I. Executive Summary: Navigating Snowflake Costs on AWS Marketplace

Deploying Snowflake on Amazon Web Services (AWS) through the AWS Marketplace offers a streamlined procurement process and consolidated billing. However, understanding the full spectrum of associated costs is crucial for effective financial management and optimizing the total cost of ownership (TCO). Costs extend beyond Snowflake's direct service fees for compute and storage, encompassing a variety of AWS infrastructure charges that can be influenced by Snowflake usage patterns. These include, but are not limited to, data transfer, storage for staging and external tables (Amazon S3), compute for ancillary services (AWS Lambda, Amazon EC2), data ingestion services (Amazon Kinesis, AWS Glue), and security or private connectivity solutions (AWS PrivateLink, AWS KMS).

While the AWS Marketplace simplifies billing by integrating Snowflake charges into the AWS invoice, this convenience can sometimes obscure the direct correlation between granular Snowflake consumption (measured in credits and terabytes of storage) and the line items appearing on the AWS bill. The AWS Marketplace often represents Snowflake usage in standardized financial units (e.g., "$0.01 per unit of usage" ), necessitating a diligent reconciliation process using both Snowflake's detailed usage reporting and AWS cost management tools.

A key consideration is the interdependent nature of the cost ecosystem. Snowflake services operate on AWS infrastructure, meaning that optimizing costs requires a holistic approach. Decisions made within Snowflake, such as data loading strategies or query design, can directly impact AWS service consumption (e.g., S3 requests, data transfer volumes). Conversely, AWS infrastructure choices, like regional placement of S3 buckets, can affect Snowflake performance and costs. Effective cost management, therefore, hinges on proactive monitoring of both Snowflake and AWS utilization, strategic configuration of resources across both platforms, and a keen awareness of the pervasive impact of data transfer. This report aims to dissect these cost components, clarify billing mechanisms, and provide insights for managing the financial aspects of a Snowflake deployment on AWS acquired via the AWS Marketplace.

II. Understanding Core Snowflake Service Costs on AWS

Snowflake's pricing model is usage-based, primarily revolving around compute, storage, and cloud services, with costs varying by the chosen Snowflake edition, AWS region, and specific features utilized.

A. Snowflake Editions: Cost and Feature Implications

Snowflake offers several editions—Standard, Enterprise, Business Critical, and Virtual Private Snowflake (VPS)—each with a different price per credit and a distinct set of features. The choice of edition is a foundational cost driver because it sets the base rate for all compute credits consumed. For instance, in the AWS US East (Northern Virginia) region, on-demand credit prices are approximately $2.00 for Standard Edition, $3.00 for Enterprise Edition, and $4.00 for Business Critical Edition.   

Higher editions provide advanced functionalities. Enterprise Edition, for example, offers features like multi-cluster warehouses for improved concurrency, extended Time Travel (up to 90 days versus 1 day in Standard), and materialized views. Business Critical Edition builds upon Enterprise by adding enhanced security and compliance features, such as support for AWS PrivateLink for private network connectivity, and failover/failback capabilities for disaster recovery.   

The selection of an edition based on a specific feature requirement can have a significant impact on overall costs. Since compute resources typically account for a substantial portion of a Snowflake bill, estimated at around 80% by some analyses , a higher per-credit cost associated with an advanced edition will amplify all compute-related expenses. For example, if an organization requires AWS PrivateLink, it must opt for the Business Critical Edition. This decision means all virtual warehouse usage will be billed at the higher Business Critical credit rate, not just the usage related to PrivateLink. This effectively increases the cost of all existing and future workloads.   

Furthermore, users must carefully evaluate the trade-offs between the benefits of higher-tier features and their associated costs. For instance, the extended 90-day Time Travel in Enterprise Edition provides longer data retention for recovery but comes with the higher credit cost of the Enterprise Edition and increased storage costs due to retaining more historical data. The business value of such extended retention must be weighed against these dual cost increases. Similar considerations apply to other advanced features, where the incremental cost of the edition and any associated resource consumption (like additional storage for Time Travel) must be justified by the feature's utility.   

The table below summarizes key differences between Snowflake editions relevant to AWS deployments:

Table 1: Snowflake Edition Feature and Cost Comparison (AWS US East - N. Virginia, On-Demand)

Note: Credit costs are indicative and can vary by region, cloud provider, and contract terms (On-Demand vs. Capacity). Feature lists are not exhaustive.   

B. Compute Costs: Virtual Warehouses and Snowflake Credits

Snowflake's compute resources are provisioned through Virtual Warehouses (VWs), and their consumption is measured in Snowflake credits. A crucial aspect of Snowflake's architecture is the separation of compute and storage, meaning VWs consume credits only when they are running and actively processing queries, loading data, or performing other DML operations.   

Billing for VWs is per-second, but with a 60-second minimum charge each time a warehouse is started or resumed from a suspended state. This minimum also applies to the incremental credits when a running warehouse is resized to a larger size; for example, resizing from a Medium (4 credits/hour) to a Large (8 credits/hour) incurs an additional charge for one minute's worth of four additional credits before per-second billing for the new size resumes.   

Warehouses are available in various T-shirt sizes, typically from X-Small to 6X-Large. Each size increment usually doubles the compute power and, consequently, the number of credits consumed per hour. For example:   

• X-Small: 1 credit/hour
• Small: 2 credits/hour
• Medium: 4 credits/hour
• Large: 8 credits/hour
• X-Large: 16 credits/hour

The choice of warehouse size directly impacts both query performance and cost. Larger warehouses can process queries faster but consume credits at a higher rate. To manage costs, Snowflake provides auto-suspend and auto-resume features. A VW can be configured to automatically suspend after a specified period of inactivity and will automatically resume when a new query is submitted.   

The 60-second minimum billing policy can lead to disproportionately high costs if not managed carefully. For workloads characterized by frequent starting and stopping of warehouses for very short tasks, the cumulative effect of these minimum charges can be significant. If a query requires only 15 seconds of compute time but starts a suspended warehouse, it will be billed for 60 seconds of that warehouse's operational time. If this pattern repeats often, the "minimum charge overhead" can substantially exceed the cost of actual productive compute.

Moreover, there's a trade-off between aggressive auto-suspension and query performance due to cache state. Virtual warehouses maintain a local data cache when active, which speeds up subsequent queries on the same data. If a warehouse is suspended too aggressively (e.g., after only 60 seconds of inactivity), this cache is lost. Upon resuming for a new query, the warehouse starts with a "cold" cache, potentially leading to slower initial query performance as data must be re-fetched from remote storage. Users might then be tempted to use larger, more expensive warehouses to compensate for this perceived slowness, inadvertently increasing costs. Finding an optimal auto-suspend timeout that balances credit savings from suspension with performance benefits from a warm cache is crucial for cost efficiency.   

C. Storage Costs: Per-Terabyte Pricing

Snowflake charges for data storage at a flat rate per terabyte (TB) per month, based on the average daily amount of data stored after Snowflake's automatic compression. Typical on-demand storage rates in AWS US East regions are around $23 per TB per month, but this can vary depending on the specific AWS region and whether the account is on-demand or has pre-purchased capacity. Snowflake's compression is generally efficient, with observed ratios often averaging around 3:1, meaning 3TB of raw data might only consume 1TB of billable storage.   

Storage costs apply not only to active data in tables but also to data retained by Snowflake's data protection features: Time Travel and Fail-Safe.   

• Time Travel allows querying data as it existed at a previous point in time. The retention period for Time Travel data is configurable, up to 1 day for Standard Edition and up to 90 days for Enterprise and Business Critical Editions. The longer the retention period, the more historical data is stored, directly impacting storage costs.   
• Fail-Safe provides a non-configurable 7-day period (after Time Travel retention ends) during which historical data may be recoverable by Snowflake Support. This Fail-Safe storage also contributes to the total storage footprint.   

These data retention features, while valuable for data recovery and historical analysis, can lead to "invisible" storage consumption. Users might underestimate their storage bills if they only consider the size of their active tables, without accounting for the additional storage consumed by Time Travel and Fail-Safe data. For tables with frequent updates or high data churn, the volume of historical data retained for a 90-day Time Travel period can be many times larger than the active data itself, potentially leading to significant storage cost overruns if not monitored. One analysis highlighted an instance where Time Travel oversight led to a 66% increase in storage costs.   

Additionally, the variation in storage costs per TB across different AWS regions is an important factor for global organizations. While data transfer costs for replication are separate, the baseline storage cost in each region will apply to the data stored there. Storing large datasets in higher-cost regions will incrementally increase the global storage bill, influencing decisions on data placement and replication strategies.   

D. Cloud Services Layer: The 10% Rule and Its Drivers

Snowflake's architecture includes a cloud services layer that performs essential functions such as query parsing and optimization, metadata management, transaction management, and security enforcement. This layer consumes Snowflake credits. However, these credits are typically only billed if the daily consumption by the cloud services layer exceeds 10% of the daily credits consumed by virtual warehouses. If cloud services usage is, for example, 5% of warehouse compute, those cloud services credits are effectively free. If it reaches 15%, then 5% (the amount over the 10% allowance) becomes billable.   

Several activities can drive up the usage of the cloud services layer. These include executing very complex SQL statements, running an extremely high number of small or metadata-intensive queries, frequent DDL operations, or certain usage patterns from third-party tools that interact heavily with Snowflake's metadata.   

Consistently exceeding the 10% threshold for cloud services charges can often be an indicator of underlying inefficiencies. While some complex workloads might naturally use more cloud services, persistent overages often point to poorly optimized queries (e.g., those with excessive joins, recursion, or very long SQL text), inefficient data access patterns, or "chatty" applications and tools that make numerous small requests to Snowflake. Addressing these underlying issues can reduce both cloud services costs and potentially virtual warehouse compute costs.   

Even when cloud services consumption remains within the 10% "free" allowance, the processing performed by this layer is still happening. Extremely high, albeit unbilled, cloud service activity could theoretically contribute to overall system load or introduce subtle latencies in query planning or metadata operations, even if it doesn't directly appear as a line item on the bill. This is more of a performance consideration but underscores the importance of efficient interaction with the platform.

E. Serverless Features: Pricing for Snowpipe, Tasks, Materialized Views, etc.

Snowflake offers a suite of serverless features that operate on Snowflake-managed compute resources, consuming credits based on their specific usage metrics rather than on user-managed virtual warehouses. Key serverless features and their cost implications include:   

• Snowpipe: Used for continuous, automated data ingestion, typically from external stages like Amazon S3. Snowpipe costs comprise a compute component (billed based on processing time, often with a multiplier like 1.25x standard compute, or a specific credit rate per hour) and a per-file overhead charge (e.g., 0.06 credits per 1,000 files processed). An overhead is also included for event notifications or API calls, even if no data is loaded.  
• Automatic Clustering / Reclutering: Snowflake-managed compute is used to maintain the clustering of data in tables, improving query performance. Credits are consumed based on the compute resources utilized for these background operations.   
• Materialized Views (Maintenance): Credits are consumed for the background processes that keep materialized views synchronized with their base tables.   
• Search Optimization Service: This feature creates and maintains data structures to accelerate point-lookup queries. Credits are consumed for building and maintaining these search access paths.   
• Tasks: Used for scheduling the execution of SQL statements. Serverless tasks consume credits based on the compute resources required for their execution.   
• Query Acceleration Service (QAS): Can be enabled to boost the performance of exceptionally large queries by offloading parts of the query processing to Snowflake-provided compute resources. QAS consumes credits when utilized, based on the amount of compute offloaded.   
• Snowpark Container Services (SPCS): Allows running containerized applications (e.g., AI/ML models, custom applications) directly within Snowflake. Costs for SPCS are multifaceted, including charges for compute pools (based on node type like CPU, GPU, High-Memory, and their hourly credit consumption), storage for container images in an image repository (which uses a Snowflake stage), storage for logs (event tables), and block storage if used by services. Data transfer costs also apply for outbound and internal data movements.   

The ease with which these serverless features can be enabled can sometimes lead to "feature creep." Organizations might activate multiple, independently-costing serverless operations (e.g., materialized view refreshes, search optimization on many tables, continuous Snowpipe ingestion, automatic clustering) without fully tracking their cumulative, ongoing credit consumption. This can result in significant background costs that may surprise users primarily focused on their interactive virtual warehouse expenses, as illustrated by an example where just two serverless features running continuously on a development environment incurred over $44,000 in charges.   

For Snowpipe specifically, the per-file overhead charge (e.g., 0.06 credits per 1,000 files) can become a dominant cost factor if the ingestion pipeline involves a high volume of very small files. One analysis showed that ingesting 100 GB of data as 4.2 million 25KB files resulted in 251 credits per day just for file overhead, whereas ingesting the same 100 GB as 410 larger files (250MB each) incurred only 0.06 credits per day for this overhead. This highlights that if the data source generates many small files, the file fee can far exceed the actual compute cost for data processing. Optimizing upstream processes to batch data into larger files before ingestion is therefore critical for Snowpipe cost-effectiveness, an implication that might not be immediately apparent when considering only the "serverless compute" aspect.   

III. Snowflake via AWS Marketplace: Billing and Integration

Subscribing to Snowflake through the AWS Marketplace offers benefits like consolidated billing and leveraging existing AWS agreements, but it also introduces specific considerations for how charges are presented and reconciled.

A. How Snowflake Subscriptions Appear on Your AWS Bill

When a Snowflake subscription is procured via the AWS Marketplace, the charges for Snowflake services are integrated into the monthly AWS bill. AWS Marketplace acts as the billing intermediary: the customer pays AWS, and AWS then remits the appropriate portion to Snowflake. The AWS bill will typically feature line items under a "Marketplace services" section, detailing the charges attributed to the Snowflake subscription.   

The billing mechanism for SaaS subscriptions on AWS Marketplace generally involves the software vendor (Snowflake, in this case) sending metering records of customer usage to AWS. AWS then processes these records, calculates the charges based on the agreed-upon pricing dimensions, and includes them in the customer's AWS invoice.This process can introduce a degree of aggregation or delay in how Snowflake usage is reflected on the AWS bill compared to real-time monitoring within Snowflake itself. The AWS bill will show charges for a past usage period, potentially summarized, rather than an instantaneous, granular breakdown of every credit consumed or every GB stored at the exact moment of consumption. This makes it essential for users to correlate AWS billing data with Snowflake's internal, more detailed usage reporting for comprehensive cost analysis.   

B. Translating AWS Marketplace "Usage Units" to Snowflake Costs (Credits & Storage)

The AWS Marketplace listing for Snowflake specifies a pricing dimension described as "Snowflake Usage (Each unit is 1 cent of usage) | $0.01". This means that AWS bills for Snowflake consumption in increments of $0.01. This $0.01 unit is a financial representation of the total monetary value of the consumed Snowflake services—which includes compute credits, storage, cloud services layer usage, and serverless features. It is not a direct one-to-one mapping to a single Snowflake credit or a fixed quantity of storage.   

For example, if an organization's total Snowflake service consumption (calculated by Snowflake based on its own pricing for credits, storage, etc.) amounts to $250.75 for a billing period, this would appear on the AWS Marketplace portion of the AWS bill as 25,075 units of "Snowflake Usage" (since $250.75 / $0.01 = 25,075 units).

The actual cost of a Snowflake credit (e.g., $2.00 for Standard Edition on AWS US East) and the rate for storage (e.g., $23/TB/month) are determined by Snowflake's pricing policies, influenced by the chosen edition, region, and contract terms (on-demand vs. capacity). The $0.01 AWS Marketplace unit is merely the standardized denomination AWS uses to bill the total charge passed on from Snowflake. Therefore, this Marketplace unit serves as a "billing container" for the aggregated monetary value of consumed Snowflake services. It doesn't alter Snowflake's underlying pricing model but rather repackages the final billable amount from Snowflake into these Marketplace units. Users still need to understand Snowflake's native pricing structure to predict or control the number of these $0.01 units they will ultimately incur.   

C. Reconciling Snowflake Charges with AWS Cost and Usage Reports (CUR)

The AWS Cost and Usage Report (CUR) is the most detailed source of information regarding AWS costs, and it includes charges originating from AWS Marketplace subscriptions. Within the CUR, Marketplace charges related to Snowflake will be identifiable through specific line item types, service codes, or product codes that distinguish them as Snowflake usage. The "Dimension API Name" defined by the seller (Snowflake) when setting up their Marketplace product is often visible in billing reports and can aid in identification.   

To perform a thorough reconciliation, the total Snowflake charges reported on the AWS invoice or within the CUR must be compared against the detailed usage and cost data available from Snowflake's own ACCOUNT_USAGE schema. This schema contains various views such as METERING_HISTORY, STORAGE_USAGE, WAREHOUSE_METERING_HISTORY, PIPE_USAGE_HISTORY, and, crucially, USAGE_IN_CURRENCY_DAILY. The USAGE_IN_CURRENCY_DAILY view is particularly valuable as it presents Snowflake consumption in monetary terms, facilitating a more direct comparison with the charges appearing on the AWS bill.   

Minor discrepancies between the AWS bill and Snowflake's internal reporting can sometimes occur due to differences in billing cycle cutoffs, rounding methodologies, or the precise timing of when Snowflake submits its metering data to AWS. Effective cost reconciliation, therefore, necessitates using tools and reports from both AWS (CUR, Billing Console) and Snowflake (ACCOUNT_USAGE views). Relying on one platform alone will provide an incomplete picture, as the AWS bill shows the final aggregated charge, while Snowflake's views offer the granular breakdown of the activities that generated those charges.   

For granular cost allocation within an organization, AWS cost allocation tags can be applied to many AWS resources.Snowflake also supports tagging its own resources, like virtual warehouses and queries, for internal cost attribution.However, since the Snowflake charge on the AWS bill is an aggregated figure from the Marketplace, breaking this single line item down by internal business units or projects requires a two-step process: first, perform detailed cost attribution within Snowflake using its native tagging and ACCOUNT_USAGE data; second, correlate these internal Snowflake attributions with the total charge appearing on the AWS bill. Relying solely on AWS tagging for the Marketplace line item itself may not suffice for deep Snowflake cost allocation; Snowflake's own cost attribution mechanisms are essential.   

D. Potential AWS Marketplace-Specific Fees or Considerations

The primary advantages of procuring Snowflake through AWS Marketplace include consolidated billing onto the AWS invoice and the potential to leverage existing AWS enterprise discount programs or committed spend agreements. Generally, AWS Marketplace does not impose an additional "Marketplace fee" directly onto the customer on top of the software vendor's listed price for SaaS subscriptions. The price set by Snowflake is what AWS bills the customer, and AWS then takes a confidential percentage from Snowflake's revenue as its platform fee. The AWS Marketplace listing for Snowflake notes that "Additional AWS infrastructure costs may apply" , but this refers to charges for other AWS services (like S3, EC2, Data Transfer) that are used in conjunction with or by the Snowflake deployment, not a fee for the act of using the Marketplace to subscribe to Snowflake.   

It's important to distinguish this from Snowflake's own marketplace (Snowflake Marketplace, not AWS Marketplace), where users might be able to utilize their existing Snowflake credits or budget for acquiring data sets or applications from third-party providers listed within Snowflake's ecosystem. This is a separate concept from the AWS Marketplace procurement of the core Snowflake service.   

The main "consideration" when using AWS Marketplace for Snowflake is the billing abstraction (the $0.01 unit) and the consequent need for diligent reconciliation, as discussed previously. While standard Snowflake pricing generally applies, AWS Marketplace also facilitates private offers or custom contract terms between the customer and Snowflake , which could result in pricing that differs from publicly listed on-demand rates. The user's query implies a standard Marketplace subscription, where typically the underlying Snowflake pricing structure remains consistent.   

IV. AWS Infrastructure Costs Directly Associated with Your Snowflake Deployment

Beyond the core Snowflake service charges billed via AWS Marketplace, a significant portion of the TCO involves costs for AWS infrastructure services that are either directly consumed by Snowflake's operations or used in conjunction with your Snowflake environment.

A. Data Transfer: A Major Cost Component

Data transfer costs are a critical and often underestimated expense category when running Snowflake on AWS. These charges can arise from multiple layers: Snowflake-initiated egress and AWS-native data transfers.

Snowflake-Initiated Egress: Snowflake itself charges for data egress when data is moved out of the Snowflake service to certain destinations. This includes:   

• Transferring data to a different AWS region than where the Snowflake account is hosted (e.g., unloading data to an S3 bucket in another region).
• Transferring data to a different cloud provider.
• Transferring data to the internet.

Snowflake's data egress rates vary by the source and destination regions and cloud platforms. For example, transferring data from a Snowflake account in AWS US East to another AWS region might cost around $20/TB, while transferring to the internet from the same region could be approximately $90/TB. Importantly, Snowflake generally does not charge for data ingress (i.e., loading data into Snowflake).   

AWS Data Transfer Costs: These are separate charges levied by AWS for data movement involving its services, even if these movements are related to Snowflake activities.

• Data Ingress to AWS Services: Generally, data transfer into most AWS services like Amazon EC2 and Amazon S3 from the internet is free.   
• Data Egress from AWS Services to the Internet:
  • From Amazon EC2: AWS charges for data transferred out from EC2 instances to the internet. Pricing is typically tiered, with rates around $0.09/GB for the first 10TB/month in many US regions, after an initial 100GB monthly free tier aggregated across services.   
  • From Amazon S3: Similarly, data transferred out from S3 buckets to the internet is charged, with comparable tiered rates and a free tier.   
• Inter-Region Data Transfer within AWS:
  • EC2: Transferring data out from an EC2 instance in one AWS region to another AWS region (e.g., from US East to US West) typically costs between $0.01/GB and $0.02/GB, depending on the specific regions involved.   
  • S3: When Snowflake, located in one AWS region, executes a COPY command to load data from an S3 bucket located in a different AWS region, AWS will charge S3 data transfer out fees from the source S3 region. These rates are often around $0.02/GB.   
• Intra-Region Data Transfer within AWS (Across Availability Zones):
  • Data transferred "in" to and "out" from services like EC2, Amazon RDS, Amazon Redshift, and Elastic Network Interfaces (ENIs) across different Availability Zones (AZs) within the same AWS Region is typically charged at $0.01/GB in each direction. This can be relevant if Snowflake's underlying infrastructure (managed by Snowflake but running on AWS) or connected user applications (e.g., EC2-based BI tools) span multiple AZs for high availability and communicate across them. Data transfer within the same AZ is generally free for these services.   
• AWS PrivateLink Data Transfer: If AWS PrivateLink is used for secure, private connectivity to Snowflake (which requires the Snowflake Business Critical Edition), AWS imposes charges for the PrivateLink service. This includes an hourly fee for each Interface Endpoint provisioned in each AZ, plus a per-GB charge for data processed by the endpoint (e.g., around $0.01/GB for the first petabyte). If the PrivateLink connection spans AWS regions (e.g., VPC endpoint in one region connecting to a service in another), standard AWS inter-region data transfer fees also apply to the data moved.   

The interplay between Snowflake's egress charges and AWS's data transfer charges can lead to scenarios where costs are incurred from both platforms. For instance, if data is unloaded from Snowflake (in AWS Region A) to an S3 bucket (also in AWS Region A), and then this data is subsequently transferred from that S3 bucket to another AWS Region B or to the internet, the user might pay Snowflake for the initial unload (if Snowflake considers its VPC boundary as an egress point for billing, though typically same-region S3 might be considered internal) and then AWS for the S3 data transfer out from Region A. Careful architectural planning is essential to minimize such "double-hop" charges.

The geographical alignment of the Snowflake deployment with data sources (e.g., S3 buckets) and data consumers (e.g., BI tools, applications) is paramount for cost control. Misalignment can lead to persistent and substantial AWS inter-region or internet data transfer costs. For example, if Snowflake is deployed in AWS Region A, but the primary S3 buckets used for data loading are located in AWS Region B, every data load operation will incur AWS inter-region data transfer fees for moving data from S3 in Region B to Snowflake in Region A. Similarly, if applications consuming data from Snowflake are in a different region or on-premises (requiring internet transfer), AWS egress charges will apply. Co-locating Snowflake, its primary data sources, and key consuming applications within the same AWS region is a critical TCO consideration. 

Snowflake offers a feature called Egress Cost Optimizer (ECO) aimed at reducing Snowflake's own egress charges when sharing the same data across multiple regions or clouds via Cross-Cloud Auto-Fulfillment. However, ECO itself has an associated cost (e.g., $16.896 per TB-month for the ECO cache) and applies to specific data sharing scenarios. It does not eliminate the underlying AWS data transfer costs for the initial movement of data to populate the ECO cache, nor does it cover other types of data movements outside this specific sharing mechanism.   

The following table provides a comparative overview of various data transfer rates relevant to a Snowflake on AWS deployment:

Table 2: Comparative Data Transfer Rates (Illustrative AWS & Snowflake Examples)

Note: All rates are illustrative and subject to change. Always consult the latest official Snowflake and AWS pricing documentation for precise figures and regional variations.

B. AWS Storage Costs (Amazon S3)

Amazon S3 is frequently used with Snowflake for several purposes, each carrying potential AWS cost implications:

• Staging Files: S3 buckets are commonly used as external stages for bulk loading data into Snowflake using the COPY INTO command, or for unloading data out of Snowflake.   
• External Tables: Data stored in S3 can be queried directly by Snowflake using External Tables, where the data remains in S3 but its metadata is registered with Snowflake.
• Snowpipe Event Notifications: S3 event notifications (often routed via Amazon SQS or SNS) can trigger Snowpipe for automated data ingestion when new files arrive in a bucket.   

The AWS costs associated with S3 usage include:

• S3 Storage Costs: These vary based on the chosen S3 storage class (e.g., S3 Standard, S3 Intelligent-Tiering, S3 Standard-Infrequent Access, S3 Glacier tiers). For staging areas where data is frequently accessed for loading, S3 Standard is common, with typical rates around $0.023/GB per month in regions like US East.   
• S3 Request Costs: AWS charges for API requests made to S3, such as:
  • PUT, COPY, POST, LIST requests (e.g., uploading files to S3, listing files in a stage): Approximately $0.005 per 1,000 requests for S3 Standard.   
  • GET requests (e.g., Snowflake reading files from S3 during a COPY operation, Snowpipe ingestion, or querying External Tables): Approximately $0.0004 per 1,000 requests for S3 Standard.   
• S3 Lifecycle Policies: These can be configured to automatically transition data in S3 to cheaper storage tiers (e.g., from S3 Standard to S3 Standard-IA or S3 Glacier) or to delete old files. While intended for cost savings, the lifecycle transition operations themselves can incur charges (e.g., per 1,000 transition requests).   
• S3 Event Notifications: For Snowpipe auto-ingest, S3 can send event notifications when new objects are created. The S3 event notification to Amazon SNS or SQS is often free or very low cost from S3's perspective. However, the downstream SNS/SQS services will have their own charges based on the number of notifications or messages processed.   

While S3 storage per GB is relatively inexpensive, frequent loading or unloading of numerous small files to and from S3 staging areas can lead to an accumulation of S3 request costs (PUTs, GETs, LISTs). These costs are independent of Snowflake's own charges and can become significant if not managed. For example, even if the per-request cost is minimal (e.g., $0.0004 per 1,000 GETs), millions of requests due to a proliferation of small files can result in a noticeable AWS S3 bill, particularly for LIST operations if staging areas are not well-organized.

Similarly, while S3 Lifecycle policies are designed to reduce storage costs by moving data to less expensive tiers, misconfigurations can inadvertently increase S3 expenses. Transitioning data too early to archive tiers (which may have retrieval fees or minimum storage duration charges) or performing frequent transitions of many small objects can negate savings or even lead to higher overall S3 costs. It is important to align lifecycle policies with the actual data access patterns and retention requirements for Snowflake staging areas.   

The following table summarizes key Amazon S3 pricing components relevant to Snowflake users, based on US East (N. Virginia) region rates for S3 Standard:

Table 3: Key Amazon S3 Pricing Components (US East - N. Virginia Example for S3 Standard)

Note: Prices are illustrative for S3 Standard in US East (N. Virginia) and subject to change. Other S3 storage classes have different rates. Always consult official AWS S3 pricing.

C. AWS Compute for Ancillary Services

Beyond Snowflake's own compute, AWS compute services like AWS Lambda and Amazon EC2 may be used in conjunction with a Snowflake deployment, each incurring its own costs.

AWS Lambda: Lambda functions can be utilized in several ways with Snowflake:

• Snowflake External Functions: Snowflake can call Lambda functions (via Amazon API Gateway) to extend its capabilities with custom code or access to external services.   
• S3 Event Processing for Snowpipe: Lambda can be used to process S3 event notifications (e.g., new file arrival) and then trigger Snowpipe, although Snowpipe can also integrate more directly with SQS/SNS for this purpose.AWS Lambda pricing is primarily based on two factors: the number of requests made to the function and the duration of its execution, measured in gigabyte-seconds (GB-seconds). AWS provides a monthly free tier for both requests and GB-seconds. Additional costs can arise from ephemeral storage configured for the Lambda function or if provisioned concurrency is used.   

If Snowflake External Functions are invoked with high frequency or if the Lambda functions they call are long-running or memory-intensive, the resulting AWS Lambda charges can become significant. These costs could potentially outweigh the Snowflake-side compute cost of simply calling the external function. Therefore, optimizing both the frequency of external function calls from Snowflake and the efficiency (runtime, memory usage) of the Lambda functions themselves is crucial for managing overall costs.

Amazon EC2: While Snowflake is a fully managed service and does not require users to manage underlying EC2 instances for its core operations, organizations might deploy their own EC2 instances for various purposes related to their Snowflake environment:

• Running custom applications that connect to Snowflake for data processing or analytics.
• Hosting self-managed ETL/ELT tools that interact with Snowflake.
• Serving as bastion hosts for secure access or for other network components. Costs for such EC2 instances are standard AWS charges based on the instance type, operating system, chosen pricing model (On-Demand, Reserved Instances, Spot Instances), duration of use, and any attached EBS storage or Elastic IP addresses. Data transfer out of these EC2 instances also incurs charges, as detailed in section IV.A.   

If various teams within an organization independently deploy EC2 instances to support their specific Snowflake-related workflows (e.g., custom data loading scripts, hosting for analytical tools) without centralized oversight or optimization, these can become unmanaged "shadow IT" costs. While indirectly related to the overall data platform solution centered on Snowflake, these EC2 expenses will appear on the AWS bill separately from Snowflake's own service charges, making the TCO calculation for the entire Snowflake solution more complex if not properly tracked and attributed.

D. AWS Data Ingestion & Processing Services

Organizations may use other AWS services for data ingestion and transformation before data lands in Snowflake, or as part of data pipelines that interact with Snowflake.

Amazon Kinesis Data Firehose / Kinesis Data Streams:

• Kinesis services are often used for streaming data ingestion. Data can be streamed into Amazon S3, from which Snowpipe can then automatically load it into Snowflake. More recently, Kinesis Data Firehose has gained the capability to load data directly into Snowflake as a destination.   
• Kinesis Data Firehose pricing is typically based on the volume of data ingested (per GB, with tiered pricing). Optional features like data format conversion (e.g., JSON to Parquet/ORC), VPC delivery, and dynamic partitioning for S3 delivery incur additional charges. If Snowflake is configured as a direct destination for Firehose, billing is per GB of data processed to that destination, often based on the higher of ingested or delivered bytes.   
• Kinesis Data Streams pricing involves charges per shard hour, per GB of data ingested (PUT payload units), and per GB of data retrieved. Options for extended data retention (beyond the default 24 hours, up to 7 days) and long-term retention (beyond 7 days) also have associated costs.   

AWS Glue:

• AWS Glue is a serverless ETL service that can be used to prepare, transform, and load data into Snowflake.   
• Glue pricing is primarily based on Data Processing Unit hours (DPU-hours), billed per second with certain minimums depending on the job type (e.g., Spark, Python Shell, flexible execution). A DPU is a unit of processing power (e.g., 4 vCPU and 16 GB memory). Costs also apply to the AWS Glue Data Catalog (for metadata storage beyond the free tier), crawlers used to discover data schemas, and development endpoints or interactive sessions used for ETL code development.   

Utilizing AWS services like Kinesis or Glue for data ingestion or transformation before loading into Snowflake effectively shifts some of the processing cost to AWS. While this can be a strategic decision (e.g., to leverage existing AWS data pipelines or for specific transformation capabilities not easily performed in Snowflake), these AWS service costs must be factored into the total cost of ownership of the overall data pipeline feeding into Snowflake.

With Kinesis Data Firehose now supporting Snowflake as a direct destination , organizations have a choice: the direct Firehose-to-Snowflake path or the traditional Firehose -> S3 -> Snowpipe path. The direct path simplifies the architecture but has its own Firehose processing cost for the Snowflake destination. The S3 staging path involves Firehose S3 delivery costs, S3 storage and request costs, Snowpipe compute and file fees, and potentially S3 data transfer costs if cross-region. A cost-benefit analysis is needed, considering data volumes, file sizes (which heavily impact Snowpipe file fees and S3 request counts), and any data transformations performed within Firehose, to determine the most economical approach.   

E. AWS Security, Governance, and Private Connectivity

Several AWS services can be employed to enhance the security, governance, and connectivity of a Snowflake deployment, each with its own cost structure.

AWS PrivateLink:

• AWS PrivateLink enables private, secure connectivity between an organization's Amazon Virtual Private Cloud (VPC) and their Snowflake account, ensuring that traffic does not traverse the public internet.   
• To use AWS PrivateLink with Snowflake, the Snowflake Business Critical Edition (or higher) is required. This in itself is a cost factor due to the higher per-credit price of this edition.   
• AWS PrivateLink service costs include an hourly charge for each VPC Interface Endpoint provisioned in each Availability Zone, plus a per-GB data processing fee for data flowing through the endpoint (e.g., approximately $0.01/GB for the first petabyte of data processed). If the VPC endpoint and the Snowflake service are in different AWS regions, standard AWS inter-region data transfer fees will also apply to the data traversing the PrivateLink connection.   
• Implementing PrivateLink involves a higher Snowflake licensing cost, direct AWS service charges for PrivateLink itself, and potentially increased network configuration complexity (e.g., DNS management), making it a premium security option.

AWS Key Management Service (KMS):

• Snowflake supports the use of customer-managed keys (CMKs) stored in AWS KMS for an additional layer of data encryption control, often referred to as Tri-Secret Secure in Snowflake's Business Critical Edition.   
• AWS KMS costs include a monthly charge per key (e.g., $1 per CMK per month) and per-request charges for cryptographic operations (e.g., encrypt, decrypt, generate data key), typically around $0.03 per 10,000 requests after a monthly free tier.   
• If Snowflake is configured to use CMKs, frequent data loading (encrypting new data), querying of encrypted tables (decrypting data), or Snowflake's internal management of data encryption keys derived from the CMK could generate a substantial volume of API requests to AWS KMS. While individual KMS requests are inexpensive, high volumes could lead to noticeable KMS charges on the AWS bill. For example, one S3 encryption scenario involving 2 million decryption requests resulted in approximately $6 per month in KMS request fees.   

AWS CloudTrail:

• AWS CloudTrail records API activity within an AWS account. It can be used to audit interactions between Snowflake and AWS resources (e.g., S3 API calls made by Snowflake for data loading/unloading, KMS API calls if CMKs are used) and potentially some of Snowflake's own control plane actions if they manifest as AWS API calls.   
• CloudTrail costs: The first copy of management events delivered by a trail to an S3 bucket within a region is often free. However, charges apply for delivering data events (e.g., S3 object-level operations, Lambda invocations, typically around $0.10 per 100,000 events) and for additional copies of management event trails (e.g., $2.00 per 100,000 events). CloudTrail Insights events, for detecting unusual API activity, also have a separate charge (e.g., $0.35 per 100,000 events analyzed). Standard Amazon S3 storage costs apply for storing the CloudTrail logs.   

Amazon Macie:

• Amazon Macie is a data security service that uses machine learning and pattern matching to discover, classify, and protect sensitive data stored in Amazon S3. This can be relevant if S3 buckets are used for staging sensitive data before it's loaded into Snowflake, or if Snowflake queries external tables residing in S3 that contain sensitive information.   
• Macie costs are based on several dimensions: the number of S3 buckets evaluated for inventory and monitoring, the number of S3 objects monitored for automated sensitive data discovery, and the quantity of data (in GB) inspected for sensitive data discovery (with tiered pricing per GB).   
• Using services like Macie to scan S3 buckets that are part of a Snowflake data pipeline adds another layer of AWS cost specifically for data governance and security, indirectly contributing to the overall TCO of the Snowflake solution.

V. Cost Analysis of Common Snowflake on AWS Workflows

Understanding the cost implications of typical Snowflake operations on AWS requires looking at both Snowflake-side charges and the AWS services involved.

A. Data Ingestion

1. COPY INTO <table> from Amazon S3: This is a common method for bulk loading data from files in an S3 bucket into Snowflake tables.

• Snowflake Costs:
  • Virtual Warehouse Compute: The COPY command consumes compute credits from the virtual warehouse executing the operation. The number of credits depends on the data volume, file format (e.g., CSV, Parquet, JSON), data complexity, any transformations applied during the load, and the size of the virtual warehouse. Larger, more complex loads will consume more credits.   
• AWS Costs:
  • S3 GET Requests: Snowflake makes GET requests to S3 to read each file being loaded. AWS charges for these requests (e.g., ~$0.0004 per 1,000 requests for S3 Standard). If loading many small files, this can add up.   
  • S3 Data Transfer Out: If the S3 bucket is in a different AWS region than the Snowflake account's deployment region, AWS will charge S3 Data Transfer Out fees from the source S3 region to the Snowflake region. This is typically around $0.02/GB for inter-region transfers within AWS. If the S3 bucket and Snowflake are in the same region, data transfer costs from S3 to Snowflake are generally minimal or free, though cross-AZ transfers within the region can still incur minor charges ($0.01/GB each way).   
  • S3 Storage: Standard S3 storage costs apply for the files residing in the S3 bucket.

2. Snowpipe (for automated ingestion from S3): Snowpipe enables automated, continuous data ingestion from S3, typically triggered by S3 event notifications.

• Snowflake Costs:
  • Serverless Compute: Snowpipe uses Snowflake-managed serverless compute resources. Costs are based on the actual compute utilized for loading each file, often billed with a specific multiplier (e.g., 1.25 times the standard compute credit rate) or a per-hour rate for client instances in Snowpipe Streaming.   
  • Per-File Overhead Fee: A distinct charge is applied per file processed, for example, 0.06 credits per 1,000 files. This fee can dominate Snowpipe costs if ingesting a high volume of very small files.   
  • Notification/API Overhead: An overhead charge is included in utilization costs for Snowpipe, even if event notifications or REST API calls do not result in data being loaded.   
• AWS Costs:
  • S3 PUT Requests: When new files arrive in the S3 bucket that Snowpipe monitors.
  • S3 GET Requests: When Snowpipe reads the files from S3 for ingestion.   
  • S3 Event Notifications & Messaging Services: S3 event notifications (e.g., s3:ObjectCreated:*) are typically sent to Amazon SQS or Amazon SNS, which Snowpipe then polls or subscribes to. While the S3 notification itself might be free , SQS (e.g., ~$0.40 per million requests) and SNS (e.g., ~$0.50 per million publishes) have their own usage-based charges.   
  • AWS Lambda (Optional): If Lambda is used as an intermediary to process S3 events before notifying Snowpipe, Lambda execution and request costs will apply.   
  • S3 Data Transfer Out: Similar to the COPY command, if the S3 bucket is in a different region than Snowpipe's underlying compute resources (which are regionally located within Snowflake's environment), AWS S3 data transfer costs will apply.
  • S3 Storage: For the files in the S3 bucket.

When comparing COPY and Snowpipe, the choice often depends on the ingestion pattern. COPY may be more cost-effective for large, infrequent batch loads where the virtual warehouse spin-up time can be amortized over a significant data volume. Snowpipe is architecturally suited for continuous, near real-time ingestion of smaller, more frequent data arrivals. However, if Snowpipe is used with a high volume of very small files, the per-file overhead can make it expensive. Conversely, using COPY for very frequent, small loads can be inefficient due to repeated virtual warehouse 60-second minimum charges or the cost of keeping a warehouse running idly. A careful analysis of data arrival velocity, file sizes, and batching capabilities is needed to select the most cost-efficient method. Snowpipe's reliance on S3 events also means that the AWS costs for SQS/SNS notifications, S3 GET requests, and potentially Lambda must be factored into its total ingestion cost, beyond Snowflake's direct Snowpipe charges.

B. External Functions (calling AWS Lambda)

Snowflake External Functions allow SQL code to call external HTTPS endpoints, commonly AWS Lambda functions fronted by Amazon API Gateway, to perform custom processing or integrate with other services.   

• Snowflake Costs:
  • Virtual Warehouse Compute: Credits are consumed by the virtual warehouse executing the SQL query that calls the external function. The amount depends on the number of rows processed and the overhead of managing the external calls.   
  • Data Transfer: Snowflake may charge for data transferred to and from the external function if it crosses regional or cloud boundaries, though typically API Gateway and Lambda are in the same AWS region as Snowflake to minimize this. Data sent via Amazon API Gateway Private Endpoints incurs AWS PrivateLink charges for both ingress and egress.   
• AWS Costs:
  • Amazon API Gateway: Charges are typically per million requests and for data transferred out from API Gateway back to Snowflake..   
  • AWS Lambda: Charged per request and for the compute duration (GB-seconds) of the function's execution.   
  • Data Transfer (AWS internal): Costs for data transferred between API Gateway and Lambda, and potentially from Lambda if it accesses other AWS services (e.g., S3, DynamoDB).
  • CloudWatch Logs: For logging Lambda function execution.

A critical cost consideration for external functions is the invocation pattern. If an external function is called for each row in a very large table, this can lead to millions of individual API Gateway and Lambda requests. Even if the per-request cost on AWS is small, the sheer volume can result in substantial AWS charges, potentially dwarfing the Snowflake compute cost for the query itself. Similarly, the Snowflake warehouse will spend credits managing these numerous calls. Therefore, vectorizing calls—designing the external function to accept and process a batch of rows in a single invocation—is crucial for controlling costs on both platforms. The size of the data payload sent to and returned from the Lambda function also impacts costs, affecting AWS data transfer within the API Gateway/Lambda interaction and Lambda execution duration. Optimizing payload size is another key lever for cost control.

C. Snowpark Operations (Interacting with External AWS Services)

Snowpark allows developers to write data processing logic in familiar languages like Python, Java, and Scala, which can then be executed within Snowflake or interact with external services.

1. Snowpark UDFs/Stored Procedures (Non-Container Services): When Snowpark code (e.g., Python UDFs, stored procedures) runs on standard Snowflake virtual warehouses:

• Snowflake Costs:
  • Virtual Warehouse Compute: Credits are consumed by the VW executing the Snowpark code, similar to SQL workloads. The amount depends on the complexity and duration of the Snowpark job.   
  • Data Transfer (Snowflake Egress): If the Snowpark code explicitly reads large volumes of data from or writes data to external stages located in different AWS regions or different cloud providers, Snowflake's standard data egress charges apply.   
• AWS Costs (when Snowpark code interacts with external AWS services):
  • Target AWS Service Costs: If Snowpark code calls services like Amazon SageMaker for model inference, directly interacts with S3 APIs (beyond Snowflake-managed staging), or communicates with custom applications on EC2, the standard charges for those AWS services apply (e.g., SageMaker endpoint invocation fees, S3 API request charges, EC2 running costs).
  • AWS Data Transfer: Data movement between the Snowflake environment (where the VW runs) and the external AWS service will incur AWS data transfer charges. This includes data sent from Snowflake to the service and data returned. If Snowflake and the target AWS service are in different regions, AWS inter-region data transfer fees will apply. For example, if Snowpark code uses libraries like boto3 to directly access S3 buckets in the customer's AWS account (not via Snowflake stages or external tables), this constitutes application-level interaction with S3. Data read from such S3 buckets into the Snowpark environment (VW) will incur S3 GET request costs and S3 data transfer out costs (from S3 to the EC2 instance underlying the VW), which are direct AWS charges to the S3 bucket owner.

2. Snowpark Container Services (SPCS): SPCS allows deploying and running containerized applications directly within the Snowflake ecosystem.

• Snowflake Costs:
  • Compute Pool Costs: SPCS uses dedicated compute pools. Costs are based on the type (e.g., CPU, GPU, High-Memory) and number of nodes in the pool, and their active duration, billed in Snowflake credits at specific rates for SPCS node types. Compute pools incur charges when in IDLE, ACTIVE, STOPPING, or RESIZING states.   
  • Storage Costs:
    • Image Repository: The container image repository uses a Snowflake stage, so standard Snowflake stage storage costs apply.   
    • Log Storage: Logs from containers can be stored in Snowflake event tables, incurring standard table storage costs.   
    • Block Storage: If services within SPCS use block storage, charges for this block storage and associated snapshot storage apply.   
  • Data Transfer Costs: Snowflake applies its standard outbound data transfer rates for data moving from SPCS services/jobs to other cloud regions or the internet. Internal data transfer within the Snowflake ecosystem related to SPCS may also have associated costs.   
• AWS Costs:
  • By running containers within Snowflake via SPCS, the need for external AWS compute services (like EC2 or SageMaker for hosting these specific containerized workloads) might be reduced or eliminated, thereby potentially lowering direct AWS costs for those services and associated data transfer to/from them. However, if SPCS services themselves interact with other external AWS services, then charges for those external services would still apply.

Snowpark Container Services offer a potential cost shift. By bringing containerized compute (e.g., for ML model serving) closer to the data within Snowflake's environment , organizations might reduce data egress from Snowflake and the costs of running separate AWS compute infrastructure for those tasks. However, this introduces new Snowflake-specific costs for compute pools, SPCS-related storage, and SPCS data transfers. A careful TCO analysis is required to determine if SPCS provides a net cost saving compared to an architecture involving external AWS compute for the same workloads.   

VI. Identifying and Mitigating Potential Hidden Costs

Beyond the primary charges for compute, storage, and explicit AWS services, several "hidden" or easily overlooked costs can inflate the TCO of a Snowflake on AWS deployment. Awareness and proactive management are key to mitigating these.

• Cloud Services Overages: As discussed (Section II.D), the Snowflake cloud services layer is generally free up to 10% of daily virtual warehouse compute credits. However, inefficient queries (e.g., overly complex SQL, excessive joins), frequent metadata operations, or "chatty" BI tools making numerous small requests can push cloud services usage beyond this threshold, resulting in direct charges. Regularly monitoring cloud services consumption against compute credits can help identify and address these inefficiencies.   
• Inefficient Virtual Warehouse Use:
  • Over-provisioning: Using warehouses that are larger than necessary for the workload leads to wasted credits, as queries might not utilize the full capacity of the larger size.   
  • Idle Warehouses: Failing to configure appropriate auto-suspend timeouts means warehouses can remain active (and consume credits) even when no queries are running.   
  • 60-Second Minimum Impact: For workloads with very short, frequent queries, the 60-second minimum billing per warehouse start/resume can lead to paying for much more compute time than actually used.   
  • Poor Query Design: Inefficiently written queries can lead to long runtimes, excessive I/O, and high credit consumption, even on appropriately sized warehouses.   
• Suboptimal Storage Management:
  • Excessive Time Travel Retention: While Time Travel is a valuable feature, retaining data for extended periods (e.g., the default 90 days for Enterprise/Business Critical editions) for all tables, irrespective of actual recovery needs, can significantly inflate storage costs.   
  • Fail-Safe Storage: The automatic 7-day Fail-Safe period adds to the storage footprint beyond active data and Time Travel data.   
  • Orphaned Data: Not cleaning up temporary tables, old staging tables within Snowflake, or cloned objects that are no longer needed can lead to paying for unused storage.
  • Unmanaged S3 Staging Areas: For data staged in S3, failing to implement lifecycle policies to archive or delete old files can result in ongoing S3 storage charges for data that is no longer relevant.
• Unmonitored Serverless Features: The ease of enabling serverless features like Materialized Views, Search Optimization Service, or Automatic Clustering can lead to continuous background credit consumption. If the cost-benefit of these features isn't regularly assessed, they can become significant hidden expenses.   
• Data Transfer Inefficiencies:
  • Unnecessary Data Movement: Moving data across AWS regions or between cloud environments when it could be processed or accessed locally incurs avoidable data transfer charges from both Snowflake and/or AWS.   
  • Loading Uncompressed Data to S3: While Snowflake compresses data upon ingestion, loading large volumes of uncompressed data into S3 staging areas first will increase S3 storage costs and the volume of data transferred from S3 to Snowflake (though Snowflake only bills for compressed storage internally).
  • Frequent, Small Transfers: Opting for many small data transfers instead of batching them can increase overhead and potentially per-request charges on services like S3 or API Gateway.
• Snowpipe Small File Penalty: As highlighted (Section II.E), Snowpipe's per-file overhead charge can lead to disproportionately high ingestion costs if processing a large number of very small files.   
• Zombie Assets: This refers to resources like unused virtual warehouses that are not suspended, databases that are no longer queried but still incurring storage costs, or scheduled tasks that continue to run and consume credits even if their output is no longer needed.

Many of these hidden costs arise from a lack of granular visibility into usage patterns or a misunderstanding of how specific Snowflake and AWS features consume resources and interact. For instance, users might not realize the storage impact of long Time Travel windows or the cumulative compute cost of multiple serverless features running concurrently. Consistently exceeding the cloud services threshold often signals underlying query or workload inefficiencies that need investigation. The pay-as-you-go model means every small inefficiency, if replicated across many users or processes, can compound over time, leading to significant and unexpected budget overruns. Proactive monitoring and regular cost audits are essential to uncover and address these hidden drains on the budget.   

VII. Strategic Recommendations for Optimizing Total Cost of Ownership (TCO)

Effectively managing the TCO of Snowflake on AWS requires a multi-pronged strategy encompassing resource optimization within Snowflake, careful management of associated AWS services, and continuous monitoring.

A. Right-Sizing and Managing Virtual Warehouses:

• Analyze Workloads: Match virtual warehouse (VW) sizes to specific workload requirements. Start with smaller sizes for new workloads and scale up only if performance dictates, rather than over-provisioning by default.   
• Dedicated Warehouses: Use separate VWs for distinct workloads (e.g., data loading, BI querying, data science transformations). This allows tailoring the size, auto-suspend settings, and concurrency configurations (like multi-cluster warehouses for Enterprise Edition and above) to each workload's specific needs, preventing resource contention and optimizing credit usage.   
• Monitor Query Performance: Regularly review query history to identify long-running or inefficient queries. Optimize SQL, consider clustering keys for tables, or use features like the Search Optimization Service or Query Acceleration Service where appropriate and cost-effective.
• Judicious Auto-Suspend/Resume: Configure auto-suspend timeouts based on actual workload patterns. For interactive BI workloads, a slightly longer timeout (e.g., 5-15 minutes) might preserve data cache and improve user experience, potentially avoiding the need for larger warehouses. For batch ETL jobs, more aggressive suspension can be used. Balance cache warmth against idle credit consumption.   

B. Optimizing Data Storage Costs:

• Leverage Snowflake Compression: Snowflake automatically compresses data, which significantly reduces storage footprint and costs. Ensure data types are appropriate to maximize compressibility where possible.   
• Manage Time Travel and Fail-Safe: Configure Time Travel retention periods based on actual business recovery point objectives (RPOs) for different datasets. Avoid global default settings of 90 days if not needed for all data, as this can drastically increase storage. Be aware of the 7-day Fail-Safe storage that adds to the total.   
• Data Lifecycle Management:
  • Within Snowflake: Regularly identify and drop or archive (e.g., to external stages) unused or temporary tables, schemas, and databases.
  • For S3 Staging: Implement S3 Lifecycle policies to transition older staging files to lower-cost storage tiers (e.g., S3 Standard-IA, S3 Glacier) or delete them after successful ingestion and validation. Ensure transition rules align with data access needs to avoid costly early retrievals from archive tiers.   
• Consolidate Small Files for Ingestion: Especially when using Snowpipe, pre-process or batch small files into larger ones (e.g., 100MB-250MB or larger) before they reach the S3 staging area. This dramatically reduces Snowpipe's per-file overhead charges and can also lower S3 request costs.   

C. Controlling Data Transfer Costs:

• Regional Strategy: Co-locate your Snowflake deployment, primary data sources (e.g., S3 buckets for staging), and primary data consumers (applications, BI tools) within the same AWS region to minimize inter-region data transfer charges from AWS.   
• Optimize Data Movement: Avoid unnecessary data transfers out of Snowflake or across AWS regions. Perform transformations within Snowflake whenever possible before exporting data.
• Use PrivateLink Strategically: If private connectivity is required and the Snowflake Business Critical Edition is justified, AWS PrivateLink can eliminate public internet exposure. However, factor in its associated AWS costs (endpoints, data processing) and the higher Snowflake edition cost.   
• Snowflake Egress Cost Optimizer (ECO): For scenarios involving sharing the same datasets to multiple regions/clouds via Snowflake's auto-fulfillment, evaluate if ECO can reduce Snowflake's egress charges, keeping in mind its own cache storage cost.   

D. Managing Serverless Feature Costs:

• Monitor Usage: Actively track the credit consumption of serverless features like Automatic Clustering, Materialized View maintenance, Search Optimization Service, and Snowpipe using Snowflake's ACCOUNT_USAGEviews.   
• Assess Cost-Benefit: Regularly evaluate whether the performance benefits or automation provided by these features justify their ongoing credit consumption. Disable or adjust configurations for features that are not providing sufficient value. For example, Search Optimization might not be needed on rarely queried archival tables.
• Optimize Snowpipe: Beyond file consolidation, ensure Snowpipe configurations (e.g., COPY options within the pipe definition) are efficient.

E. Proactive Monitoring, Alerting, and Governance:

• Snowflake Usage Monitoring: Regularly query Snowflake's ACCOUNT_USAGE and ORGANIZATION_USAGE schemas (e.g., WAREHOUSE_METERING_HISTORY, STORAGE_USAGE, QUERY_HISTORY, PIPE_USAGE_HISTORY, USAGE_IN_CURRENCY_DAILY) to gain granular insights into credit consumption, storage trends, and costs by service type or account.   
• AWS Cost Management Tools: Utilize AWS Cost Explorer, AWS Budgets, and the AWS Cost and Usage Report (CUR) to track Snowflake Marketplace charges and all associated AWS service costs (S3, Data Transfer, Lambda, Kinesis, etc.).   
• Set Budgets and Alerts: Establish budgets in AWS for overall cloud spend and specifically for services heavily used with Snowflake. Set up alerts in both AWS and potentially within Snowflake (using tasks to monitor usage tables) to be notified of unexpected cost spikes or when thresholds are approached.   
• Tagging and Cost Allocation: Implement a consistent tagging strategy for Snowflake resources (where possible) and AWS resources to attribute costs back to specific projects, teams, or applications. This aids in showback/chargeback and identifying areas for optimization.
• Regular Reviews: Conduct periodic cost optimization reviews, involving stakeholders from data engineering, finance (FinOps), and business units, to ensure alignment with budget and identify new optimization opportunities.

F. Understanding AWS Marketplace Billing:

• Reconcile Bills: Regularly reconcile the Snowflake charges appearing on your AWS invoice (and detailed in the CUR) with the usage data from Snowflake's ACCOUNT_USAGE views, particularly USAGE_IN_CURRENCY_DAILY.Understand that the AWS Marketplace "$0.01 unit" is a financial aggregation of your total Snowflake spend.   
• Review Contract Terms: If on a capacity commitment or private offer via AWS Marketplace, understand the terms, duration, and any overage rates.

By adopting these strategic recommendations, organizations can gain better control over their Snowflake and associated AWS infrastructure costs, ensuring that the platform delivers value efficiently and sustainably.

VIII. Conclusion

Deploying Snowflake on AWS through the AWS Marketplace provides a powerful and scalable data cloud solution, but it necessitates a comprehensive understanding of a multi-layered cost structure. The total cost of ownership extends beyond Snowflake's direct charges for compute credits and storage, encompassing a range of AWS infrastructure costs related to data transfer, ancillary storage on S3, compute for services like Lambda or Kinesis, and security or private connectivity solutions.

Key takeaways for managing these costs include:

1. Holistic Cost View: Recognize that Snowflake and AWS costs are interconnected. Optimizing one often impacts the other. A holistic view is essential for effective TCO management.
2. Snowflake Core Costs: The choice of Snowflake edition fundamentally dictates per-credit compute costs. Virtual warehouse sizing, auto-suspend policies, and management of data retention features like Time Travel are critical levers for controlling Snowflake-specific expenses. Serverless features, while convenient, require monitoring to prevent "feature creep" and associated cost accumulation, with Snowpipe's per-file charges being particularly sensitive to ingestion patterns.
3. AWS Infrastructure Costs: Data transfer is a significant and often underestimated AWS cost. Careful planning of data source and consumer locations relative to the Snowflake deployment region is crucial. S3 storage and request costs for staging, Kinesis/Glue for ingestion, Lambda for external functions, and services like PrivateLink or KMS for security all contribute to the AWS bill and must be factored in.
4. Marketplace Billing: While AWS Marketplace offers billing consolidation, the "$0.01 per unit of usage" for Snowflake requires reconciliation against Snowflake's more granular internal usage reports (ACCOUNT_USAGEschema) and the AWS Cost and Usage Report (CUR) to fully understand and attribute charges.
5. Proactive Optimization: Continuous monitoring of both Snowflake and AWS usage, right-sizing resources, optimizing queries and data loading processes, and implementing effective data lifecycle management are not one-time tasks but ongoing disciplines. Leveraging tools like Snowflake's usage views and AWS Cost Explorer, and setting up budgets and alerts, are vital for identifying and mitigating potential hidden costs and inefficiencies.

Ultimately, maximizing the value of Snowflake on AWS involves a diligent approach to cost management, characterized by informed architectural decisions, continuous monitoring, and a commitment to ongoing optimization across both platforms. By understanding the nuances of each cost component and implementing best practices, organizations can ensure their Snowflake deployment remains both powerful and economically efficient.

Further Readings

Snowflake & AWS Pricing References

• AWS Marketplace: Snowflake Data Cloud - Amazon.com
• Snowflake Pricing Explained: A 2024 Usage Cost Guide - CloudZero
• Snowflake Pricing Explained | 2025 Billing Model Guide - SELECT.dev
• Snowflake Software Pricing & Plans 2025: See Your Cost - Vendr
• Snowflake Pricing Explained: Compute, Storage, and Beyond - Cloudchipr
• Snowflake Pricing 101: A Comprehensive Guide (2025) - Chaos Genius
• Pricing Options - Snowflake
• Snowflake Pricing Breakdown in 2025: Guide & Hidden Costs - Qrvey
• AWS PrivateLink and Snowflake
• Snowflake Pricing, Explained: A Comprehensive 2025 Guide to Costs & Savings - Keebo.ai
• Snowflake Pricing Explained: Models, Features, and Cost-Saving Tips - CloudOptimo
• Snowflake Editions
• Understanding overall cost | Snowflake Documentation
• Snowflake On AWS - Pricing and Architecture Best Explained
• How to Optimize Snowflake Costs: Best Practices for 2025 - Sedai
• What Are the Benefits of Using Snowflake on AWS? - Secoda
• Snowflake Cloud Services and Adjustments - Cloud Cost Handbook
• Snowflake Snowpipe: The Definitive Guide (2024) - SELECT.dev
• Snowpipe costs | Snowflake Documentation
• Snowpark Container Services costs - Snowflake Documentation
• Subscribing with SaaS usage-based subscriptions - AWS Marketplace
• Pricing for SaaS subscriptions - AWS Marketplace
• Pricing for SaaS contracts - AWS Marketplace
• Understanding your bill - AWS Documentation
• Understanding unexpected charges - AWS Billing Documentation
• AWS Billing and Cost Management Explained: Features and Process - nOps
• Viewing and managing reports - AWS Cost & Usage Reports
• Cost & billing - Snowflake Documentation
• Reconcile a billing usage statement - Snowflake Documentation
• Snowflake Marketplace | Snowflake AI Data Cloud
• Snowflake Data Transfer Costs 101—An In-Depth Guide (2025) - Chaos Genius
• COPY FILES | Snowflake Documentation
• CREATE STORAGE INTEGRATION - Snowflake Documentation
• EC2 On-Demand Instance Pricing – Amazon Web Services
• What is AWS data transfer pricing? | AWS bandwidth pricing - Cloudflare
• AWS Data Transfer Pricing & Saving | CloudBolt Software
• AWS S3 Pricing - GeeksforGeeks
• A Look Into AWS S3 Pricing Models - CloudOptimo
• AWS Product and Service Pricing | Amazon Web Services
• The Ultimate Guide to Amazon S3 Pricing 2025 (Updated) - Cloudchipr
• Snowflake in One Region, AWS bucket in another. Costs? - Stack Overflow
• AWS PrivateLink Pricing
• Leveraging AWS PrivateLink for volumetric data processing | AWS Networking & CDN Blog
• Expand to More Regions and Clouds with Zero Additional Egress Cost - Snowflake Blog
• Stream Data from Kinesis to Snowflake | Estuary
• Reliable event processing with Amazon S3 Event Notifications | AWS Storage Blog
• Amazon S3 Pricing - Cloud Object Storage - AWS
• How to Leverage AWS CloudTrail to Stay Compliant and Secure in the Cloud? - CloudOptimo
• Serverless Computing – AWS Lambda Pricing – Amazon Web Services
• Snowflake Cost Monitoring with AWS CloudWatch & External Functions - Cloudyard
• Introduction to external functions - Snowflake Documentation
• Amazon EC2 Dedicated Instances Pricing - AWS
• Amazon Data Firehose Pricing - Streaming Data Pipeline - AWS
• Amazon Kinesis Data Streams Pricing - AWS
• Amazon Kinesis Video Streams Pricing - AWS
• Serverless Data Integration – AWS Glue Pricing
• AWS Glue Pricing Breakdown: The Comprehensive Guide for 2025 - Cloudchipr
• Configuring Snowflake and AWS PrivateLink | dbt Developer Hub
• Pricing | AWS Key Management Service (KMS)
• pricing encrypting RDS with KMS - Stack Overflow
• CloudTrail Pricing Guide: Save On AWS Logging - CloudZero
• AWS CloudTrail pricing
• Sensitive Data Discovery – Amazon Macie Pricing - AWS
• Amazon Macie Guide: Discover & Protect Sensitive Data on AWS - Cloudchipr
• Understanding compute cost | Snowflake Documentation
• 6 Common Problems Faced by Snowflake Customers - Revefi
• An Analysis of AWS and Snowflake's Role in Cloud-Based Policy Management Solutions - PhilArchive
• Usage dashboard - AWS Marketplace