Businesses of all sizes and natures strive for financial efficiency—meticulously tracking expenses and plugging obvious leaks. However, the cloud data world is vast and dynamic, making the possibility of insidious, unseen leaks inevitable. Resources consume valuable credits silently and are often only noticed when the monthly bill arrives.
You’ve embraced Snowflake, trusting its architecture to power your insights and simplify your data operations. But the very elasticity that makes it so powerful can also induce surprising costs if not carefully managed.
By the end of 2025, a staggering $44.5 billion in cloud infrastructure spend is projected to be wasted globally due to underutilized resources. But you don’t have to contribute to it.
Here’s the ultimate guide to Snowflake cost optimization to help you identify and seal those leaks, ensuring your data platform delivers maximum value.
Let’s begin!
Laying the Foundation for Snowflake Cost Optimization: Pricing Explained
Snowflake database follows the pay-as-you-go principle, stating you only have to pay for the compute or the storage you use. Currently, Snowflake offers multiple editions, each serving a specific purpose.
Snowflake editions follow a specific hierarchy:
Standard 🡪 Enterprise 🡪 Business Critical 🡪 Virtual Private Snowflake
In simpler terms, higher edition = higher price per credit = better features.
Snowflake pricing also depends on the given factors:
- Platform: You can choose leading cloud platforms like AWS, Google Cloud, or Microsoft Azure.
- Region: US-East, US-West, Canada, etc.
For instance, the price upon choosing AWS and for the US East (Ohio) region would be:
- Standard Edition: $2 cost/credit
- Enterprise Edition: $3 cost/credit
- Business Critical Edition: $4 cost/credit
- Virtual Private Snowflake: For this, Snowflake has to be contacted.
- On-demand Storage: $40/TB/month
- Capacity Storage: $23/TB/month
Snowflake’s decoupled architecture comprises three layers, namely storage, compute, and services. The pricing model is also based on your actual usage of these layers within the Snowflake platform.
What is Snowflake Credit?
Snowflake credit is a unit of measure. You can use them to pay for the consumption of the resources within the platform. These are consumed only when resources are used.
For instance, your Snowflake credits will be consumed if the virtual warehouse and serverless features (including Snowpipe and materialized views) are running.
Snowflake Compute Pricing
Snowflake’s compute is the virtual warehouse where you run workloads. You can choose from a variety of virtual warehouses (VWHs) depending on the kind of workload you run. The size of the virtual warehouse also determines how fast a query runs.
When the virtual warehouse is not running or is in a suspended state, it does not consume any credits.
Snowflake’s credits have fixed rates and are billed by the second, with a minimum time of one minute.
Here’s a basic formula to evaluate the compute cost:
| $ (Cost) = No. of clusters (applicable if multi-cluster configuration, else consider 1) x Number of nodes (determined by Warehouse Size) x Warehouse running time (in hours) x $ value per credit (based on region and cloud provider) |
Note these factors here:
- 60 seconds is the minimum billing for all VWHs, irrespective of size & type.
- Beyond the 60 seconds, the billing is calculated on a per-second basis.
- Resizing the warehouse incurs additional costs every time.
Snowflake Cloud Services Pricing & Calculation
Cloud services are resources within the platform. Snowflake automatically assigns them according to your specific workload requirements. But understanding your Snowflake bill, especially in the case of the Cloud Services layer, can feel like a complex puzzle.
Beyond compute and storage, these essential services orchestrate everything and directly impact your overall spend. Let’s break down the mechanics behind Snowflake’s Cloud Services pricing and calculations, but first, let’s understand its key tasks.
The Cloud Service layer primarily undertakes the following tasks:
- Authentication
- Security and Governance
- Metadata management
- Transaction management
- Query caching
- Query compilation and optimization
Managing these resources yourself is a hefty task and best done with the aid of a reliable Snowflake development services partner.
Snowflake also offers discounts up to 10% on daily compute credits. Let’s understand it with the following formula:
Credits billed = Actual Compute credits + Actual Cloud Service credits – (10% of daily compute credits)
For instance, if your compute credits per day are 100, and your cloud service cost is 10, then you do not have to pay anything for using the cloud service. Your 10% discount on the actual compute, which in this case is 100, will nullify the amount.
10% of 100 (actual compute credits) = 10 credits (this would be discounted)
Snowflake Managed Service Features
Snowflake’s comprehensive suite of managed services is one of its biggest appeals. It holds the power to abstract away infrastructure complexities and boost your operational efficiency.
But are you fully leveraging these snowflake features to their maximum potential? Its powerful capabilities simplify your data operations while optimizing performance and cost. You do not have control over defining or allocating resources that Snowflake manages.
Let’s explore how Snowflake’s managed features empower your data team.
• Snowpipe and Snowpipe Streaming
It is an automated service that rapidly ingests streaming data without a virtual warehouse.
• Database Replication
It replicates the data across the cloud and regions with standard storage and data transfer costs.
• Materialized View
Automatically syncs materialized views with underlying base tables without using the virtual warehouse.
• Automatic Clustering
Applies to tables and materialized views to maintain the optimal clustering state of the table by using defined cluster keys.
• Search Optimization Service
Billed per second, it uses Snowflake-managed compute resources to speed up the point look-up queries.
Cost & Performance Driving Parameters of the Warehouse
Your Snowflake virtual warehouse is the engine of your analytics. It directly impacts query performance and operational costs. It is imperative that you are well-versed in its configuration choices for optimizing spend while ensuring blazing-fast query execution.
Let us understand all the key parameters within the Snowflake virtual warehouse.
1. Warehouse Size
The warehouse size ranges from X-Small to 6X-Large. It controls the number of compute resources and impacts query parallelism and speed.
2. Max Concurrency Level
Different concurrency levels define the number of queries that can run parallelly. Higher sizes naturally support greater concurrency.
3. Auto-Suspend
Automatically suspends the warehouse after a defined period of inactivity to help save costs.
4. Auto-Resume
For smoother execution without manual intervention, it automatically resumes the warehouse when a new query is submitted.
5. Min/Max Clusters (for Multi-Cluster Warehouses)
These are used in multi-cluster setups to scale out for high-concurrency workloads. It automatically starts more clusters if necessary.
6. Scaling Policy
Includes two options, namely Standard and Economy. They determine the cluster’s aggressiveness upon addition or removal from multi-cluster warehouses.
7. Resource Monitors
It tracks and controls credit usage to manage costs effectively. The warehouse is alerted or suspended when it approaches the threshold.
Managing the Compute credits is of utmost importance. The following section goes deeper into the best practices of compute management and cost optimization.
11 Snowflake Cost Optimization Best Practices
Without a proactive strategy, your compute costs can quickly outpace expectations and turn Snowflake’s elasticity into a headache.
How do you ensure you’re not overspending while maintaining peak performance?
As a business owner, your knowledge shouldn’t be limited to Snowflake pricing and credit details, but you should also learn about its best practices.
Here are tested Snowflake cost optimization best practices for actionable strategies to help eliminate waste, streamline resource consumption, and maximize your ROI.
1. Workload Isolation
Your multiple workloads must include data ingestion, data engineering teams, data transformation, and data analytics. Creating a single virtual warehouse supporting all these workloads and teams is not always recommended. Allocating different warehouses to different teams makes it easier to track and scale compute.
2. Right Scaling Policy on Multi-Cluster Warehouse
The two policies to choose from are:
- STANDARD: It focuses on delivering high query performance by minimizing wait times. Upon detecting queuing, it quickly spins up an additional VWH to handle the load. It is suitable for user-facing workloads where attention on speed and responsiveness is high.
- ECONOMY: As a cost-efficient option, it is well-suited for batch workloads where performance is not the top priority. It does not immediately react to query queues. Instead, it waits until there’s at least six minutes of work queued before adding more clusters.
3. AUTO_SUSPEND Settings
It is a parameter set when configuring the warehouse and is directly proportional to the warehouse cache that Snowflake provides. If the warehouse is suspended, all its cache is lost. Consequently, incremental queries won’t be able to use the cache to improve query performance.
While all analytical workloads should keep the AUTO_SUSPEND parameter minimum, here is the recommendation for all other kinds of workloads:
| Use Cases | Recommended AUTO_SUSPEND | Reason |
| Interactive / BI Queries | 60 – 120 seconds | Minimizes idle cost while ensuring quick response for ad-hoc queries |
| ETL / Batch Jobs | 300 – 600 seconds | Avoids frequent suspend-resume during short gaps in scheduled workloads |
| Low Usage / Dev Warehouse | 60 – 180 seconds | Keeps costs low while allowing occasional testing or development queries |
| High-Concurrency Workload | 60 seconds or based on demand | Prioritize responsiveness for many concurrent users |
| Rarely Used | 30 – 60 seconds | Suspend as quickly as possible to minimize cost |
4. Scaling Up a Warehouse
Scaling up a warehouse involves increasing the warehouse size, for instance, going from X-Small to Large. It often happens in the case of complexity that you must handle.
| Condition | When to Scale Up a Warehouse |
| Resource spillage or disk spillage | When queries spill to local or remote storage, it’s a sign that you need more memory or CPU. |
| Long-running queries | If individual queries are taking too long, increasing size improves parallelism and CPU availability. |
| Consistent workload slowdowns | If performance is slow across the board, even with low usage, the warehouse size may be too small. |
| Heavy transformations | For large JOINs, aggregations, window functions, or MERGE/UPDATE/DELETE, bigger compute can significantly speed up performance. |
| Low concurrency but high compute | When a few users or processes run computationally intensive tasks, scaling up is wiser than adding more clusters. |
5. Scaling Out a Warehouse
You scale out a warehouse when there is a need for concurrency. The multi-cluster warehouse comes into play here.
Suppose you have workloads where, at any point in time, there are multiple processes and jobs expected to run. In such a situation, the multi-cluster warehouse will kick in.
Here are a few key use cases for its consideration:
| Condition | When to Scale Out |
| High query concurrency | Multiple users or apps are submitting queries at the same time. |
| Queued queries | Scaling out distributes the load if queries often wait in the queue and are not long-running. |
| BI dashboards with multiple users | Dashboards can trigger many simultaneous queries. Scale out to keep them responsive. |
| Customer-facing apps | Since end-user queries should not queue, scaling out maintains real-time performance. |
| Multiple data pipelines running | Concurrent ETL/ELT workflows benefit from more parallel compute |
6. STATEMENT_TIMEOUT_IN_SECONDS Value Settings
STATEMENT_TIMEOUT_IN_SECONDS is a manageable value at a warehouse level, set to be 48 hours by default.
Hence, if a bad query is executed on the platform, it can continue running for at least 48 hrs. It will thus cost you more and must be set at each warehouse level to optimize the spend.
| Aspect | Best Practice |
| Default Behaviour | The timeout is 48 hours by default. Queries can hence run till that time without manual intervention. |
| Align with Use Case | Interactive queries: 300–600 seconds. ETL workloads: 1800+ seconds. |
| Monitor & Adjust | Use Query History to identify unusually long-running queries. Tune the timeout accordingly. |
| Purpose | Automatically cancels long-running queries after a defined time. |
| Use for Cost & Governance | Prevents accidentally expensive or poorly written queries, which may consume too many resources. |
| Set at Appropriate Level | Preferably set at the warehouse level for workload flexibility. |
| Avoid Overly Aggressive Values | A very short timeout (eg, 30 seconds) can kill valid complex queries. |
7. Resource Monitor Set up at a Warehouse Level
Resource monitors are Snowflake’s internal feature for tracking the credit consumption. Setting up resource monitors for every warehouse to optimize credit is recommended.
8. Minimum Cluster Size to 1
To avoid overprovisioning in a multi-cluster warehouse, set the minimum cluster size to one. Snowflake automatically adds clusters as needed, up to the defined maximum, with minimal provisioning delay. If the minimum cluster count is above 1, it can result in idle clusters incurring charges.
9. Access Controls for a Warehouse
Applying Role-Based Access Control (RBAC) to VWHs in Snowflake secures access while working as a strategic lever for cost optimization. You can prevent unauthorized or unintentional consumption of computing resources by controlling who can start, stop, modify, or resume a warehouse.
You can grant specific authorities, such as ETL admins or data scientists, the usage rights on heavy-duty warehouses. It will help you reduce the risk of triggering large compute clusters for lightweight queries.
Similarly, you can restrict the ability to resize or resume warehouses. Consequently, warehouses do not stay active unnecessarily and dodge idle compute charges
10. Query Frequency Choices
Batch data transformation pipelines usually run hourly by default in many enterprises. Yet, this often mismatches actual downstream consumption, which may not genuinely require such frequent, low-latency updates. You can adjust the run frequency of these pipelines to align with your specific business needs. It will help you achieve substantial cost optimizations.
11 Data Management
Effective data management involves systematically organizing, maintaining, and storing data to lower unnecessary expenses. Regularly reviewing and pruning unused/old data, efficiently loading data, and leveraging micro-partitioning for optimal query performance are a few things to follow.
Implement clear data retention policies to understand data access patterns. It is a proactive approach that minimizes storage costs while optimizing compute usage by processing only necessary, well-structured data.
16 High-Impact Cost Optimization Metrics
Getting into the depths of Snowflake cost optimization is more than just learning its strategies.
You need precise measurement and knowledge of where your credits are going.
Here is a practical checklist of key areas to focus on for granular cost control within your Snowflake environment:
| Parameters/Case in Point | Category | Comments |
| Manage auto_suspend Values | Compute | Check this value at the warehouse level. |
| Manage statement_timeout Values | Compute | The default value is 48 hours at a warehouse level. Change it to an optimum level. |
| Manage Warehouse Size | Compute | Reduce the warehouse size of the workloads as necessary. |
| Warehouse Segregation | Compute | Results in better user experience and workload isolation. |
| Manage Cluster Size | Compute | Query queuing enables size change from single to multi-cluster. |
| Manage Query Frequency | Compute | Optimizes the number of query runs. |
| Consolidate Warehouse | Compute | A single warehouse solves all data teams or environments. |
| Avoid Frequent DML Operations | Storage | Minimize DML operations when necessary. |
| Lower Data Retention Period | Storage | Time travel to Active byte percentage checks must be done continuously. |
| Drop Unused Warehouse | Compute | Eliminates inactive warehouses. |
| Use Transient/Temporary Tables | Storage | Determine the right table design in the layered architecture. |
| Drop Unused Tables | Storage | Housekeeping checks. |
| Manage Failed Query Runs | Compute | Identify the failed query runs and avoid running them. |
| Manage Long Running Queries | Compute | Apply query optimization techniques. |
| Resource Monitors, Budgets,Alerts & Notifications | Alerts | Actively set and monitor these for proactive cost control. |
How can Aegis help with Snowflake Cost Optimization?
Optimizing cost within a data platform is important for sustainable and scalable operations. You must actively manage compute resources, query efficiency, and storage usage to eliminate unnecessary expenditure while maintaining performance.
Snowflake cost optimization prevents overspending and aligns platform usage with business priorities. So, you can allocate the budget towards innovation and growth.
At Aegis, our seasoned experts deliver Snowflake consulting services for the right governance, monitoring, and configuration of the Snowflake platform. Achieve high operational efficiency with financial accountability with our professionals by your side.
Contact us to cut your Snowflake costs and boost performance.
FAQs
Q1. What are the top Snowflake cost optimization techniques?
Top Snowflake cost optimization techniques include optimizing query performance, managing data retention policies, right-sizing virtual warehouses, and scaling out a warehouse. Adopting these helps reduce costs.
Q2. What are the native features of the best Snowflake cost optimization tools?
Top Snowflake cost optimization tools include native features like Resource Monitors, Account Usage schema, Auto-Suspend/Resume for warehouses, and Query Profile for usage analysis.
Q3. How to reduce costs in Snowflake?
To reduce costs in Snowflake, you must adopt the right strategies and techniques as per your organizational needs.
