Keebo | Snowflake Pricing, Explained: A Full Breakdown of Costs + Savings Opportunities for 2025

Snowflake Pricing, Explained: A Full Breakdown of Costs + Savings Opportunities for 2025

Let’s not beat around the bush: Snowflake pricing is complicated. Multiple factors determine your monthly spend—pricing tiers, warehouse sizes, query latency, credit discounts, and more. Some of those factors are set in your contract. Others change based on usage and performance. 

Before you can control your Snowflake spend, it helps to know where that spend is going. In this guide, we’ll break down exactly how Snowflake pricing works (with examples) and the best strategies for cost optimization.  

Before you start: What is Snowflake? 

Snowflake is a cloud-based data platform for storing, analyzing, and sharing data. It’s a fully managed platform, which means users can operate 100% of its features without accessing its underlying infrastructure. 

Snowflake sits on top of all the “big three” cloud providers—Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.

Users cite a range of reasons for why they chose Snowflake over its competitors: 

  • Elasticity and scalability. Snowflake is built to scale up and down in response to changing workloads. This is helpful for users with dynamic user demands, as well as companies anticipating rapid growth. 
  • UI/UX. Snowflake runs on an intuitive UI that fosters an overall positive user experience. Most commands run using SQL, although the Snowpark API enables developers to write code in any language of their choice. 
  • Decoupled architecture. Data storage, compute, and cloud services are managed and priced separately, enabling freedom and flexibility.
  • Compatibility and integration. Because Snowflake is widely recognized and used across the IT industry, it easily integrates with other cloud platforms. 
  • Marketplace. In the same vein, Snowflake offers a robust marketplace of apps, skills, and public data sets to customize each instance to the user’s preferences. 

How is Snowflake pricing calculated? 

Snowflake pricing isn’t based on a flat rate or monthly fee. Instead, the platform employs a usage-based pricing model with three components: 

  • Storage costs are calculated based on a flat rate per terabyte (TB) of both compressed and uncompressed data. These data can be used for bulk unloading or loading, historical data maintained for File-safe, and database tables.
  • Compute costs are incurred any time you consume Snowflake credits—performing queries, loading data, or other DML operations. These fall into three categories: virtual warehouses, serverless, and cloud services. 
  • Data transfer costs are incurred when you transfer data from a Snowflake account to a different region, cloud provider, or both.

Each of these pricing components has its own nuances and complexities. Let’s take a closer look at each. 

Snowflake storage cost

Snowflake stores data in micro-partitions that sit in the underlying cloud provider’s cloud storage layer (e.g. AWS, GCP, Azure). Micro-partitions are contiguous units of storage used to organize and manage data within Snowflake tables, and vary in size from 50 to 500 MB.

Pro tip: Since micro-partitions can achieve up to a 3:1 compression ratio, it’s usually more cost effective to store your data with Snowflake than another solution.

Data included in Snowflake storage costs includes:

  • Files staged for bulk loading/unloading (both compressed or uncompressed)
  • Database tables
  • Historical data stored for Time Travel
  • Fail-safe for database tables
  • Clones of database tables that reference data deleted from their reference tables

Snowflake then calculates data storage costs based on the number of on-disk bytes within those micro-partitions. The exact cost varies by region, underlying cloud platform, pricing tier and whether the account is Capacity or On-Demand. 

Generally speaking, expect data storage costs to be less expensive in the U.S. than in Europe or Asia. Likewise, Capacity storage prices are significantly less than On-Demand. In one example, we’re talking a difference between $40/TB and $23/TB—which is significant when you tally it up across your database. 

Snowflake warehouse pricing

Snowflake runs its data operations on virtual warehouses. These vary in size based on the resources provisioned to them. Thankfully, Snowflake warehouse sizes are easy to follow—as easy as T-shirt sizes. Each increase in size corresponds to a 2X increase in the hourly credits consumed.

Warehouse sizeCredits per hour
X-small1
Small2
Medium4
Large8
X-large16
2X-large32
3X-large64
4X-large128
5X-large256
6X-large512

We’ll get into more specifics on virtual warehouse pricing in our section on Snowflake credits below. 

Some other key considerations when calculating warehouse costs: 

  • Warehouses only consume Snowflake credits while running, not while suspended or idle
  • Snowflake always charges for the first 60 seconds of warehouse activity, regardless of whether the warehouse is active during that time. For larger warehouses, this can be a significant source of wasted spend. 
  • Snowflake performance is typically linear, so doubling a warehouse’s size usually results in a 50% reduction in a given task’s processing time for the same cost
  • As long as a warehouse is running, Snowflake caches query information, enabling subsequent queries to run faster. If the warehouse is suspended, that cache is lost and the query takes longer to run. 

Since warehouses form the biggest cost center for most Snowflake instances, adjusting warehouse settings is one of the most impactful ways to optimize Snowflake spend. 

Snowflake serverless costs

Snowflake offers a range of serverless compute services that consume their own credits separately from virtual warehouses. These include: 

  • Snowpipe (i.e. automatic file loading requests)
  • Automatic clustering
  • Data quality monitoring
  • Replication
  • Search optimization
  • Materialized views

Snowflake charges for these serverless services based on a set number of credits per hour: 

FeatureCompute Credits per hourCloud Services Credits per hour
Clustered tables21
Copy files2n/a
Logging1.251
Materialized views maintenance105
Materialized views maintenance (secondary databases)21
Query acceleration11
Replication21
Search optimization service 105
Search optimization service (secondary databases)21
Serverless tasks1.51
Snowpipe1.25n/a — charged 0.06 credits per 1,000 files
Snowpipe streaming1n/a – charged at 0.01 credits per instance

Snowflake cloud services costs

Snowflake’s cloud services layer handles all the platform’s functionality except for the actual storing and processing of data. This includes authentication, infrastructure management, metadata management, query compilation, access control, zero-copy cloning, and more. 

As long cloud services usage doesn’t exceed 10% of daily platform usage, they incur no additional cost. So if a user consumes 150 total credits but only 10 of those are for cloud services, Snowflake doesn’t charge for the ten. 

In our experience, the vast majority of Snowflake users never exceed that 10% threshold, so cloud services have a negligible—if any—impact on overall cost. 

Snowflake data transfer costs

Every Snowflake account runs in one of 35 distinct regions based on geographic locations. Additionally, as mentioned above, Snowflake supports the three big cloud providers. 

Pro tip: Any time you transfer data across regions, cloud providers, or both, you incur data transfer costs. As such, most Snowflake customers will choose the same region and cloud provider for their primary account. 

Some things to keep in mind when considering Snowflake data transfer costs:

  • Snowflake doesn’t charge to bring data into the platform (ingress), but only to move it out (egress)
  • Transferring data within the same region or cloud provider incurs no data transfer costs
  • Not all Snowflake functions incur data transfer costs (here’s a full list of applicable functions)

Snowflake data transfer costs are charged on a fee-per-byte basis. 

Snowpark container services costs

Last year, Snowflake launched its fully managed container offering: Snowpark Container Services (SPCS). With SCPS, users can run containerized workloads directly within Snowflake. 

Instead of virtual warehouses, SPCS runs on top of Compute Pools. As such, there’s a slightly different pricing structure (the rates below are all credits per hour): 

Compute Node TypeXSSML
CPU0.110.220.431.65
High-Memory CPUn/a0.562.228.88
GPUn/a1.145.3728.25

Snowflake AI services costs

As of now, Snowflake offers two types of AI services: Document AI and Cortex AI. Each is priced differently, so let’s consider each of them separately.

Document AI

Snowflake’s Document AI is an LLM-powered model that extracts information from documents. This enables faster and continuous processing of new documents of a specific type (e.g. purchase orders, invoices, reports)

Snowflake automatically scales compute resources up and down for each Document AI workload. Simply put, the amount you spend on Document AI is based on time spent, calculated on the per-second basis. 

A number of factors play into how many credits Document AI might consume:

  • Number of pages in the document
  • Number of documents
  • Page density (i.e. how much information is included on the page—this directly correlates to the amount of time it takes to process the document) 

Here are some examples of how these factors all come together to determine the total credits consumed: 

Number of documentsNumber of pages per documentPage densityEstimated credit range for 10 valuesEstimated credit range for 20 valuesEstimated credit range for 40 values
10100Low3-54-66-8
10010Low5-77-1010-12
1,0001Low10-1211-1312-14
10100Medium4-67-912-14
10010Medium7-910-1216-18
1,0001Medium10-1212-1415-17
10100High5-79-1116-18
10010High8-1012-1421-23
1,0001High11-1313-1517-19

Snowflake Cortex

Snowflake Cortex includes a suite of services leveraging LLMs: text completion, generation, summarization, language translation, extract answer, sentiment analysis, text embed, and more. Pricing is calculated on a token-based system, with each service consuming tokens a different rate: 

Cortex ServiceCredits Consumed Per 1M Tokens
Cortex Complete (reka-core)5.50
Cortex Complete (mistral-large)5.10
Cortex Complete (llama3-70b)1.21
Cortex Translate0.33
Cortex Summarize0.10
Cortex Extract Answer0.08
Cortex Sentiment0.08
Cortex Embed Text 7680.03

What are Snowflake credits and how are they priced? 

We’ve mentioned Snowflake credits enough times now that we should probably stop and answer the question: what are Snowflake credits and how are they priced? 

Snowflake credits are the virtual “currency” used to measure and charge for compute resources. In nearly every case, they correspond linearly to provisioned resources. However, the exact rate varies based on which service is in question.

Snowflake credits are priced based on which pricing tier (called “Snowflake Editions”) you use. Here’s a breakdown of the average On-Demand price for each Edition: 

StandardEnterpriseBusiness CriticalVPS (Virtual Private Snowflake)
$2.00 – $3.10$3.00 – $4.65$4.00 – $6.20$6.00 – $9.30

So if you’re on an Enterprise tier and run 100 Medium warehouses for 42 minutes, assuming you’re on the lower end of that range, it’ll cost $840 to run those warehouses. 

You can provision credits in Snowflake in one of two ways: On-Demand or Capacity

On-Demand

On-Demand is a true pay-as-you-go model. Snowflake only charges you for the credits you consume. If you’re unsure about how many credits you need, or are confident your usage needs are small, On-Demand is often the best choice.  

Pros

Your Snowflake spend is directly proportional to your usage. This can be helpful if you don’t know exactly how much compute power you need. 

Cons 

Snowflake workloads are dynamic and difficult to predict. You may think you have a small workload, but end up with inefficient queries, high latency, and other factors that drive up your credit usage. On-Demand also has a higher per-credit cost. When applied across large workloads, it can significantly increase your Snowflake spend. 

Capacity

When you purchase credits at Capacity, you’re essentially pre-buying a set number of credits, typically based on anticipated usage. These credits have a lower price than On-Demand. However, if you exceed your Capacity, each additional credit over that limit is charged at On-Demand rates. 

Pros

The biggest pro is a lower per-credit price when you buy at Capacity. If you have predictable workloads or anticipate a certain level of usage over the course of your contact, this can be a great way to keep costs under control. 

Cons

If your usage projections are too high, you may end up over-provisioning credits you don’t end up using. If they’re too low, you’ll have to provision more credits at the On-Demand price. Finding the right balance is a delicate exercise, made no easier by the dynamic nature of cloud environments. 

Additionally, if your usage spikes unexpectedly, you could end up using more credits than you bought, dramatically increasing your costs. This likely won’t go over well with your CFO. 

What are the Snowflake pricing tiers?

As we showed in the chart above, the Snowflake pricing model varies based on which Edition you use. Each Edition has its own features that work well for different use cases, business sizes, and IT configurations. 

Snowflake Standard Edition

This entry-level pricing tier works for small organizations and companies with relatively basic data warehousing needs. Users on the Standard Edition gain access to basic functionality: virtual warehouses, data loading, query processing, etc. In terms of security and performance, however, it’s the bare minimum. 

Snowflake Enterprise Edition

For larger organizations with more complex needs, the Enterprise Edition is a popular choice. Its security features are far more advanced than the Standard Edition, and offers a suite of enhanced performance features.

Snowflake Business Critical Edition

Organizations with highly sensitive data (like healthcare companies that need to guarantee HIPAA compliance) should seriously consider using Business Critical Edition. This Snowflake tier not only includes stringent security and regulatory requirements, but also multi-cluster support and database failover/failback adds support for disaster recovery.

Snowflake Virtual Private Snowflake (VPS) Edition

This top-of-the-line Snowflake tier offers maximum security through its private network configuration. VPS accounts share no hardware with any accounts outside the VPS. If you’re in a highly regulated industry and need the utmost data protection, this is the tier for you. 

Snowflake pricing example

Now that we’ve laid out all the details of how Snowflake pricing works, let’s see how all these components fit together in one example. 

Let’s consider a company that buys a set of 4,000 Capacity monthly credits on the Enterprise plan, deployed on AWS in the US East Region. If Capacity gets the user a 20% discount, we’re looking at an adjusted price of $2.40 per credit, so $9,600 total. 

Additionally, this user stores an average of five TB of compressed data. At $23 per TB, you’re looking at $115 in data storage. So now we’re spending $9,715 per month. 

In terms of usage, here’s a breakdown of your anticipated usage:

  • 10 Small warehouses running an average of six hours per day—using 3,600 credits per month
  • A batch loading job on 1 XS warehouse, running two hours per day—using 60 credits per month
  • A reverse ETL job to query and sync warehouse data for 45 minutes per day on 1 Small warehouse—using 45 credits per month
  • Your cloud services usage never exceeds the 10% threshold, so the charge is $0 per month

If you add the total up, you’re using 3,705 credits per month, giving you 295 “free” credits to handle minor fluctuations in usage and spend. Overall, your Capacity planning is tightly aligned with your usage and few resources are wasted.

However, consider what would happen if you had a sudden influx of users you did not anticipate. Let’s say that you also have a performance SLA in place that requires you to scale up warehouses to maintain desired query latency levels—from a Small to a Large. Let’s say this happens three times a day, every other day, for an average of 10 minutes per influx.

You end up consuming an additional 601 credits that you didn’t account for in your Capacity Planning. 295 of those are covered, but there’s an extra 306 you have to pay for at full price.

So, given this scenario, you’ll end up paying the following per month for Snowflake:

Capacity Credits$9,600
Non-Capacity Credits$918
Database Storage$115
Cloud Services$0
Total Spend$10,633

By comparison, if you planned to include those 306 credits in your Capacity planning and provisioned a flat 4,305 credits total, you’d spend $10,435 and save $198 per month ($2,376 per year).

Obviously, if your Capacity planning is way off the mark in either direction, you’re going to end up with more extreme overspending. That’s why it’s important to keep a close eye on your usage to make sure you don’t spend more than necessary. 

Snowflake cost optimization: best practices

Despite its overall affordability compared to other alternatives (more on that below), Snowflake costs can get out of control more easily than you might think. There are a number of reasons why is the case: 

  • Over- or under-provisioning at Capacity
  • Lack of real-time insight into performance, which leads to the inability to proactively manage resources
  • Limited time and resources available for optimization—both due to internal demands and the simple fact that DBAs can’t be available 24/7
  • Poor usage: inefficient queries, improper warehouse sizing, running queries on the wrong warehouse, etc. 

The fact that Snowflake pricing is tied to usage is a double-edged sword. On the one hand, there are no “hard costs,” which means you can use the platform as little as possible and pay little. On the other hand, there are virtually no upper limits—so it’s easier to exceed your budgets. 

While Snowflake does have a native resource monitor tool that automatically shuts down your warehouses when they reach a certain credit limit, this solution has some drawbacks. If you have a service-level agreement that demands certain access to data and performance benchmarks, you can’t just shut Snowflake down at an arbitrary point. In reality, you’d let the warehouses run, and pay the piper later.

Although Snowflake’s native cost control mechanisms are limited, there are plenty of strategies for keeping Snowflake costs under control. 

Warehouse optimization 

Because Keebo doubles credit consumption for each subsequent warehouse size, running small queries on a large warehouse can burn through credits too quickly. At the same time, running large queries on smaller warehouses increases latency, which not only hurts performance but increases your query spend. 

Warehouse optimization is a category that covers a range of tactics centered around one goal: provisioning only the resources you need to maintain desired performance levels—no more, no less.

Rightsizing your virtual warehouses

The first warehouse optimization approach is rightsizing your virtual warehouse to match incoming workloads. Generally, you want to keep your queries running on as small a warehouse as possible until it starts to negatively impact cost or performance. 

Let’s compare two examples to see how the duration of a query can affect which warehouse size you should select: 

Warehouse SizeCredits Per HourQuery Execution Time (hours)Total Credits Consumed
S20.180.36
XL160.020.32

In that example, then, the cost savings ended up favoring the XL warehouse. But let’s consider another case:

Warehouse SizeCredits Per HourQuery Execution Time (hours)Total Credits Consumed
S20.0140.033
XL160.0010.267

Because Snowflake has a 60-second minimum for query execution, scaling up to an XL, while speeding up query performance, consumes more credits. 

These decisions happen seconds at a time, at every hour of the day. Spread that out across dozens or hundreds of warehouses, and it’s impossible for a human engineer or DBA to keep up with them. See the image below for an illustration of how this works: 

Keebo | Snowflake Pricing, Explained: A Full Breakdown of Costs + Savings Opportunities for 2025

That’s one reason we recommend using an AI-powered tool to handle this. That way, your engineers are freed up to perform more value-add tasks and projects. 

Reducing auto-suspend

As mentioned above, Snowflake only consumes credits when a warehouse is actively running. By auto-suspending warehouses after a designated period of inactivity (say, 30 seconds), you can keep costs under control. But there are some problems that arise when you set auto-suspend too low.

When a Snowflake warehouse is proactively running, it maintains a cache of table data accessed by recent queries. As long as the warehouse remains active, it can access this cache and run subsequent queries on the same table faster. 

However, if you set your auto-suspend too low, you could end up with a thrashing warehouse constantly going back and forth between suspend and active. Each time the warehouse suspends, you lose your cache, and queries take longer to execute as they’re constantly pulling data from cold storage. 

An alternative is a predictive approach to auto-suspend. There’s a manually set maximum upper limit, but if the AI determines based on predictive analysis that there will be no incoming queries within that limit, it can shut down the warehouse sooner and save those credits. 

Proactive suspension

But there’s another problem with auto-suspend that ends up causing problems. Despite Snowflake’s front-end ability to adjust auto-suspend down to the second, in reality Snowflake auto-suspends every 30 seconds. So if you set your value to 15 seconds, the warehouse is really suspended after 30. If you set it to 31 seconds, it’s suspended after 60.

Obviously this results in lots of unnecessary activity and eats into your credit usage. Fortunately, there’s an ALTER WAREHOUSE command that will force a warehouse to suspend itself once all queries have run. 

The problem is that manually forcing suspension is, like manual warehouse rightsizing, infeasible. Using an AI algorithm that enforces your defined force suspend requirements, however, can help you realize more savings than Snowflake natively offers. 

Query routing

Another way to control Snowflake spend is to focus less on warehouses, and more on workload configuration. One of the most effective workload configuration tactics is query routing. 

With query routing, you send queries to the warehouses with the resources needed to handle them. In other words, small queries go to small warehouses, medium to medium, large to large, etc. This enables you to decouple app logic from warehouse logic and ensure you’re using every single credit you’ve provisioned. 

For example, let’s say you have a warehouse that runs 90% “small” queries. Ideally, you’d keep that warehouse at a Small. But that other 10% consists of “XL” queries which, for app logic reasons, cannot be run on another warehouse. Query routing enables you to take those “XL” queries and route them to an XL warehouse, enabling you to keep the warehouse connected to the app at a Small. 

Other examples of how query routing works include: 

  • Knowing that there are five queries already running in a warehouse, we can route new queries to a different warehouse
  • Knowing that the last query ended more than five minutes ago (which is greater than the auto-suspend value), we might conclude that the warehouse is suspended and decide not to wake it up to run a query on a different warehouse
  • Keebo can combine query history-based metrics (e.g. average bytes scanned by similar queries) and real-time query and system information when making routing decisions

Workload intelligence

Finally, there are a range of decisions that require comprehensive Snowflake observability: such as systemically bad query writing and poor clustering. You need to know they’re happening quickly, so you can take action as soon as possible. 

That’s where workload intelligence comes in, providing robust Snowflake observability with real-time, actionable FinOps insights. Much more than just a dashboard for tracking various Snowflake metrics, it provides you with real-time insights, recommended actions and, more importantly, the estimated impact of implementing those actions.

Taken together, warehouse optimization, query routing, and workload intelligence will maximize your savings potential. Keebo is the only platform that offers all three—schedule a demo to see it in action now

Snowflake alternatives: comparisons & pricing

According to 6sense, Snowflake is the industry leader in the data warehousing space, with a market share of over 20 percent. However, the platform isn’t without its challengers. 

If you’re wondering how Snowflake stacks up against the competition—especially in terms of pricing—read on for our platform comparisons. 

Snowflake vs. Databricks: pricing & features

No question, Snowflake’s biggest competitor is Databricks. While Snowflake is easy to use and scale, Databricks is built for more advanced data science and AI capabilities. 

One key difference is that while Snowflake can handle unstructured data, Databricks has the flexibility to do it well. Another is the programming languages used. Snowflake is optimized around SQL (although it allows other languages), while Databricks has better support for other languages—Python, Scala, R, etc. On the flip side, Snowflake’s UI is more straightforward and easier to use. 

In terms of pricing, Snowflake has the simpler pricing model. Databricks charges by workload type and cloud provider: 

Workload TypeStarting Price per DBU (Databricks Unit)
Workflows$0.15
Delta Live Tables$0.20 (Core), $0.25 (Pro), $0.36 (Advanced)
Databricks SQL$0.22
Interactive Workloads$0.40
Mosaic AI$0.07

Because Snowflake and Databricks price on completely different bases, a direct comparison is impossible without more in-depth information about the workload in question. However, Snowflake generally tends to be the less expensive option, especially for organizations with consistent storage requirements.

Snowflake vs. AWS RedShift: pricing & features

Although lumped together in the broad “data warehouse” category, Snowflake and AWS RedShift have some key differences:

  • Snowflake separates compute from storage, while RedShift couples the two and requires manual node management
  • Snowflake auto-scales compute resources (warehouses, cloud, etc.), while RedShift requires you to add and remove nodes manually with concurrency scaling costing extra
  • Snowflake supports all data types, while RedShift only supports unstructured data when used with Amazon S3

Another major difference between the two platforms is their price structure. RedShift charges per hour per node, bundling compute and data storage into one price. For many use cases, RedShift ends up being the more affordable option. However, its inability to scale up and down automatically makes it a no-go for mature IT organizations. 

Snowflake vs. BigQuery: pricing & features

Although less popular than Snowflake, BigQuery is a formidable competitor. Its serverless architecture functions differently from Snowflake’s hybrid architecture, but unlike Snowflake it doesn’t handle unstructured data. Snowflake also has generally better performance than BigQuery.

In terms of data storage, BigQuery has a slightly lower flat rate per TB price than Snowflake. However, when it comes to compute pricing, Snowflake is less expensive due to its faster performance and time-based credit consumption. 

Snowflake vs. Azure Synapse: pricing & features

Unlike Snowflake, Azure Synapse’s scalability requires far more manual intervention than Snowflake allows. This, in addition to its inability to handle unstructured data, makes it a less valuable platform than Snowflake. 

In terms of pricing, both platforms have their own versions of pay-as-you-go and capacity planning. While Snowflake separates compute and storage pricing, Synapse bundles them together into specific “Data Warehouse Units” (DWUs). This makes it harder to pinpoint which factors are driving changes in cost. 

Although Synapse integrates holistically with the broader Azure ecosystem, Snowflake just as easily runs on Azure. As a result, it’s a smarter choice for data warehousing than the native Microsoft offering. 

Snowflake pricing FAQs

Is Snowflake very expensive? 

Snowflake is not necessarily very expensive. However, depending on how much you use the platform, its costs can start to get out of control. For this reason (and others) Snowflake cost optimization is a top priority at most organizations. 

Is Snowflake cost effective?

As with any technology, Snowflake can be cost effective if used well. If you have good visibility into your Snowflake usage, actively work to optimize costs and performance, and take action on issues in real time, Snowflake can be a very cost effective platform. Without proactive management, however, it’s easy to exceed your budget and divert resources from other business critical engineering projects. 

Is Snowflake free to use?

No, Snowflake is not free to use. The lowest pricing tier is Snowflake’s Standard Edition, which starts at $2.00 per credit (US region). Additionally, Snowflake charges a flat data storage fee for each TB of micropartitioned data, regardless of how much compute you use. 

What are the advantages of Snowflake pricing?

Snowflake pricing offers significant advantages to organizations who have flexible workloads, are quickly growing and scaling, and want to balance cost with performance. 

How does Snowflake billing work? 

Snowflake bills are generated on a monthly basis. For more information on how often a Snowflake bill is generated and how to interpret it, check out this article. 

Final thoughts on Snowflake pricing

Although Snowflake pricing is complex, it’s not impossible to figure out which factors are driving your overspend. Then comes the real challenge: getting costs under control. 

If you’re trying to control Snowflake costs manually, odds are you’re missing savings opportunities left and right. If you’re using AI-powered optimizations and workload intelligence, however, you can not only identify high-impact savings potential, but you can automatically adjust your workloads 24/7. 

To learn more about how Keebo helps reduce Snowflake spending and improve engineer efficiency, schedule a demo today

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Keebo | Snowflake Pricing, Explained: A Full Breakdown of Costs + Savings Opportunities for 2025
Keebo the Robot Dog
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