Understanding Snowflake's Cost Model and How to Optimize It

Why Some Say Snowflake Costs Can Be Unpredictable

One of the biggest concerns we hear from companies using Snowflake is: How much will this cost me each month?

Snowflake’s flexible, usage-based pricing model is great for scalability, but it can quickly lead to unexpected expenses if not managed properly. Without a clear understanding of storage, compute, overhead services, and data transfer costs, businesses often struggle to predict and optimize their spending.

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Quick Summary:

• Compute costs depend on warehouse size and usage.
• Storage costs vary based on compression and data volume.
• Cloud services and data transfers can add hidden costs.
• Optimization strategies can help significantly reduce expenses.

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What You’ll Achieve: Predictable and Optimized Snowflake Spend

By the end of this post, you’ll be able to: 

- Break down your Snowflake bill into its core cost components.
- Estimate your monthly Snowflake cost based on your usage.
- Apply practical strategies to optimize Snowflake usage and reduce waste.

Without further ado, let’s dive right in!

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The Three Cost Drivers of Snowflake

📌 Quick Summary:

Your Snowflake spend is the total of 4 cost layers. Depending on how the platform is being used, 1 or more layers is billed based on the actual use of it during the period  :

• Compute Costs: Charged based on warehouse size and usage; enable auto-suspend to optimize.
• Cloud Services Costs: Free if ≤10% of compute; excessive metadata operations can increase costs.
• Storage Costs: Compressed data is cheaper; Apache Iceberg provides external storage options, storage is billed on another contract.
• Data Transfer Costs: Egress is charged; use data sharing to minimize external transfers.

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Compute Costs 

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Snowflake Credits: Understanding Usage and Consumption-Based Charging

What Are Snowflake Credits?

Snowflake operates on a credit-based consumption model, where credits represent the fundamental unit of compute usage. Instead of a traditional per-hour or per-core pricing structure, Snowflake abstracts these into credits that track consumption across different workloads.

Key Characteristics of Snowflake Credits:

• Credits are used for all compute-related activities, including query execution, data ingestion, transformations, and AI/ML workloads.
• Each warehouse size has a defined credit consumption rate per hour.
• Some Snowflake-managed services (serverless features) consume credits independently of warehouses.
• Credits can be purchased on-demand or through prepaid commitments at discounted rates.

Understanding how Snowflake charges for credit consumption is key to managing costs effectively.

How Credits Are Consumed

1. Virtual Warehouses

Virtual warehouses are the primary consumer of credits in Snowflake. They power query execution and data transformations. Credit consumption is determined by warehouse size and runtime.

Warehouse Size

Credits per Hour

XS

1

S

2

M

4

L

8

XL

16

2XL

32

3XL

64

4XL

128

Optimization Tip: Use auto-suspend and scaling policies to minimize credit consumption when warehouses are idle.

2. Serverless Features

Certain Snowflake features use Snowflake-managed compute, billed separately from virtual warehouses.

Feature

Credits per Hour

Snowpipe Streaming

0.01 per client instance

Query Acceleration

1

Materialized Views Maintenance

2

Optimization Tip: Use scheduled refresh intervals to control costs for dynamic tables and materialized views.

3. AI & ML Workloads (Snowflake Cortex)

Snowflake’s AI and ML functions are billed based on credit consumption per unit of processing.

Feature

Credits per 1M Tokens or Execution

Claude 3.5 Sonnet

2.55 credits

Llama3-70B

1.21 credits

Mixtral-8x7B

0.22 credits

Cortex Search (per GB)

9 credits

Optimization Tip: Batch processing can reduce unnecessary credit usage for AI workloads.

4. Data Sharing & Replication

• Data Sharing: Providers serving shared data may see increased compute consumption, leading to higher credit usage.
• Replication & Disaster Recovery: Cross-region replication consumes storage and compute credits for data synchronization.

Optimization Tip: Use replication selectively and limit query execution on shared data to control costs.

Credit-Based Cost Management

Pre-Purchased vs. On-Demand Credits

• On-Demand Credits: Billed at standard rates, useful for flexibility but more expensive.
• Pre-Purchased Credits: Available via Snowflake Capacity Plans, offering bulk discounts.

Managing Credit Consumption

• Enable Warehouse Auto-Suspend: Prevent unused compute from running unnecessarily.
• Monitor Query Performance: Optimize queries to reduce credit-heavy operations.
• Right-Size Warehouses: Use the smallest warehouse that meets performance requirements.
• Leverage Cost Reporting: Track credit usage in Snowflake’s billing dashboard.

By implementing these strategies, organizations can optimize their Snowflake spend, ensuring predictable and efficient credit usage.

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How it works: Compute costs are based on the credits consumed when running queries, loading data, and performing analytics. Snowflake offers different warehouse sizes, each consuming a different number of credits per hour. Virtual Warehouses, the compute engines that power your , Serverless Features, Materialized Views & Dynamic Tables)

Compute Costs Per Hour (By Warehouse Size)

Warehouse Size

Credits/Hour

XS

1 credit

S

2 credits

M

4 credits

L

8 credits

XL

16 credits

2XL

32 credits

3XL

64 credits

4XL

128 credits

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Serverless Features & Dynamic Tables Costs 

Some Snowflake features use Snowflake-managed compute and are billed separately from virtual warehouses. These include:

• Snowpipe Streaming: 0.01 credits per client instance per hour.
• Query Acceleration: 1 credit per hour.
• Materialized Views Maintenance: 2 credits per hour.

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✅ Optimization Tip: Enable auto-suspend to prevent idle warehouses from consuming credits unnecessarily. Dynamic Tables run automatically to maintain fresh data, so configure refresh intervals to balance cost and performance.

1.2. Cloud Services Costs (Metadata, System Operations & Query Overhead)

• Snowflake’s Cloud Services layer includes logging in, managing metadata, query parsing, and system operations necessary to run the platform effectively. These services are used for handling authentication, session management, query compilation, and performance monitoring.
• Cost: 4.4 credits per hour, which applies to operations such as:
  
  • Query parsing and optimization.
  • Metadata management (tracking schemas, tables, and views).
  • Session authentication and security processes.
  • Performance monitoring and logging.
• Optimization: Cloud Services costs are free if they are ≤10% of your total compute credit consumption. If your Cloud Services usage exceeds this limit, you will incur additional charges.

✅ Actionable Insight: Be mindful of:

• Frequent schema changes: Constantly altering table structures can increase metadata-related costs.
• External Stages & Egress Fees: Querying External Stages (e.g., AWS S3, Azure Blob) may generate unexpected egress charges—consider staging frequently accessed data inside Snowflake to reduce costs.
• Query parsing inefficiencies: Complex queries that require excessive optimization cycles may drive up Cloud Services costs.
• Excessive session authentications: Unoptimized authentication mechanisms can create unnecessary overhead.

1.3. Storage Costs (Data Storage, Cloning, Fail-Safe, Hybrid Tables & Time Travel)

How it works: You are charged based on the average amount of compressed data stored per month.

Storage Costs Per TB Per Month

Cloud Provider

Region

Standard Storage Cost ($/TB)

AWS

US East

$23.00

AWS

EU (Frankfurt)

$24.50

Azure

West Europe

$23.00

GCP

US Central

$20.00

✅ Optimization Tip: Use Snowflake’s automatic compression and regularly archive unused data to reduce storage costs. Be mindful that Time Travel (available for up to 90 days in Enterprise Edition) increases storage costs, so configure retention settings appropriately. If using Hybrid Tables, monitor the compute overhead caused by transactional consistency and indexing operations.

2. Additional Snowflake Cost Factors

2.3. Replication & Disaster Recovery Costs

Cross-region replication enables high availability and disaster recovery, but it comes at an additional cost.

Cost Considerations for Replication:

• Storage Costs: Both the source and target Snowflake accounts are billed for stored data.
• Compute Costs: Running replication jobs consumes credits based on the amount of data copied.
• Network Transfer Fees: Replicating data across cloud providers or regions incurs egress charges.

✅ Optimization Tip: Use replication selectively for mission-critical datasets and avoid unnecessary cross-region copies.

2.0. Snowflake Container Services & External Functions

Snowflake Container Services provide the ability to run containerized workloads directly within the Snowflake ecosystem. This feature allows organizations to deploy custom applications, machine learning models, and data processing pipelines in a serverless environment, reducing the need for external infrastructure.

External Functions & API Calls Costs

Snowflake enables integration with external services through External Functions (e.g., AWS Lambda, Azure Functions). However, these functions consume compute credits, and outbound API calls incur additional charges.

Cost Considerations:

• Function Execution Costs: External functions run on Snowflake-managed compute, incurring per-second billing.
• API Call Charges: Each request to an external service may add network costs.
• Data Transfer Fees: Large payloads increase API call expenses.

✅ Optimization Tip: Optimize external function usage by batching API requests and minimizing unnecessary function calls. Snowflake Container Services provide the ability to run containerized workloads directly within the Snowflake ecosystem. This feature allows organizations to deploy custom applications, machine learning models, and data processing pipelines in a serverless environment, reducing the need for external infrastructure.

Cost Considerations for Snowflake Container Services:

• Compute Usage: Containers consume Snowflake compute credits based on allocated CPU and memory resources.
• Data Access Fees: Reading and writing data from Snowflake tables within a containerized workload can generate additional costs.
• Execution Time: Longer-running container jobs increase credit consumption, making optimization strategies crucial.

✅ Optimization Tip: Optimize containerized workloads by right-sizing resource allocations and scheduling jobs during low-demand periods to minimize cost.

2.1. Snowflake AI Features

Snowflake offers AI-powered compute for various machine learning and natural language processing tasks. These features are divided into two categories: Cortex LLM Functions and Cortex Standard Machine Learning Functions, each with distinct cost structures and use cases.

Cortex LLM Functions

Snowflake Cortex provides access to large language models (LLMs) for tasks such as text embedding, summarization, translation, and sentiment analysis. These functions are billed based on the number of tokens processed, where tokens represent units of text (words or subwords) handled by the model. Each processed token contributes to overall compute costs.. These models process input and output text as tokens, and costs are determined based on the volume of tokens processed.

Pricing Details for LLM Functions:

Feature

Cost (Credits per 1M Tokens)

Claude 3.5 Sonnet

2.55 credits

Llama3-70B

1.21 credits

Mixtral-8x7B

0.22 credits

Cortex Standard Machine Learning Pricing:

Function

Cost Basis

Forecasting

Compute time used

Clustering

Compute time used

Anomaly Detection

Compute time used

Classification

Compute time used

Example Calculation: For LLM Functions:

• A company using Claude 3.5 Sonnet to process 10 million tokens per month would incur the following cost:
  
  • 10M tokens × 2.55 credits per 1M tokens = 25.5 credits
  • If each credit costs $2.50, the total monthly cost would be 25.5 × $2.50 = $63.75/month

For Standard Machine Learning Functions:

• Running a forecasting model that uses 10 compute credits per hour, operating 5 hours per day for 20 days a month:
  
  • 10 credits/hour × 5 hours/day × 20 days = 1,000 compute credits
  • At $2.50 per credit, this would cost $2,500/month
• 10M tokens × 2.55 credits per 1M tokens = 25.5 credits
• If each credit costs $2.50, the total monthly cost for this AI feature would be: 25.5 × $2.50 = $63.75/month

✅ Optimization Tip: Batch processing workloads can help reduce unnecessary compute costs and take advantage of volume efficiencies.

Cortex Standard Machine Learning Functions

Cortex also supports standard machine learning functions, such as forecasting, anomaly detection, clustering, and classification. Unlike LLMs, these functions do not rely on token-based pricing but instead consume compute resources based on data volume and algorithm complexity. These functions are useful for structured data analysis and predictive modeling., such as forecasting, anomaly detection, clustering, and classification. These functions do not rely on token-based pricing but instead consume compute resources based on data volume and algorithm complexity.

Cost Factors for Machine Learning Functions:

• Compute Credits Used: Similar to virtual warehouses, machine learning functions are billed per second of execution time.
• Feature-Specific Multipliers: Some functions, such as deep learning models, may have higher compute costs due to their complexity.
• Data Processing Volume: The more data points included in the analysis, the higher the compute cost.

✅ Optimization Tip: Use incremental forecasting and sampled data when possible to reduce compute usage while maintaining model accuracy. Snowflake offers AI-powered compute for natural language processing, classification, and search.

Cost of Snowflake Cortex Features

Snowflake Cortex provides built-in AI and machine learning capabilities that integrate seamlessly with Snowflake’s data cloud. These features include text embedding, sentiment analysis, summarization, and advanced search, all of which are billed based on compute usage.

• Compute-Based Pricing: Each Cortex feature consumes compute resources, which are charged in Snowflake credits.
• Usage-Based Billing: Similar to virtual warehouses, Cortex workloads are metered and charged per second, rounded up to the nearest credit unit.
• Feature-Specific Multipliers: Certain AI-driven functions may have multiplier rates applied to compute usage, impacting cost.

Pricing Details

Feature

Cost (Credits per 1M Tokens)

Claude 3.5 Sonnet

2.55 credits

Llama3-70B

1.21 credits

Mixtral-8x7B

0.22 credits

Cortex Search

9 credits per GB indexed

Example Calculation

A company using Claude 3.5 Sonnet to process 10 million tokens per month would incur the following cost:

• 10M tokens × 2.55 credits per 1M tokens = 25.5 credits
• If each credit costs $2.50, the total monthly cost for this AI feature would be: 25.5 × $2.50 = $63.75/month

✅ Optimization Tip: If using AI features, batch process workloads to avoid unnecessary compute costs and take advantage of volume efficiencies. Snowflake offers AI-powered compute for natural language processing, classification, and search.

Cost of Snowflake Cortex Features

Snowflake Cortex provides built-in AI and machine learning capabilities that integrate seamlessly with Snowflake’s data cloud. These features include text embedding, sentiment analysis, summarization, and advanced search, all of which are billed based on compute usage.

• Compute-Based Pricing: Each Cortex feature consumes compute resources, which are charged in Snowflake credits.
• Usage-Based Billing: Similar to virtual warehouses, Cortex workloads are metered and charged per second, rounded up to the nearest credit unit.
• Feature-Specific Multipliers: Certain AI-driven functions may have multiplier rates applied to compute usage, impacting cost.

Understanding how different AI features impact your Snowflake bill is crucial for budgeting and optimization. Snowflake offers AI-powered compute for natural language processing, classification, and search.

Understanding Tokens and Their Cost Impact

A token is a unit of text processed by AI models. Tokens can represent words, characters, or subword fragments, depending on the model. For example:

• "Snowflake is powerful." → Could be 4 tokens ("Snowflake", "is", "power", "ful.")
• "Data-driven insights." → Could be 3 tokens ("Data", "-driven", "insights.")

How tokens generate cost:

• Each AI-powered feature charges based on the number of tokens processed.
• Some models charge per input tokens (text you send), while others charge per both input and output tokens (generated text).
• Large queries or summarization tasks generate more tokens, increasing cost.

Feature

Cost (Credits per 1M Tokens)

Claude 3.5 Sonnet

2.55 credits

Llama3-70B

1.21 credits

Mixtral-8x7B

0.22 credits

Cortex Search

9 credits per GB indexed

✅ Optimization Tip: If using AI features, batch process workloads to avoid unnecessary compute costs. ✅ Optimization Tip: If using AI features, batch process workloads to avoid unnecessary compute costs.

2.2. Data Transfer Costs (Including External Stages)

Understanding Data Sharing and Its Cost Implications

Data transfers between regions or cloud providers can add hidden costs.

Understanding Ingress vs. Egress

• Ingress refers to data entering Snowflake from an external source (e.g., loading data from an on-premise system or another cloud service). Snowflake does not charge for ingress.
• Egress refers to data leaving Snowflake (e.g., extracting data to another cloud provider or external system). Egress is charged based on the amount of data transferred.

Snowflake allows organizations to share data with other Snowflake users without physically copying or transferring the data. This reverses traditional data transfer costs because shared data is accessed directly within Snowflake, eliminating egress fees typically associated with moving large datasets across cloud providers.

How Data Sharing Reduces Costs

Snowflake provides a Data Sharing Rebate Program, which grants credit offsets when you share data with external consumers. If you act as a data provider, you can receive rebates on compute credits used to serve shared data, effectively reducing the net cost of running queries for external consumers.

However, while data sharing reduces egress costs and grants rebates, it may increase compute costs for the data provider if complex queries are executed on the shared data. To manage this:

• Monitor shared query usage: Optimize workloads to avoid excessive compute costs.
• Leverage the Rebate Program: If you qualify, rebates can significantly reduce operational costs.
• Set access controls: Limit unnecessary computational loads from shared users. Snowflake allows organizations to share data with other Snowflake users without physically copying or transferring the data. This reverses traditional data transfer costs in some cases, because shared data is accessed directly within Snowflake, eliminating egress fees typically associated with moving large datasets across cloud providers.

However, while data sharing reduces egress costs, it may increase compute costs for the data provider if complex queries are executed on the shared data. Data providers should monitor usage and ensure proper governance to control costs.

Data transfers between regions or cloud providers can add hidden costs.

Understanding Ingress vs. Egress

• Ingress refers to data entering Snowflake from an external source (e.g., loading data from an on-premise system or another cloud service). Snowflake does not charge for ingress.
• Egress refers to data leaving Snowflake (e.g., extracting data to another cloud provider or external system). Egress is charged based on the amount of data transferred.

Cloud Provider

Region

Same-Cloud Transfer ($/TB)

Cross-Cloud Transfer ($/TB)

AWS

US East

Free

$90.00

Azure

West Europe

Free

$87.50

GCP

US Central

Free

$120.00

✅ Optimization Tip: Minimize cross-region or cross-cloud data transfers by keeping compute and storage in the same region. Data transfers between regions or cloud providers can add hidden costs.

Cloud Provider

Region

Same-Cloud Transfer ($/TB)

Cross-Cloud Transfer ($/TB)

AWS

US East

Free

$90.00

Azure

West Europe

Free

$87.50

GCP

US Central

Free

$120.00

✅ Optimization Tip: Minimize cross-region or cross-cloud data transfers by keeping compute and storage in the same region.

3. How to Estimate Your Monthly Snowflake Cost

📌 Scenario:

• You store 500GB of data.
• You run a Medium (M) warehouse for 2 hours per day, 20 days a month.

Cost Breakdown

Cost Component

Calculation

Estimated Cost

Storage

500GB @ $23 per TB/month

$11.50/month

Compute

40 hours/month × 4 credits/hour × $2.50 per credit

$400/month

Services

5% of total compute cost

$20.58/month

Total Monthly Cost

$432.08/month

✅ Outcome: By understanding this structure, you can predict and adjust your Snowflake usage to stay within budget.

4. Optimizing Snowflake Costs: Practical Tips

4.1. Compute Cost Optimization

✔ Enable Auto-Suspend: Avoid running warehouses when not needed.
✔ Use Multi-Cluster Scaling Carefully: Avoid over-provisioning warehouses.
✔ Optimize Queries: Use Snowflake Query Profile to reduce resource-intensive operations.

4.2. Storage Cost Optimization

✔ Use Snowflake’s Native Compression: Reduce data footprint automatically.
✔ Archive Historical Data: Move rarely used data to cheaper storage tiers.

4.3. Service & Data Transfer Cost Optimization

✔ Monitor Service Usage: Avoid excessive metadata operations. ✔ Keep Compute & Storage in the Same Region: Reduces unnecessary data transfer costs.

5. Buying Snowflake Directly vs. Through a Partner

📩 Struggling with unexpected Snowflake costs? Let’s analyze your bill and find savings—get a free audit today!

Final Takeaway: Take Control of Your Snowflake Costs

🚀 Next Step: Run a Snowflake cost audit to identify quick-win savings. Need help? Let’s talk.

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