Snowflake Competitors: In-Depth Comparison of the 4 Biggest Alternatives

Snowflake has become one of the leading cloud data platforms, valued for its scalability and ease of use. Unlike traditional on-premise data warehouses, Snowflake is fully cloud-based, allowing businesses to scale storage and compute resources independently while paying only for what they use.

As Snowflake has gained popularity, major competitors—including Amazon Redshift, Google BigQuery, Microsoft Azure Synapse, and Databricks—have emerged, each with unique strengths and trade-offs.

This article compares Snowflake's competitors, examining their architectures, pricing models, performance, and key differentiators compared to Snowfkale to help businesses and individuals choose the right platform for their needs.

If you’re new to Snowflake and want to get started learning the basics, be sure to check out our course, Introduction to Snowflake. 

Major Cloud Data Warehouse Snowflake Competitors

Let’s start by examining some of Snowflake’s biggest competitors and what makes each one unique in the market.

Amazon Redshift

Amazon Redshift is AWS’s cloud data warehouse, built on PostgreSQL but optimized for large-scale data processing. It uses a cluster-based architecture and integrates tightly with AWS services, making it a strong choice for businesses already invested in the AWS ecosystem.

Google BigQuery

Google BigQuery is a serverless data warehouse that separates storage and compute, letting users analyze massive datasets while only paying for queries. Powered by Google’s Dremel engine, it delivers fast performance on large-scale analytics workloads.

Microsoft Azure Synapse

Azure Synapse (formerly SQL Data Warehouse) combines data warehousing with big data analytics. It uses massive parallel processing (MPP) architecture to handle large-scale data. Synapse can process both structured and unstructured data, and integrates deeply with other Microsoft tools.

Databricks

Databricks is a 'lakehouse' platform built on Apache Spark, combining data warehouse and data lake capabilities. Developed by Apache Spark’s creators, it excels at large-scale batch and real-time data processing.

Other notable players

• Teradata: A long-standing player in data warehousing, known for handling extremely large workloads.
• Oracle Autonomous Data Warehouse: Built on Oracle’s database technology with automated management.
• IBM Db2 Warehouse: Offers both cloud and on-premises solutions with AI optimization.
• Firebolt: A newer competitor focused on extremely fast query performance for analytics.

Each platform has its unique architecture and technical approach to handling data at scale. While they all serve the same basic purpose — managing and analyzing large amounts of data — their designs create different strengths and limitations that we’ll explore in the following sections.

Snowflake vs Competitors: A Feature-by-Feature Breakdown

Let’s dive deep into how these platforms compare across key technical and business dimensions.

Architecture and technology

The key differences between these platforms become apparent when examining their underlying architectures and technological approaches.

Cloud infrastructure approaches

Data warehouse platforms take different approaches to cloud infrastructure. Snowflake and Databricks offer multi-cloud flexibility, while Redshift, BigQuery, and Azure Synapse integrate tightly with specific cloud providers. These choices affect performance, scalability, vendor lock-in, and costs — key factors organizations must weigh when choosing a platform.

Snowflake’s multi-cloud architecture works across AWS, Azure, and Google Cloud. Its three-layer design (storage, compute, and services) scales independently, reducing cloud vendor lock-in and optimizing performance.

Amazon Redshift runs exclusively on AWS, using a cluster-based architecture where compute and storage are tightly coupled. This integration benefits AWS users but limits multi-cloud flexibility.

BigQuery is a fully managed, serverless data warehouse exclusive to Google Cloud. It uses Colossus for storage and Dremel for fast, distributed query execution.

Azure Synapse runs on Microsoft Azure, using an MPP architecture to distribute workloads. It includes auto-scaling to optimize resource costs.

Databricks' 'lakehouse' architecture blends data warehouse and data lake capabilities. Running on multiple cloud platforms, it uses Delta Lake for storage and Apache Spark for distributed processing.

Here is a table summarizing the various cloud infrastructure approaches of these platforms:

Performance and scalability

Each platform takes a unique approach to performance and scaling capabilities. Snowflake uses “virtual warehouses,”  groups of computers that work together and can grow or shrink instantly based on need. This feature means companies can handle sudden increases in workload without any delay. The system also keeps frequently used data in memory, making repeated queries run much faster.

Redshift takes a more traditional approach where companies need to manually adjust their computer clusters. While this gives more direct control, making changes to the cluster size can take anywhere from several minutes to a few hours. Redshift makes up for this by using specialized hardware and columnar storage that helps certain types of queries run very quickly.

BigQuery handles growth differently — it automatically adjusts resources for each individual query without users needing to do anything. This means every query gets exactly the computing power it needs, though this can sometimes lead to less predictable costs. The system can process massive amounts of data by spreading work across thousands of computers simultaneously.

Azure Synapse gives users two choices for handling workloads. They can either use a serverless option where resources adjust automatically, or they can set up dedicated pools of resources they control directly. This flexibility lets companies choose the best approach for different types of work. The platform also includes special optimizations for processing data stored in Azure Data Lake.

Databricks uses Apache Spark clusters that can grow and shrink automatically as needed. These clusters are particularly good at processing huge amounts of data in parallel. The platform includes special optimizations that help queries run faster over time by learning from past performance.

Data processing

For data processing capabilities, each platform handles different types of information. Snowflake works well with organized data in tables as well as semi-organized data like JSON files, XML documents, and Parquet files. Redshift is best suited for traditionally organized data in tables, with some basic support for other formats. BigQuery can handle organized and semi-organized data and even process information as it arrives in real time. 

Azure Synapse can work with both organized and completely unorganized data thanks to its built-in Spark capabilities. Databricks offers the most flexibility, capable of processing any kind of data, including pictures, videos, and other complex file types.

Here is a table that summarizes the differences in terms of performance and scalability:

Features and capabilities

Each platform has unique features tailored for different use cases. Below, we compare key areas such as data sharing, security, integration, and analytics.

Data sharing and collaboration

Data sharing and collaboration capabilities have become essential features of modern data platforms, enabling organizations to securely exchange information with partners, customers, and the public. These platforms offer various approaches to data sharing, from marketplace-style exchanges to direct cross-account access, each with different tradeoffs in terms of flexibility, security, and ease of use.

Snowflake provides a secure data marketplace where organizations can share and monetize their data assets. Their unique architecture allows data providers to share access to their data without physically copying or moving it, saving storage costs and ensuring data stays current. Providers can create branded data listings and manage access through secure data clean rooms.

Redshift enables data sharing between AWS accounts through Redshift datashares. Organizations can share live, read-only data across databases, AWS accounts and AWS Regions. This allows teams to access data directly from their own Redshift clusters without copying or moving the data. However, sharing is limited to within the AWS ecosystem.

BigQuery offers access to hundreds of public datasets spanning various domains like economics, healthcare, and scientific data. Organizations can share datasets across different Google Cloud projects while maintaining granular access controls. The platform also supports real-time data sharing through streaming APIs and pub/sub integration.

Azure Synapse works with Azure Data Share to facilitate secure data sharing across organizations. It supports sharing both snapshots and incremental data changes. Recipients can automatically receive updates when new data becomes available. The service handles all the underlying data movement and security.

Databricks created the open Delta Sharing protocol which enables sharing of data stored in Delta Lake format with any computing platform. This vendor-neutral approach allows organizations to share data with partners regardless of what technology they use. The protocol supports both batch and streaming data sharing scenarios.

Security and governance

Security and governance are critical priorities for modern data platforms, with each vendor offering comprehensive features to protect sensitive data and ensure regulatory compliance. These platforms provide robust authentication, encryption, access controls, and auditing capabilities to help organizations maintain data security while enabling appropriate data access and sharing.

Authentication in these platforms goes beyond simple username/password. Snowflake supports Single Sign-On through identity providers like Okta and Azure AD. Their Multi-Factor Authentication adds an extra security layer through authenticator apps or hardware tokens. OAuth enables secure API access while key pair authentication allows for automated tools and scripts.

Redshift leverages AWS Identity and Access Management (IAM) for authentication and access control. It supports federation with corporate directories and enforces SSL/TLS encryption for all connections. Organizations can define fine-grained permissions at the database, table, and column level.

BigQuery uses Google Cloud IAM for access management and VPC Service Controls to create security perimeters around resources. Organizations can restrict which IP addresses can access data and enforce organization-wide policies. The platform maintains detailed audit logs of all data access.

Synapse integrates with Azure Active Directory for identity management. It provides column and row-level security to restrict what data users can see. The platform includes built-in data discovery and classification to help identify sensitive information.

Databricks Enterprise Security includes SCIM for automated user provisioning, Unity Catalog for centralized access control, and audit logging. Organizations can enforce encryption requirements and manage secrets securely through key vaults.

Integration capabilities

Integration capabilities are a crucial differentiator among modern data platforms, determining how easily they can connect with other tools and services in an organization’s technology stack. Each platform offers different approaches to integration, from native connectors to API support and programming language compatibility.

Snowflake Snowpark provides pre-built connectors for popular BI tools like Tableau, Power BI, and Looker. It supports major programming languages, including Python, Java, Node.js, and Go through official client libraries. The platform can directly query data from cloud storage in formats like JSON, Avro, and Parquet.

Redshift integrates deeply with the AWS ecosystem. It can load data directly from S3, use AWS Glue for ETL workflows, and connect with EMR for big data processing. The platform works with AWS DMS for database migration and AWS Backup for disaster recovery.

BigQuery connects seamlessly with other Google Cloud services. It can query data directly from Cloud Storage, process streaming data through Dataflow, and integrate with operations tools like Cloud Monitoring. The platform supports federated queries across multiple data sources.

Synapse provides native integration with the Microsoft ecosystem, including Power BI, Azure ML, and Azure Data Factory. It can process data stored in Azure Data Lake Storage and integrate with Azure Purview for data governance. The platform includes built-in development tools through Azure Synapse Studio.

Databricks leverages the extensive Apache Spark ecosystem for data processing and analytics. It integrates with MLflow for machine learning lifecycle management and Delta Lake for reliable data storage. The platform supports running custom containers and connecting to external data sources.

Analytics and machine learning

Analytics and machine learning capabilities have become essential features of modern data platforms as organizations seek to derive insights and build intelligent applications. These platforms offer varying levels of built-in analytics and ML functionality, from basic SQL analytics to advanced deep learning support, while also providing integration with specialized ML tools and frameworks.

Snowflake Cortex provides a programming framework for data processing and machine learning. Users can write UDFs and stored procedures in Python, Java or Scala that run directly in Snowflake. The platform includes optimized runtimes for popular ML frameworks and supports deploying models as UDFs.

Redshift integrates with Amazon SageMaker for machine learning. Users can train models on data in Redshift and deploy them for in-database inference. The platform includes built-in ML functions for common tasks like forecasting and anomaly detection.

BigQuery provides native ML capabilities through SQL-based model training and prediction. Users can create models for classification, regression, forecasting and more without moving data. The platform integrates with Vertex AI for advanced ML workflows and model deployment.

Synapse combines SQL and Spark-based analytics with integrated Azure ML support. Users can develop end-to-end ML solutions using notebooks, create automated ML experiments, and deploy models for real-time scoring. The platform includes built-in ML model management.

Databricks provides comprehensive ML capabilities through MLflow integration. Users can track experiments, package code into reproducible runs, and deploy models as REST APIs. The platform includes optimized deep learning libraries and support for distributed model training.

Pricing and cost management

Cost management and pricing models are critical factors in evaluating data platforms for enterprise adoption. Each platform takes a different approach to pricing and cost optimization, with varying degrees of flexibility and transparency in their billing models.

Pricing models

Snowflake uses a flexible consumption-based pricing model that separates storage and compute costs. Storage is charged based on the actual data volume stored after compression, while compute resources are billed per second based on virtual warehouse usage. This model allows customers to scale up or down instantly and only pay for actual usage.

Redshift follows a more traditional instance-based pricing structure where customers pay for the EC2 instances that power their clusters. To reduce costs, customers can purchase reserved instances with 1 or 3-year commitments that offer significant discounts compared to on-demand pricing. The instance types determine both compute and storage capacity.

BigQuery offers two primary pricing options. The on-demand model charges for storage and queries separately, with query costs based on the amount of data processed. Alternatively, customers can choose flat-rate pricing with reserved slots for more predictable costs. Both models include free tier quotas for small workloads.

Azure Synapse Analytics provides two purchasing models: Database Transaction Units (DTUs) which bundle compute, storage, and I/O resources, or vCore-based pricing that allows separate scaling of compute and storage. The vCore model offers more granular control but requires more active management.

Databricks charges based on compute time of cluster usage plus consumption of Databricks Units (DBUs). DBUs measure processing power used by various instance types and configurations. Premium features and advanced security controls incur additional costs based on deployment size.

Cost optimization features

Automatic suspension of compute resources helps organizations reduce unnecessary spending. Snowflake automatically suspends virtual warehouses after a period of inactivity. Similarly, Synapse can pause dedicated SQL pools, and Databricks clusters can shut down when idle. This prevents billing for unused compute time while maintaining quick resume capabilities.

Storage optimization through compression is a critical cost-saving feature across platforms. Each vendor implements proprietary compression algorithms — Snowflake uses automatic micro-partitioning and clustering, Redshift employs columnar compression encoding, BigQuery automatically optimizes storage, Synapse offers rowstore and columnstore compression, and Databricks leverages Delta Lake optimization.

Query performance optimization tools help reduce processing costs through more efficient execution. Snowflake provides query profiling and materialized views. Redshift offers query planning and workload management tools. BigQuery includes query optimization recommendations. Synapse Analytics provides execution plans and statistics. Databricks features Photon engine optimization and query caching.

Snowflake Competitive Advantages

With so many data platforms available, it’s important to know what sets each one apart. Every platform has its own strengths, making some a better fit than others depending on your organization’s needs.

Snowflake’s key strengths

Let’s look at what makes Snowflake stand out from its competitors: 

1. Multi-cloud architecture

Beyond just supporting multiple clouds, Snowflake enables seamless integration of data assets across cloud providers. Organizations can maintain governance standards while letting different departments use their preferred cloud platform. The platform handles all the complexity of cross-cloud operations, including data transfer optimization, security protocol alignment, and performance tuning across providers.

2. Separation of resources

The decoupled architecture allows for granular resource management that traditional data warehouses can’t match. When marketing runs heavy analytics while finance generates month-end reports, their workloads remain completely isolated. Storage costs stay optimized since data exists in one place, while each department’s compute usage is tracked separately for precise chargeback.

3. Data marketplace innovation

The marketplace transforms how organizations monetize and share data assets. Healthcare companies can securely exchange patient insights while maintaining HIPAA compliance. Retailers can offer real-time inventory data to suppliers without complex ETL processes. Financial institutions can create new revenue streams by packaging and selling anonymized transaction data through secure data clean rooms.

4. Simplified management

The platform’s automation capabilities extend beyond basic maintenance. When query patterns change, clustering automatically adjusts without DBA intervention. Time travel integrates with corporate governance policies for automated compliance. Zero-copy cloning enables instant test environments for developers while keeping storage costs flat. Regional failover happens automatically based on customizable business continuity rules.

Areas for improvement

While Snowflake offers compelling advantages, it’s important to understand potential drawbacks and areas where the platform could evolve to better serve customer needs:

1. Current limitations

For organizations with straightforward data needs, the sophisticated architecture can result in higher operational costs. Processing large volumes of images or video files requires workarounds due to limited native support. Complex stored procedures face performance bottlenecks compared to traditional databases. Organizations must carefully evaluate pricing tiers as some essential security features require premium subscriptions.

2. Development opportunities

While basic machine learning functions exist, the platform lacks advanced capabilities like automated model training and deployment. Real-time data processing has room for improvement, particularly for sub-second latency requirements. Data transformation tools could better handle complex hierarchical data structures. The platform could expand support for emerging data types like graph databases and spatial data. Large enterprises need more sophisticated tools to optimize costs across multiple business units and usage patterns.

The impact of these characteristics varies by organization size and use case. Small companies benefit from simplified management but must watch costs carefully. Large enterprises can leverage the sophisticated multi-cloud capabilities but need dedicated resources for optimization. Data-driven businesses can create new revenue streams through the marketplace while ensuring regulatory compliance.

Conclusion

Snowflake stands out for its multi-cloud architecture, efficient resource management, and growing data marketplace. It simplifies cross-cloud integration but comes with considerations around specialized data types, advanced analytics, and cost management.

As Snowflake evolves, ongoing innovation will help address its limitations. Organizations that assess their needs carefully can maximize its benefits while strategically managing costs and resources.

If Snowflake is the right choice for your organization, you’re going to need a range of training and learning materials. Here are some excellent resources to get started with: 

• Introduction to Snowflake Course
• Snowflake Tutorial For Beginners: From Architecture to Running Databases
• Introduction to Data Modeling in Snowflake Course
• How to Learn Snowflake in 2025: A Complete Guide
• Which is the Best Snowflake Certification For 2025? Choose The Right Path