How to reduce Snowflake costs without sacrificing performance

Snowflake’s usage-based pricing gives teams flexibility—but without intentional management, it’s easy for costs to spiral. Most organizations overspend not because Snowflake is expensive by default, but because of inefficient patterns in how compute, storage, and data movement are managed.

• Compute typically drives the largest share of spend. Each second a warehouse runs (even idle), it consumes credits.
• Storage costs build up from retained tables, Time Travel history, and unmonitored staging areas.
• Data transfer fees can surprise teams when data moves across clouds, regions, or external integrations.

These factors don’t exist in silos. Poor data modeling or bloated storage can increase query complexity, which drives up compute. A lack of warehouse segmentation can lead to resource contention and slow performance—pushing teams to overprovision as a quick fix.

In this post, we’ll break down practical strategies for reducing Snowflake costs across your stack—from warehouse and storage optimizations to query tuning, architecture decisions, and team practices. These aren’t theoretical tips; they’re based on real customer outcomes and best practices from teams using dbt and Snowflake together to build cost-efficient analytics workflows.

Optimize warehouse configuration and usage

Warehouse optimization is often the fastest—and most overlooked—path to Snowflake savings. By tailoring warehouse size, schedule, and scope to actual workload needs, teams can significantly reduce compute costs without degrading performance.

Match warehouse size to workload

Overprovisioned warehouses are one of the most common sources of overspend. Start by right-sizing based on query volume and complexity.

💡 Tip: Use warehouse monitoring tools or Snowflake's query history to identify peak usage vs. idle time.

Use auto-suspend and auto-resume settings

Idle warehouses still burn credits. Configure auto-suspend after a short inactivity window—typically 2 to 5 minutes—and enable auto-resume to ensure processes don’t get blocked.

Align warehouse schedules with actual usage

Not all workloads run 24/7. Use orchestration tools (like dbt or Airflow) to start and stop warehouses around business hours, especially for regional or time-zoned teams.

Segment by workload type

Sharing a single warehouse across dev, reporting, and data science creates performance bottlenecks—and wastes compute. Create purpose-specific warehouses to optimize sizing and concurrency for each use case.

Optimize data storage

Snowflake makes it easy to store massive volumes of data—but without a retention and organization strategy, storage costs can quietly compound. While storage is typically cheaper than compute, poor data hygiene can indirectly increase compute costs by slowing queries or bloating transformations.

Here’s how to make storage work harder (and cheaper) for your team:

Tier data based on value

Not all data needs to stay in Snowflake forever. By adopting a tiered storage model, teams can preserve what matters and archive the rest.

Consider moving cold data to object storage or a data lake, while retaining hot data—like metrics and model outputs—in Snowflake for speed.

Tune Time Travel for the real world

Snowflake’s Time Travel is powerful for recovery and auditing—but the default 90-day window might be overkill. Shortening retention on less critical tables can yield quick wins.

💡 Tip: Set different defaults at the schema level to reflect each domain’s needs.

Organize tables for performance and compression

Storage and compute are connected. Better-organized data compresses more efficiently and supports faster queries—reducing both costs.

Using clustering keys that align with your most common filter conditions pays dividends across your stack.

Automate cleanup of temp and dev data

Staging tables, temp models, and old test data often go unnoticed—but Snowflake charges for it all. Put automated cleanup jobs in place to track and remove these regularly.

Tools like dbt’s run-operation can help you automate cleanup scripts directly within your project.

Improve query and code efficiency

Poorly written queries are among the fastest ways to inflate Snowflake costs. By improving how data is transformed and accessed, organizations can significantly reduce compute usage—without compromising performance or insights.

Start with better SQL hygiene

A few targeted improvements can make a big dent in warehouse usage:

• Filter early and often – Narrow scans with WHERE clauses before joining.
• Avoid Cartesian joins – Explicit join conditions prevent runaway costs.
• Use SELECT with intention – Only pull what’s needed; skip SELECT *
• Summarize when possible – Aggregated views are faster and cheaper than raw detail.

Modularize transformations with dbt

Large, monolithic SQL scripts are fragile and hard to optimize. With dbt, you can break down complex pipelines into modular, testable models—each designed for a single purpose.

Need to go faster? Use incremental models to transform only new or changed records, reducing warehouse load.

Test early to reduce reprocessing

Silent errors in transformations can lead to expensive re-runs. Adding automated testing catches issues before they escalate.

Start with these built-in tests:

• not_null
• unique
• relationships
• accepted_values

Then expand into custom assertions as your models mature.

Materialize and cache repeat logic

If a query (or subquery) is run frequently, materialize the results. Use table, incremental, or ephemeral models depending on how often the data changes.

This is especially valuable when paired with a semantic layer—one version of the logic, reused across your stack.

Make architecture work for your budget

You can optimize individual queries and warehouses all day—but unless your architecture supports those efforts, costs will keep creeping up. Structural choices like how you route workloads, manage scaling, and enforce limits play a critical role in long-term Snowflake spend.

Balance performance with dynamic scaling

For spiky workloads (think: end-of-day reports or campaign launches), consider multi-cluster warehouses with auto-scaling. This lets you serve concurrent queries efficiently—without paying for idle capacity the rest of the day.

Enforce guardrails with resource monitors

No team wants to be surprised by an end-of-month bill. Snowflake’s resource monitors let you:

• Set spend thresholds (e.g., alert at 80%, suspend at 95%)
• Automate alerts and take corrective action early
• Keep non-critical workloads from consuming critical capacity

Route workloads intentionally

Different query types deserve different environments. Splitting out BI dashboards, ad hoc exploration, and data science into dedicated warehouses:

• Minimizes contention
• Optimizes performance per workload
• Makes cost attribution and forecasting easier

Use Snowflake's built-in accelerators (selectively)

Snowflake offers features like search optimization and automatic clustering to accelerate query performance. These can increase storage costs but often pay off when used for the right tables.

Consider enabling these on:

• High-volume tables with frequent filters (e.g., WHERE user_id =)
• Slowly changing dimension tables
• Models powering operational dashboards

Build a culture of cost ownership

You can optimize compute and storage—but sustainable Snowflake cost management comes down to people. The most successful teams treat cost awareness as a shared responsibility across engineering, analytics, and business stakeholders.

Make costs visible—and attributable

Visibility drives accountability. Use Snowflake’s tagging and query history features to track usage by team, project, or environment. Then surface that data in internal dashboards to enable cost ownership.

Foster a culture of shared learning

Documentation and education go further than mandates. Host recurring “cost review” sessions where teams share wins, inefficiencies, and lessons learned. Encourage experimentation with techniques like:

• Warehouse resizing
• Query optimization
• Staging table cleanup

Make optimization continuous

Treat cost reviews the way you treat product retros. A retail data team holds quarterly usage audits to:

• Flag cost regressions
• Revisit underperforming models
• Reallocate resources as business needs evolve

This process helped them maintain stable Snowflake costs even as data volume tripled over two years.

Focus on cost-to-value, not just cutting spend

Cost optimization isn’t always about spending less—it’s about spending smarter. Use metrics like:

• Cost per insight delivered
• Query cost per stakeholder group
• Revenue influenced by data products

This shifts the focus from blanket cuts to intelligent trade-offs.

Cut costs, not capabilities, with Snowflake and dbt

Managing Snowflake spend isn’t about sacrificing performance or putting limits on innovation. It’s about building smarter systems that scale efficiently—and that’s where dbt comes in.

Organizations that pair Snowflake’s scalable compute with dbt’s modular, testable transformations unlock a powerful model for cost control:

• Fewer redundant queries. dbt models are reusable and version-controlled, so teams write once and use everywhere—reducing warehouse load and developer rework.
• More efficient pipelines. Incremental models and optimized materializations ensure Snowflake only processes what’s new, cutting compute costs over time.
• Cleaner, more trustworthy data. Built-in tests and documentation stop bad data from flowing downstream, preventing costly reprocessing and boosting confidence in outputs.
• Greater visibility. Column-level lineage, semantic consistency, and environment-aware execution help teams understand—and justify—how resources are used.

The result? Teams can scale analytics workloads and meet SLAs without running up costs behind the scenes.

But the biggest differentiator is discipline: organizations that treat data like code—with CI/CD, testing, and review—are the ones who control their spend while accelerating delivery.

With dbt and Snowflake working in tandem, cost efficiency becomes part of your architecture—not an afterthought. That’s how you deliver reliable, business-ready insights without overspending.

Snowflake Cost Optimization FAQs