As analytics steps into the era of enterprise AI, customers’ requirements for a robust platform that is easy to use, connected and trusted for their current and future data needs remain unchanged.
“Serverless computing” has enabled customers to use cloud capabilities without provisioning, deploying and managing either hardware or software resources. Snowflake has embraced serverless since our founding in 2012, with customers providing their code to load, manage and query data and us taking care of the rest. With some serverless platforms, customers still have to stitch together disparate services from a single vendor or across multiple vendors — for example, in the case where compute is managed by the vendor but storage isn’t.
Furthermore, most vendors require valuable time and resources for cluster spin-up and spin-down, disruptive upgrades, code refactoring or even migrations to new editions to access features such as serverless capabilities and performance improvements. Despite most having security and governance features, these may require the customer to integrate multiple services to provide a comprehensive solution, or worse yet, may not be available in earlier versions of the product, forcing upgrades. The problem is exacerbated for customers with cross-region and cross-cloud deployments, especially when there are potential risks of an outage or disaster. Lastly, companies have historically collaborated using inefficient and legacy technologies requiring file retrieval from FTP servers, API scraping and complex data pipelines. These processes were costly and time-consuming and also introduced governance and security risks, as once data is moved, customers lose all control. As a result, data often went underutilized. Some vendors use a federated sharing model, which might work in some situations but can lead to poor cost predictability and control.
Snowflake, on the other hand, has not only been serverless since our founding but also provides a fully managed service that is truly easy, connected across your data estate and trusted by thousands of customers.
Snowflake is easy
Organizations always had to differentiate themselves by accelerating time to value through end-to-end data management and scalability — the move toward AI further necessitates these needs. Having a platform that makes innovations such as generative AI easy to implement is imperative.
Against the backdrop of organizations racing to unlock as much business value as possible from data and AI, budgets are also being closely scrutinized. Organizations constantly are being forced to do more with less, so they need an economical solution with easy-to-use cost controls.
Snowflake’s fully managed service takes care of platform management and brings AI to your data while offering a constantly improving engine with built-in cost management to continuously optimize value for customers. Such features include:
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Automated administration: With Snowflake, there are minimal knobs to turn — it just works out of the box. Ongoing usage requires little in the way of tuning or optimizing, and maintenance tasks such as running vacuum commands and other housekeeping tasks happen in the background without customer effort or impact. This has a significant effect on the total cost of ownership, helping save valuable admin time and therefore costs, which can now be reallocated to launching new products and completing data projects faster.
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Instant scalability: With a fully managed service like Snowflake, there is a spare pool of resources that enables instant scalability. Because these resources are run in the vendor VPC, they can be shared across all customers in that region to enable effortless scaling up or down of resources. With a customer-managed service, customers run compute in their own tenancy, so if they want a spare pool, they have to pay for it, which defeats the purpose of cloud-supported elasticity. For most customer-managed platforms, there’s a delay to add or remove capacity, with the customer having to manage the instance, load software and join it to the cluster — things that happen instantly with Snowflake.
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Upgrades without disruptions: Snowflake handles all software version upgrades and patches, without customer disruption. We take care of planning, executing and verifying upgrades, and we do so using a rolling process without downtime. This also means that all customers run on the same software with the same capabilities. When you read the documentation on platform as a service (PaaS) offerings, you’ll often see references to features that are not supported in certain versions of the service, along with outage windows for planned maintenance — none of these are an issue with Snowflake.
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Built-in AI: Snowflake brings AI to our customers’ data and they can leverage models, such as Snowflake Arctic and Anthropic’s Claude, for tasks such as text processing, sentiment analysis and custom summaries with Snowflake Cortex AI, Snowflake’s gen AI capabilities. They can easily access multiple code interfaces, including those for SQL and Python, and the Snowflake AI & ML Studio for no-code development. Cortex AI has also led to the creation of a suite of gen AI products including Snowflake Copilot, an AI-powered assistant that helps you complete tasks within Snowflake, and Document AI, a family of services that use machine learning to understand and extract data from various types of documents.
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Cost visibility, control and optimization through an easy-to-use interface: Snowflake’s cost management interface has account and organization overviews to easily track spend and usage metrics, such as the most expensive queries, with deeper drill-downs. It further shows how the value of a Snowflake credit changes over time. Customers can also control Snowflake spend in the interface through resource monitors to set limits on credit usage, receive notifications and take action on virtual warehouses that approach or exceed specified limits. Budgets are another capability in the interface to help control spend, where customers can specify a spending limit at the account level or for a custom grouping of credit-consuming resources and receive notifications when limits are projected to be exceeded. Furthermore, cost insights in the interface expose cost-saving opportunities, such as large tables that haven’t been queried recently, to help optimize resource utilization.
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Automatic performance improvements: Snowflake automatically rolls out regular performance enhancements in query times and query processing that customers can benefit from without needing to take any action. These improvements in Snowflake performance experienced by real customers over time can be tracked regularly with the Snowflake Performance Index (SPI) — in fact, over the last 26 months, Snowflake has improved query duration by 40%.
Snowflake is connected
Data teams and developers are spending countless hours building infrastructure to copy and move data around. This challenge is exacerbated by increasing data volumes, regulatory scrutiny and the fact that not all the needed data sits within one organization. To maximize insights and business value from data, teams need to quickly and easily collaborate on data not only with others in their organization but also with their partners, vendors or even their customers. And when they identify a data product they’d like to purchase from a third party, they should be able to easily evaluate, pay for and integrate that product.
To remain nimble and have the flexibility to use the best tools for different workstreams, customers should not be locked into a specific vendor or technology. Instead, they need to architect their data ecosystems around interoperable, open data formats so that multiple query and transformation engines can operate on the same data without ingestion or translation.
Core to Snowflake is our rich data ecosystem, enabled by our zero-ETL sharing and interoperability with open table formats, to help customers maximize value from all of their data, apps and models:
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Seamless data sharing: Snowflake pioneered privacy-preserving data sharing without moving data through modern, access-control-based technology spanning regions and clouds. These sharing capabilities have been extended to zero-copy integration of SaaS applications and even support ML models and knowledge extensions to expand the capabilities of Snowflake Cortex Search and Snowflake Intelligence.
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Secure internal and external collaboration: With Snowflake, customers can easily find and access dashboards, worksheets, data, models, apps and documentation from inside and outside their organization with the single Universal Search. They can further explore and integrate data, apps and AI products from other teams inside their organization through the Internal Marketplace, or from over 680 partners and vendors across more than 18 categories through Snowflake Marketplace. Customers can accelerate the procurement of data and apps with the ability to purchase directly via Snowflake Marketplace and can even use existing Snowflake capacity commitments.
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Interoperable storage: Snowflake enables customers to access and process structured, semi-structured and unstructured data seamlessly, without silos or delays. Unique automations and optimizations include encryption by default, built-in storage compression and fast access to data even at petabyte scale. Snowflake’s flexibility enables businesses to deploy a wide range of architectural patterns including a data lake, data warehouse, lakehouse or data mesh. Compatibility with on-premises data and open table formats, such as Apache Iceberg, further extends the value of Snowflake to an organization’s entire data estate.
Snowflake is trusted
Comprehensive security, governance and business continuity have been paramount in recent years, but as companies extend to newer AI use cases and build data fabrics and meshes across regions and clouds, trust becomes even more critical.
Snowflake’s fully managed service has out-of-the-box, enterprise-grade trust with unified security, governance and business continuity/disaster recovery across regions and clouds:
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Out-of-the-box Snowflake Horizon Catalog: With Snowflake Horizon Catalog, security admins and chief information security officers can uniformly implement access controls across clouds and quickly uncover and resolve cross-cloud security risks. Meanwhile, data governors and stewards can easily apply built-in, proven governance protections to sensitive content, while data teams can quickly search, discover, access and share governed data, apps and models from across their ecosystem to boost privacy-preserving collaboration. The advanced capabilities of the Horizon Catalog can even be extended to Apache Iceberg™ Tables created by any other compatible engine in the Snowflake Open Catalog, after these tables are integrated and synced to Snowflake.
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Out-of-the-box business continuity/disaster recovery: Snowflake enables customers to easily safeguard mission-critical accounts and data sets to maintain uptime. We provide seamless replication and synchronization of databases, accounts, pipelines and more from one place between regions and clouds for resiliency, durability and failover in a stressed event or by choice based on business strategy changes.
The power of the Snowflake platform
Many enterprise buyers struggle to choose the right data platform to meet their needs, focusing solely on feature and pricing differences to make their choice. But comparing a fully managed service to a customer-managed one — even one that is “serverless” — gets complicated when taking into consideration the value of all of the automations and out-of-the-box capabilities covered here. With a customer-managed service, organizations should expect to plan, implement and manage these capabilities on their own, so when calculating the TCO, they must factor in all of the incremental time, effort and cost needed to hire, train and dedicate resources to manage these capabilities — resources that could otherwise be spent on launching products and completing data projects faster. These additional costs often make the total cost of operating customer-managed solutions far higher than that of a fully managed service.
In comparison, Snowflake has had more than a decade of experience delivering a fully managed service that has been built with ease of use, connectivity and trust in mind to help our customers prepare for whatever technological movements come their way. Hear from our customers Pfizer and OM1 on how Snowflake revolutionized their data and AI needs:
“Snowflake has been strategic to simplify our data foundation. It allowed us to solve concurrency and data silo problems at an enterprise scale. It’s easy to use, there’s no maintenance, and database administration is drastically reduced. It gives us functionality we can’t get anywhere else — and it costs us less. … Now, when different business units need to share data, it’s all in one compliant, secure, trusted place. Snowflake was a key contributor in helping us get to One Pfizer.”
—Steve Ring, Director of Enterprise Database Solutions, Pfizer
Learn more about Pfizer’s Snowflake journey.
“We were on a mission to simplify our entire ecosystem. While we had moved our data to Snowflake, we had a complex ecosystem of compute spread across AWS, Databricks and custom software. While this system worked, it came with fairly high cost and overhead. From a business perspective, it’s all about efficiency. We want our data engineers to spend their time innovating and solving hard problems, not maintaining platforms. To simplify its ecosystem, OM1 moved its data processing from its previous platform to Snowflake — unleashing greater efficiency, cost savings and performance in the process.”
—Eric Schrock, CTO, OM1
Learn more about OM1’s Snowflake journey.
