SELECT Announces Automated BigQuery Cost Optimization Early Access Program
Solution will combine a proven data platform visibility and automation engine with DoiT's market-leading depth of BigQuery customer experience.
The Cost Visibility Gap in Cloud Data Platforms
As organizations deepen their investment in Google Cloud, BigQuery has become central infrastructure for analytics, machine learning and AI workloads, introducing a cost management challenge most teams are only beginning to confront. BigQuery's pricing model spans on-demand queries, slot-based capacity reservations and storage billing distinctions that compound over time. Optimizing across all of these simultaneously, while also identifying wasted spend from failed or inefficient queries, is a sustained operational commitment that most engineering and FinOps teams aren't resourced to maintain.
"Across thousands of BigQuery customer interactions, we've seen the level of expertise required to understand and optimize spend," said
A Foundation Built on Experience
SELECT's BigQuery solution is built on two complementary capabilities: SELECT's automation engine, refined through production deployments across more than
"SELECT's automation engine has been proven in production across hundreds of Snowflake customers and will be scaled to BigQuery in combination with DoiT's remarkable BigQuery expertise," said
How the Solution Works
SELECT's BigQuery solution integrates across the data stack to end-to-end costs, then optimizes across three automated layers:
- Storage Optimizer automatically converts tables from logical to physical storage billing where the transition yields savings with no changes to queries or pipelines.
- Reservations Manager continuously right-sizes slot reservation capacity based on actual workload demand, eliminating manual analysis over time.
- Query Router routes individual queries to the most cost-effective billing path based on historical cost data, dynamically optimizing between on-demand and reservation-based execution, with automatic fallback to protect performance.
The tool complements DoiT Cloud Intelligence™, connecting BigQuery cost attribution, anomaly detection and optimization within the same platform used to manage spend across cloud infrastructure, Kubernetes, and Snowflake.
Early Access & Availability
Early access is now available at select.dev/signup. Teams attending Google Cloud Next 2026 in
BigQuery is the third pillar of SELECT by DoiT's data platform optimization roadmap, following Snowflake (available today) and Databricks (coming June 2026).
About SELECT by DoiT
SELECT by DoiT is a leading platform optimization solution, purpose-built to help engineering and data teams control and reduce cloud data platform spend. The platform delivers deep visibility into cost and performance across Snowflake, Databricks and BigQuery, and takes continuous, automated actions to reduce cost without compromising performance. SELECT's automation engine has been proven in production across more than
About DoiT
DoiT is a global leader with its DoiT Cloud Intelligence™ platform, providing intent-aware FinOps and CloudOps solutions that help businesses maximize the impact of their cloud investments. With deep expertise in AWS, Google Cloud and Azure, DoiT Cloud Intelligence empowers organizations to connect every dollar spent to the goals of each workload, uncover the root causes of inefficiency, and drive real optimization through automation and action. To learn more, visit doit.com.
Contact:
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SOURCE SELECT by DoiT
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