Your BigQuery Scans Cost $5 Per TB of Noise
BigQuery's on-demand pricing charges $5 per TB scanned. When tables are full of noise and duplicates, every query is more expensive than it needs to be. Expanso filters upstream - BigQuery only scans data that matters.
Why GCP Costs Grow Unchecked
BigQuery, Cloud Logging, and Cloud Storage all charge by volume. Unfiltered data drives costs up across every service.
BigQuery scans everything
$5/TB On-Demand Pricing
BigQuery charges per TB scanned for on-demand queries. When tables contain noise and duplicates, every analyst query costs more. Slot-based pricing doesn't help if your data is bloated.
Cloud Logging adds up fast
$0.50/GB After Free Tier
Cloud Logging charges $0.50/GB for ingestion after the free 50GB. Verbose GKE logs, Cloud Run traces, and application debug output can generate hundreds of GB daily.
Egress fees punish data movement
$0.12/GB Internet Egress
Moving data out of GCP or between regions costs $0.08-0.12/GB. Multi-region architectures and hybrid deployments generate significant monthly egress bills.
Filter Before GCP Meters Tick
Expanso processes data on GCE instances or GKE clusters before it reaches BigQuery, Cloud Logging, Cloud Storage, or Pub/Sub. Less data in means lower costs across every GCP service.
How Expanso Cuts GCP Costs
Reduce spend across BigQuery, Cloud Logging, and data transfer
BigQuery scan reduction
Smaller Tables, Cheaper Queries
Filter noise and duplicates before data lands in BigQuery. Smaller tables mean every query scans less data - reducing per-query costs for on-demand pricing and slot utilization for flat-rate.
Cloud Logging volume control
Ingest Signals, Not Noise
Classify GKE, Cloud Run, and application logs at the source. Forward errors and security events, aggregate routine logs, drop debug noise before it reaches Cloud Logging.
Pub/Sub message optimization
Fewer Messages, Lower Costs
Filter event streams before they reach Pub/Sub. Reduce message volume, lower throughput costs, and decrease downstream Cloud Functions or Dataflow invocations.
Cloud Storage optimization
Store Clean Data Only
Filter and compress data before it lands in Cloud Storage buckets. Reduce storage volume, accelerate downstream BigQuery loads, and lower Nearline/Coldline migration overhead.
Cross-region transfer reduction
Process Locally, Transfer Less
Filter data at the source region before cross-region transfers. Reduce inter-region networking costs for multi-region deployments and disaster recovery.
Dataflow cost reduction
Smaller Pipelines, Less Compute
When Dataflow processes pre-filtered data, pipelines run on fewer workers, finish faster, and consume less compute. Streaming pipelines scale down with cleaner input.
Proven GCP Cost Reductions
Results from upstream data optimization applied to Google Cloud workloads
Cost reduction for enterprise data warehouse with distributed data sources
Data movement reduction by processing queries where data lives
Query performance improvement with cleaner, localized data
Log volume reduction applicable to Cloud Logging ingestion
Real-World Impact
See how organizations cut GCP costs with upstream data control
Enterprise DW: 58% Cost Reduction
A Fortune 500 retail chain centralized 3.5 PB of store data in their cloud data warehouse. By processing queries where data lives and filtering before cloud ingestion, they cut costs 58%. The same approach applies directly to BigQuery workloads.
O-RAN: 47% Observability Savings
A European telecom operator generated massive network telemetry across 12,000 cell sites. Expanso filtered and aggregated telemetry at the edge before centralized analytics. The same upstream filtering reduces Cloud Logging and BigQuery costs for GCP deployments.
Why Expanso for GCP
Runs on GCP infrastructure
Deploy on GCE instances, GKE clusters, or Anthos. Expanso runs where your data originates within Google Cloud.
Works with GCP services
Integrates upstream of BigQuery, Cloud Logging, Pub/Sub, Cloud Storage, and Dataflow. No service changes required.
Multi-cloud capable
If you run hybrid or multi-cloud with AWS or Azure, Expanso applies consistent filtering everywhere. No GCP lock-in.
Free tier to start
Process up to 1TB/day free. Test on your highest-cost BigQuery tables or Cloud Logging sources and measure real savings.
Optimize Costs Across Your Stack
See how Expanso reduces costs for other platforms
Frequently Asked Questions
How does Expanso deploy on GCP?
Expanso runs on GCE instances, GKE pods, or Anthos-managed infrastructure. It processes data at the source before it reaches BigQuery, Cloud Logging, Pub/Sub, or Cloud Storage. Standard GCP deployment - no marketplace dependencies required.
Will this affect BigQuery queries and dashboards?
No. BigQuery tables receive the same data format - just without noise and duplicates. Existing queries, views, and Looker dashboards continue working. Query performance improves because tables are smaller and cleaner.
How does this compare to BigQuery cost controls?
BigQuery offers reservation slots, BI Engine, and materialized views for cost control. These optimize how BigQuery processes data. Expanso reduces the data volume BigQuery needs to process. Use both: BigQuery tools for query optimization, Expanso for data volume optimization.
Can we target specific BigQuery datasets?
Yes. Configure Expanso to filter data feeding specific datasets or tables. Start with your highest-volume or most-queried tables to see the fastest ROI, then expand to other pipelines.
Does this work with BigQuery streaming inserts?
Yes. Expanso filters data before it reaches BigQuery's streaming API. Fewer rows streamed means lower streaming insert costs and reduced slot consumption for real-time analytics.
BigQuery scans costing $5/TB of junk?
Every duplicate record and noise log inflates your tables, slows your queries, and burns your budget. Filter upstream and pay for signal, not noise.