Snowflake vs. Databricks: 2025 Comparison + Buyer’s Guide

In the data cloud space, two platforms are the undisputed frontrunners: Snowflake vs. Databricks. But each has its own terminology, structure, configurations, implementation processes, and pricing. This makes a straightforward, apples-to-apples comparison difficult. 

In this guide, we’re breaking down how each platform works so you can see the pros and cons of each. By the end, you should have a clear idea of which tool best meets your needs, plus some tips for optimizing costs and performance. 

Snowflake: an overview

Snowflake is a cloud native data platform that supports a range of workloads: data warehousing, data lakes, data science, AI/ML applications, and more. As a fully managed platform, Snowflake requires no interaction with your underlying cloud infrastructure—AWS, GCP, Azure, etc. 

Snowflake’s architecture is split into three distinct layers: storage, compute, and cloud services. This approach enables users to independently provision and scale resources to optimize long-term performance and effectiveness. 

Key advantages of using Snowflake include: 

• Scalable pricing (pay-as-you-go model) that provisions resources based on usage
• Built-in data replication and failover capabilities to ensure continuity across regions or cloud providers
• Massively Parallel Processing (MPP) architecture that provides high concurrency and speedy query execution
• Fully managed platform, enabling users to automate complex operations without accessing underlying cloud infrastructure

Databricks: an overview

Databricks is a cloud native data platform that is built to be a comprehensive solution for storing, processing, and analyzing data on a large scale. Databricks’s architecture consists of two primary layers: 

• The control plane houses back-end services, including both the graphical user interface and REST APIs for workspaces and account management
• The data plane (also called the compute plane) handles client communications and data processing within the customer’s cloud account

Like Snowflake, Databricks can integrate with all the “big three” infrastructure providers. One of the advantages Databricks has over Snowflake is access to multiple user interfaces: SQL editor, AI/BI dashboards, notebooks, etc. This enables more advanced customization and flexibility than Snowflake. However, it’s more difficult to implement and use. 

Some reasons for choosing Databricks include:

• Highly scalable platform that handles fluctuating data demands
• Built-in collaboration among data scientists, engineers, and analysts through interactive workspaces and version controls
• End-to-end support across the entire machine learning lifecycle, including pre-built ML libraries
• Flexibility with regard to programming language—SQL, Python, R, Scala—which enables integration and compatibility with various data sources and platforms
• Lakehouse architecture offers the benefits of both data lakes and data warehouses. This enables better management of both structured and unstructured data. 
  

Key Snowflake features

Unified platform

Snowflake enables robust data management from one platform: secure elastic data processing, data sharing, AI/ML, streaming, and more. The platform integrates structured, semi-structured, and unstructured data and supports diverse workloads and use cases. 

That makes Snowflake easy to use across different businesses, departments, and stakeholders. Users can input SQL commands and access, transform, and analyze data without interacting with the underlying cloud layers. 

Scalability

Snowflake is built to scale. As your organization grows and usage patterns shift, the platform can easily provision more storage, compute, and cloud resources to handle these demands. 

What’s more, Snowflake’s straightforward warehouse sizing mechanism (XS to 5XL, just like T-shirts) with horizontal scaling and multi-clustering. This can help you plan and provision resources based on demands and your own performance expectations. 

Cost reduction measures

The flip side of using a scalable cloud data platform is that you can sometimes exceed budget due to poor planning, overprovisioning, or unexpected spikes in usage. Thankfully, Snowflake offers several cost reduction measures to help keep your spend under control. These include resource monitors, auto-suspend, and more. 

Multiple programming language support

While Snowsight (Snowflake’s user interface) accepts commands written in SQL, developers can use a tool called Snowpark to write queries in other languages, like Python and Java. 

Governance & security

Snowflake Business Critical and VPS Editions have comprehensive data governance and security features to help ensure compliance with PCI DSS, HIPAA, GDPR, CCPA, and other regulations. Additionally, the platform offers robust access control and metadata management. 

Cross-cloud collaboration

Snowflake offers a cross-cloud technology layer called Snowgrid. This layer connects business ecosystems across clouds and regions, enabling business continuity at a global scale. By using Snowgrid, you can bypass standard ETL processes and speed up collaboration across data clouds. 

AI features

Snowflake offers two AI/ML offerings. Snowflake Cortex is a suite of pre-built AI features that serve a range of functions (e.g. answering freeform questions). Snowflake ML, on the other hand, provides developers and engineers with the functionality they need to build their own LLM functionality within the platform. 

Snowflake Marketplace

Snowflake is more than just a platform. It truly is a global community. This is nowhere more embodied than in the Snowflake Marketplace, where users can access apps, skills, and datasets that integrate directly with the platform in a single click.

Key Databricks features

Unified platform

Databricks offers a highly flexible and customizable environment. This is one of its strengths, as it allows users to tailor the platform to their specific needs and workflows. This flexibility, however, can lead to increased complexity, overwhelming new users or those with limited experience.

Apache Spark integration

Databricks runs on Apache Spark as its core processing engine, giving the platform access to distributed computing and the ability to process large datasets at scale. 

Data lakehouse architecture

Perhaps the most distinct of Databricks’s features is their data lakehouse architecture, which combines elements from both data lakes and data warehouses. Databricks uses cloud object storage for structured, semi-structured, and unstructured data formats, and leverages Delta Lake to provide ACID transactions, versioning, and schema enforcement. 

Scalability

Databricks enables you to automatically scale your clusters, enabling optimal resource utilization for each job. Automated scalability also enables you to accommodate ongoing, fluctuating workloads. 

AI/ML features

Databricks integrates MLflow into the platform to support a range of ML applications and AI-driven solutions. Additionally, Databricks Runtime ML offers access to popular ML libraries, including TensorFlow, PyTorch, Keras3, and more. 

Governance and security

Unity Catalog is Databricks’s standard governance and security solution. Its capabilities include roles-based access control, data audits and lineage, data quality monitoring, delta sharing, ML model governance, version control, and more.

Snowflake limitations

• A single account can only hold up to 10,000 dynamic tables
• Snowflake Hybrid tables—or tables with unique and referential integrity constraint enforcement—have a 2TB per database active data storage limit
• Hybrid table requests should be limited to approximately 8,000 operations per second. While this is not a hard limit, additional throughput can seriously hinder performance.
• Hybrid tables lack support for clustering keys, cross-account data sharing, and replication
• Lack of failover support in native app framework
• Only one executable ipynb file per notebook
• Notebooks cannot be replicated, restored once dropped, or created or executed by Snowflake database roles
• JavaScript UDF output rows are limited to 16 MB

Databricks limitations

• Complex user interface with steep learning curve and time-consuming implementation
• 48-hour query runtime constraints for serverless compute
• Individual table rows cannot exceed 128MB in size
• Each notebook cell can have no more than 6 MB of input, and the maximum size for a notebook to be autosaved, imported, exported, or cloned is 10 MB
• Table results displayed in a notebook are limited to the smaller of 10,000 rows or 2 MB
• No more than 2,000 concurrent task runs per workspace
• No more than 200 queries per second (although you can increase this by contacting Databricks)
• Git operations are limited to 2 GB of memory and 4 GB of disk writes
• Working branches for Git operations are limited to 1 GB

Snowflake pricing

Snowflake’s pricing model can be complicated (to say the least). Instead of a flat rate or monthly fee, Snowflake is priced based on usage. The pricing model has three components: storage costs, compute costs, and data transfer costs. Let’s look at all three in detail. 

Storage costs

Snowflake charges to store data that meet the following criteria: 

• 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

The exact cost varies by region, underlying cloud platform (e.g. AWS or Azure), Edition, and whether the account is Capacity or On-Demand (more on both of those below). 

Compute costs 

Compute costs are incurred any time you consume Snowflake credits by performing queries, loading data, or conducting other DML operations. These fall into three categories: virtual warehouses, serverless, and cloud services. 

Virtual warehouses

Without a doubt the biggest determining factor of Snowflake’s pricing is the platform’s virtual warehouse. These vary in size based on the resources provisioned to them, with each increase in size resulting in a 2X per-hour credit consumption rate: 

Key considerations when calculating warehouse costs: 

• Warehouses do not consume credits when suspended or idle
• Snowflake charges for first 60 seconds of warehouse activity, regardless of whether the warehouse is active during that time
• 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. 

Serverless

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. See our comprehensive Snowflake pricing guide for more details. 

Cloud services

Snowflake’s cloud services layer handles all the platform’s functionality except for the actual storing and processing of data. As long cloud services don’t exceed 10% of daily platform usage, no cost is incurred. 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. 

Data transfer

While Snowflake doesn’t charge to bring data into the platform (ingress), there is a charge for data transfer across regions or cloud providers (egress). However, not all Snowflake functions incur data transfer costs (here’s a full list of applicable functions). When a charge is incurred, it’s on a fee-per-byte basis. 

Other Snowflake pricing considerations

Snowflake Edition & per-credit pricing

Snowflake credits are the virtual “currency” used to measure and charge for compute resources. They’re priced based on which pricing tier (called “Snowflake Editions”) you use. Here’s a breakdown of the average On-Demand price for each Edition: 

Each tier (Edition) offers more advanced capabilities than the previous:

• Standard Edition is the standard, entry-level pricing tier with basic data warehousing needs
• Enterprise Edition is suited for larger organizations with more complex needs
• Business Critical Edition has stringent security and governance capabilities necessary for organizations with sensitive data, as well as multi-cluster support and database failover/failback for disaster recovery
• Virtual Private Snowflake (VPS) Edition offers maximum security  through a private network configuration, sharing no hardware with any accounts outside the VPS

On-demand vs. capacity

There are two ways to provision credits in Snowflake. The first is On-Demand, which is a true pay-as-you-go model. The second is Capacity, where you provision a set number of credits at a discounted rate and pay whether you use them or not. 

There are pros and cons to both approaches, and both can result in overspending if you’re not careful. This is one of the reasons it’s important to have a cost optimization strategy in place when you start to use Snowflake—otherwise you may exceed your budget and end up on your CFO’s bad side. 

Snowpark 

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. 

AI services

As of now, Snowflake offers two types of AI services: Document AI and Cortex AI. 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. 

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 credits at a different rate. 

Databricks pricing

Like Snowflake, Databricks employs a usage-based pricing model. Databricks measures usage through Databricks Units (DBUs) consumed across all workloads. The exact price of a DBU depends on two main factors: the type of workload the DBU is used for, and the user’s platform tier.

Workload types

Unlike Snowflake, where all warehouse workloads are charged the same rate, Databricks has a dynamic pricing schedule for DBUs. Here are some examples of how these workload types vary on the Standard Plan:

Platform tiers

Databricks offers three platform tiers: Standard, Premium, and Enterprise. Here’s a broad summary of what to expect with each tier: 

• Standard: Basic Apache Spark functionality, job scheduling, autopilot & interactive clusters, Databricks Delta, notebooks and other collaboration tools, and Ecosystem integrations. 
• Premium: All Standard features, Unity Catalog to centralize data governance, Private Link advanced network features, Delta Live Tables (DLT), serverless compute, enhanced security features, and advanced AI capabilities.
• Enterprise: All Premium features, higher levels of support and service, rigorous security and compliance, and more advanced governance and control capabilities. 

The difference in DBU pricing per platform tier is opaque, but we can extrapolate an example here. If the Standard rate is $0.40 per DBU, the Premium rate will be somewhere around $0.55 per DBU, while the Enterprise rate will likely be custom to the organization in question. 

Note that not every platform tier is available with every cloud provider. See Databricks’s documentation for a comprehensive breakdown. 

Pay-as-you-go vs. Committed Use Contracts

Like Snowflake, Databricks offers two ways to provision their usage-based platform: pay-as-you-go and Committed Use Contracts. 

Pay-as-you-go requires no upfront costs or recurring contracts, and you’re billed based on actual resource consumption per second. While there’s lots of flexibility to scale up or down, you’re paying full price for each DBU.

Committed Use Contracts are essentially the same as Snowflake Capacity. You get a set discount per DBU, but you’re committed to purchasing a certain number of resources. If you have stable, predictable workloads, this can result in significant savings. However, just like Snowflake Capacity, you can run into challenges with over- and under-provisioning, both of which eat into your budget. 

Snowflake reviews & ratings

According to G2, Snowflake has an average rating of 4.5 stars. Users count the platform’s ease of use and data management features to be among its pros. 

On the flip side, the platform is described as “expensive” and has some missing or limited features—table limits, limited support for unstructured data, and lack of visualization tools to stay on top of cost and performance.  

Databricks reviews & ratings

According to G2, Databricks receives an average 4.6-star rating from its users. Pros include features like collaborative notebooks, ability to run large & complex SQL queries, the Unity Catalog, ETL logic, and more. 

As far as cons go, the learning curve can be frustrating to some users. Often users need to hire a specialized consultant just for implementation. Others complain about performance lags among data lakehouses. 

Snowflake vs. Databricks FAQs

Who is Databricks’s biggest competitor?

Databricks’s biggest competitor is Snowflake. At a basic level, Databricks has the advantage in terms of flexibility and advanced features, while Snowflake is better at ease of use. Both platforms require third-party solutions for workload intelligence, data visualization, and cost and performance optimization. 

Does Databricks integrate with Snowflake?

Yes, Databricks integrates with Snowflake in a variety of ways. Databricks can query Snowflake data, read external tables from Snowflake, authenticate Snowflake data via Okta and other methods, and more.

Final thoughts on Snowflake vs. Databricks

Snowflake vs. Databricks: it’s a big question facing most companies building, expanding, or updating their data architecture. At the end of the day, the difference between the two is pretty straightforward: Databricks has more flexibility and functionality, but Snowflake is easier to use. 

In terms of pricing, both have complex, usage-based systems that make a straightforward comparison difficult. Usage-based platforms have a habit of letting your costs get out of control, which is why proactive cost optimization is important.

To learn more about how Keebo autonomously keeps Snowflake costs under control, check out this article.