)
🔹 How It Works
You purchase dedicated processing capacity, measured as slots (virtual CPUs).
You pay a fixed fee for a committed number of slots each month or year.
All queries use this reserved capacity, and you don’t pay per byte scanned.
Google Cloud
✔️ Pros
Predictable costs: you know your monthly spend ahead of time.
Useful for high and steady workloads — cost doesn’t rise with more data scanned.
Good for organizations with heavy, frequent querying where on-demand bills are high.
Google Cloud
❗ Cons
Requires upfront commitment and minimum capacity purchase.
You may overpay if workload is low (unused reserved slots).
If your queries exceed available slots, execution may queue and run slower.
Google Cloud
⚖️ When to Choose Each
Scenario Best Pricing Model
Small team or startup with occasional queries On-Demand
Projects with unpredictable usage patterns On-Demand
Large organization with steady, heavy queries & dashboards Flat-Rate
Need predictable budgeting for analytics compute Flat-Rate
💡 Key Differences Summarized
💰 Cost Basis
On-Demand — Pay per bytes processed.
Flat-Rate — Pay for slots capacity (fixed monthly/annual cost).
Google Cloud
📈 Flexibility
On-Demand is more flexible and scalable for varying workloads.
Flat-Rate is more stable and predictable, but needs careful planning to avoid waste.
Economize Cloud
📊 Performance & Scaling
On-Demand uses pooled resources and can burst for faster small query performance.
Flat-Rate gives dedicated capacity but may queue queries if over capacity.
Google Cloud
🧠 Final Thought
On-Demand is great if you want no commitment and pay-as-you-go simplicity.
Flat-Rate is better if you have large predictable workloads and want budget certainty.
Many organizations start with on-demand pricing and switch to flat-rate once usage and costs justify it.
Learn GCP Training in Hyderabad
Read More
Building a Cost Explorer Dashboard with Looker Studio and BigQuery
Automating Budget Alerts and Cost Control in GCP
How to Use Cost Tables in BigQuery for FinOps
Cost Optimization & Billing in GCP
Visit Our Quality Thought Training Institute in Hyderabad
Subscribe by Email
Follow Updates Articles from This Blog via Email
No Comments