Snowflake | What it is, and How It’s Become Successful (2025 Update)

TL;DR

Snowflake is a cloud data platform that separates compute from storage, runs on AWS/Azure/GCP and supports SQL across structured, semi‑structured and unstructured data.

Key 2025 updates:

• Standard Warehouse Gen2 and Adaptive Compute improve price/performance and automate warehouse sizing.
• Horizon Catalog and Copilot provide AI‑driven governance and cross‑catalog discovery, while new FinOps tools enable cost anomaly detection and tag‑based budgets.
• Security enhancements include leaked‑password and bad‑IP protection, new MFA methods and sensitive‑data monitoring.
• Snapshots and improved replication support business‑continuity.
• Snowpark Container Services and upcoming Snowflake Postgres expand extensibility.
• Dynamic Tables enhancements allow date/time filtering, immutability, insert‑only inputs and backfill.
• Document AI extracts data from unstructured documents and is now generally available.

Table of Contents

What is Snowflake?

Today, data is a core asset for modern organizations and the ability of technology to scale has caused a surge of big data. Storing and managing data is now an important function for modern business operations. Every organization is looking to choose a data platform that can handle huge volumes of big data at high speed and with reliability. Most organizations are using a cloud data platform and others are considering moving their data to a cloud data platform.

Snowflake is a fully‑managed cloud data platform designed to store, process and analyse vast amounts of data. It decouples compute from storage, so organisations can scale each independently and only pay for what they use. Snowflake runs on top of the major cloud providers (AWS, Microsoft Azure and Google Cloud) and is accessible through Snowsight (its web UI) or through standard SQL clients. The platform offers a unified environment for data warehousing, data lakes, data engineering, data science, application development, AI/ML workloads and secure data sharing. Since 2024 Snowflake has added numerous AI‑driven features, making it an increasingly attractive “data cloud” for enterprises.

Why has it become so successful?

Snowflake’s success comes from a combination of architectural choices, ease of use and a steady cadence of innovations.

Instant and almost Unlimited Scalability

The Snowflake architecture relies on a single elastic performance engine that offers high speed and scalability. Snowflake can also support a high number of concurrent users and workloads, both interactive and batch. This can be attributed to its multi-cluster resource isolation feature. Snowflake is robust and high-performing, giving organizations the confidence they need to handle all types of data workloads.

In June 2025 Snowflake introduced the Standard Warehouse – Generation 2 (Gen2), which delivers roughly 2.1× faster price/performance for analytics compared with the earlier generation. A new Adaptive Compute service (private preview) selects cluster sizes, number of clusters and auto‑suspend/resume durations automatically, further optimising cost and performance.

Unified Data Processing

Snowflake users can query structured, semi‑structured and (with recent releases) unstructured data using standard SQL. Features like Document AI, powered by Snowflake’s Arctic‑TILT large language model, convert PDFs, invoices and other unstructured documents into structured tables. Document AI became generally available in late 2024 across most AWS and Azure regions.

A Single Data Copy, Shared Securely

Snowflake’s cross‑region and cross‑cloud sharing means teams work from one consistent copy of data. The Snowflake Horizon Catalog (2025) federates across Apache Iceberg tables through catalog‑linked databases, giving users unified governance and discovery across Snowflake and external sources. Horizon Catalog now includes a copilot that uses natural language to assist with governance and metadata discovery tasks.

FinOps and cost visibility

Snowflake joined the FinOps Foundation in 2025 and rolled out features to help customers monitor and control spending. These include Cost‑Based Anomaly Detection (public preview) for spotting abnormal cost spikes, Tag‑Based Budgets for allocating spend to teams, and tools such as Performance Explorer, Query Insights and Cost Insights for Large Query Gaps.

Security and governance

Recent releases added leaked‑password protection and Bad IP Protection to disable compromised credentials. Snowflake expanded Trust Center detectors and introduced new MFA methods, including passkeys, Windows Hello and security‑key support. It also added personal access tokens and workload identity federation to replace long‑lived passwords, plus sensitive‑data monitoring, synthetic data generation and enhanced Private Link connections.

Business continuity

Snowflake’s snapshots (public preview) create point‑in‑time, immutable backups that cannot be modified or deleted, even by administrators, helping organisations meet regulatory and ransomware‑protection requirements. Improved replication interfaces and schema‑level replication make disaster recovery more intuitive and cost‑effective.

Extensibility and ecosystem

Snowpark Container Services (SPCS) lets customers run containerised workloads (e.g., AI model training, custom applications) directly inside Snowflake, with general availability on GCP expected soon. In development is Snowflake Postgres, a fully managed PostgreSQL service within Snowflake. Unistore, Snowflake’s transactional engine, expanded to Azure with Hybrid Tables and now offers Tri‑Secret Secure (TSS) encryption.

Dynamic tables enhancements

Snowflake’s Dynamic Tables simplify data pipelines by refreshing downstream tables automatically. Four enhancements launching in 2025 include:

• Filtering by current date/time, allowing SQL filters to minimise processing costs.

• Immutability, letting users mark regions of a dynamic table as read‑only to preserve historical data and avoid costly recomputation.

• Insert‑only inputs, which process only new inserts for faster performance.

• Backfill, enabling dynamic tables to be seeded from existing data using zero‑copy cloning.

Pricing Model

Snowflake’s pricing model is based on demand rather than the typical SaaS subscription model. It allows users to choose the amount of storage and computing they need before paying for it based on utilization or on a monthly fixed-rate model.

Snowflake Pricing (2025)

Snowflake offers a consumption-based pricing model – meaning you pay for what you use, with costs broken into three main components: Compute, Storage, and Cloud Services. Your pricing is also influenced by your choice of Edition, Region, and Cloud Platform (AWS/Azure/GCP).

1. Editions & Their Features

Snowflake offers four distinct tiers, each building on the previous:

Each higher tier provides more advanced features but also increases credit pricing per compute usage.

2. Compute (Virtual Warehouses & Credits)

• Credits are the billing unit for compute. Every running warehouse consumes credits by the second (with a minimum of 1 minute), even when idle. The number of credits per second depends on the warehouse size (X‑Small through 6X‑Large).

Example warehouse sizes:

• Credit costs vary by edition and region: Standard is roughly $2-3/credit, Enterprise $3-4.65, Business Critical $4-6.20, and VPS $6-9.30+ in U.S. regions .
• Cloud Services: This covers metadata, optimization, and coordination workloads. Snowflake offsets this by billing a maximum of 10% of your compute credits per day, whichever is lower .

3. Storage

• Billed as a flat rate per TB per month, based on the compressed data size stored—including Time Travel history and staged data. Snowflake optimizes compression automatically.
• The exact cost depends on region and on whether you’re using On-Demand or Capacity (pre-purchased) plans.
• Storage fees are generally a smaller portion of total cost, often under 10%, since compute typically dominates 80–90% of your bill.

4. Data Transfer

• Inbound data (uploads): Free, no charges.
• Outbound data (egress): Charged based on region and cloud provider. Transfer within the same region/cloud is free; cross-region or cross-cloud transfers are billed per byte.

5. Purchasing Options

• On-Demand: No upfront commitment, pay full price per credit—best for flexible or variable usage.
• Capacity (Pre-Purchased): Commitment-based credits can significantly reduce compute and storage costs—ideal for steady, predictable workloads.

Summary Table

What Drives Snowflake Costs Most?

• Compute costs are by far the largest contributor, due to warehouse runtime and cluster size.

• Storage and cloud services costs are generally minor but vary depending on usage patterns and commit-level choices.

• Transfers can add up if you’re moving data cross-region or cross-cloud regularly.

Who are its Competitors?

Although Snowflake is a powerful cloud data warehouse platform, it faces stiff competition from a number of other cloud data warehouse platforms. Below are the two top Snowflake competitors:

1. Amazon Redshift

Amazon Redshift is a cloud-based data warehouse of the Amazon Web Services cloud platform. Redshift was designed for data scientists and data engineers. It is a fast and fully-managed data warehouse solution, which makes it easy to analyze data using SQL and BI tools.

However, Redshift requires its users to rely on third-party tools when it comes to ETL (Extract, Transform, Load) and data transformation. This comes with additional costs, setup, and maintenance. Redshift users have also reported poor performance when running parallel queries.

Redshift is GDPR and HIPAA compliant. It has a 2 months free-trial period, after which users are charged $0.25 per hour.

2. Databricks

Databricks is an enterprise software company that provides its users with a cloud data platform for automated cluster management. Databricks brings together machine learning, data engineering, and collaborative data science. It groups its assets into workspaces. A Databricks workspace organizes objects, that is, libraries, notebooks, and folders, into folders and offers access to data and computation resources like jobs and clusters.

The Databricks platform provides cross-functional teams with a secure way of communication. The teams concentrate on their data science, data engineering, and data analytics tasks as Databricks manages the backend services.

Whereas Snowflake is used to handle structured and semi-stuctured data, Databricks works with all types of data, even unstructured. Recently, Databricks became a data lake vendor. First, they added Delta Lake, which requires integrations to work with other engines. The list of integrations has been growing steadily, and other engines have direct access to the data.

Snowflake Analytics using Knowi

The Snowflake cloud data warehouse platform offers unmatched flexibility, automatic scaling of storage, security, and seamless integration with various BI tools. While you can use a BI tool of your choice to analyze your Snowflake data, consider Knowi on Snowflake as it provides many added benefits.

1. Knowi provides intelligent query execution capabilities with the ability to store query results within Knowi that will reduce your compute and storage costs on Snowflake.
2. Provides native integration into Snowflake, including arrays, JSON and custom data type support.
3. Knowi comes with advanced data analytics features, including search-based analytics that allows non-technical users to ask questions in plain English and get instant responses even in the form of graphs and tables.

To learn more about how to connect Snowflake to Knowi as the data source, query data from Snowflake, analyze the data by creating visualizations and more, visit the following blog tutorial.

Frequently Asked Question