How to Evaluate a Cloud Data Warehouse in 2026: Snowflake vs. AWS Redshift vs. Google BigQuery vs. Fabric vs. Databricks: A Buyer’s Scorecard

Introduction

Choosing a cloud data warehouse sounds simple. Until you start comparing them.

Snowflake, Amazon Redshift, Google BigQuery, Microsoft Fabric, and Databricks can all deliver enterprise-scale analytics. But today, choosing a data warehouse involves far more than performance and compute costs. AI workloads, governance, data sharing, BI performance, and the architecture around the warehouse can materially change which platform makes the most sense.

And the market has moved.

Microsoft Fabric is now used by more than 40,000 organizations, while Databricks has expanded well beyond its original lakehouse positioning into SQL analytics, data engineering, ML, and AI. Meanwhile, the major warehouse platforms are adding their own AI and agent capabilities. The shortlist is getting longer, while the differences between platforms are becoming harder to see on a feature sheet.

Pricing isn’t getting simpler either. BigQuery offers both on-demand and capacity-based models. Redshift combines provisioned and serverless options with reservations. Snowflake now separates Platform Credits from AI Credits for applicable AI services.

That means a comparison based on 2024 pricing, yesterday’s architecture assumptions, or a generic feature checklist can lead to the wrong buying decision.

The better question is:

Which platform fits your workloads, operating model, and AI strategy best?

This scorecard breaks down the decision across six dimensions that matter most in 2026.

Platform Profiles at a Glance

Before comparing the platforms, one distinction matters: Snowflake, Amazon Redshift, and Google BigQuery are primarily cloud data warehouse and analytics platforms, while Microsoft Fabric and Databricks take broader data platform and lakehouse approaches. They increasingly overlap in capabilities, which is why they belong in the same buyer evaluation, but they are not interchangeable architectural choices.

Snowflake: The Multi-Cloud Enterprise Data Platform

Snowflake began as a cloud data warehouse, but its scope has expanded significantly. Its architecture separates storage and compute, supports multiple cloud providers, and provides capabilities spanning analytics, data sharing, governance, applications, and AI.

In 2026, Snowflake is pushing further into AI and agentic workloads through its Cortex portfolio, spanning AI agents, search, natural-language analytics, and AI-assisted development. Snowflake also now uses a separate AI Credit model for applicable AI services, making AI consumption a more explicit part of the platform’s economics.

Best suited for: Enterprises prioritizing managed analytics, multi-cloud flexibility, governed data access, and an increasingly AI-native data platform.

Amazon Redshift: The AWS-Native Warehouse

Amazon Redshift is a mature cloud data warehouse designed to work closely with the broader AWS ecosystem.

Its strongest differentiator is not simply the warehouse itself. It is the surrounding AWS architecture: S3, Glue, IAM, Lake Formation, CloudWatch, and AWS’s growing portfolio of AI services can all become part of the same data architecture.

Redshift also offers both provisioned and Serverless deployment models. Serverless automatically manages capacity and can scale workloads without requiring teams to manage clusters, while reservations provide an option for organizations seeking more predictable costs.

Best suited for: AWS-first organizations that want their analytical warehouse tightly integrated with their existing cloud environment.

Google BigQuery: The Serverless Analytics Engine

BigQuery takes the infrastructure abstraction model further.

As a fully managed, serverless analytics platform, it removes the need to provision and manage traditional warehouse infrastructure. Organizations can choose between on-demand pricing based on data processed and capacity-based pricing measured in slot-hours, with autoscaling and commitment options available for predictable workloads.

Its other major advantage is its position within Google’s data and AI ecosystem, including Gemini and Vertex AI.

Best suited for: Organizations that prioritize serverless analytics, large-scale data processing, and deep integration with Google Cloud’s data and AI services.

Microsoft Fabric: The Unified Analytics Contender

Microsoft Fabric takes a different approach from a traditional standalone warehouse.

Rather than focusing on the warehouse as an individual product, Fabric brings together data engineering, data integration, warehousing, analytics, governance, and Power BI within a unified SaaS platform.

Its enterprise momentum is difficult to ignore. Microsoft reported more than 40,000 paid Fabric customers in FY26, with customer growth exceeding 60% year over year^[i].

Best suited for: Microsoft-centric organizations, particularly those with substantial Power BI adoption, that are looking to consolidate their data and analytics stack.

Databricks: The Lakehouse for Data and AI

Databricks approaches the problem from the lakehouse perspective rather than through a traditional warehouse model.

Its platform brings together data engineering, SQL analytics, machine learning, and AI, giving technical teams considerable flexibility across analytical and AI workloads.

That flexibility comes with a trade-off: Databricks can offer significant architectural power, but it generally demands a more technically sophisticated operating model than a highly managed warehouse.

Best suited for: Data- and ML-intensive organizations that want a unified platform for data engineering, analytics, machine learning, and AI.

The Scoreboard – Six Evaluation Dimensions 

1. Architecture & Operational Overhead

The first question isn’t just how powerful a warehouse is. It’s how much work your team has to do to keep it running well.

Provisioning, scaling, workload isolation, performance tuning, and ongoing maintenance all contribute to the real operational cost of a platform.

Snowflake: Built for low operational overhead

Snowflake separates storage and compute, allowing teams to scale workloads independently and isolate resources across virtual warehouses. Much of the underlying infrastructure is managed by the platform, reducing the need for manual provisioning and tuning. Its Dynamic Tables also support continuously refreshed data pipelines, making Snowflake relevant for near-real-time analytics use cases.

Best for: Enterprises that want a highly managed warehouse with strong workload isolation and minimal infrastructure administration.

Amazon Redshift: Strongest when you’re already on AWS

Redshift offers both provisioned and Serverless deployment models. Serverless reduces infrastructure management by automatically adjusting capacity to workload demand, while provisioned deployments give teams greater control. Redshift also supports streaming ingestion from services such as Amazon Kinesis Data Streams and Amazon MSK for workloads that require data to reach the warehouse with low latency.

Its biggest advantage is its integration with the broader AWS ecosystem, particularly for organizations already using services such as S3, Glue, IAM, and Lake Formation.

Best for: AWS-first organizations that want their warehouse closely integrated with their existing cloud environment.

Google BigQuery: Serverless by design

BigQuery eliminates traditional cluster management altogether. Google handles the underlying infrastructure, allowing teams to focus on data, queries, and workloads rather than provisioning and maintaining compute resources. It also supports streaming ingestion and analytics, making it suitable for use cases where data needs to be available with low latency.

That simplicity is particularly valuable for workloads with unpredictable demand, although query design, partitioning, and data modeling still matter for controlling performance and cost.

Best for: Teams prioritizing infrastructure simplicity and elastic, large-scale analytics.

Microsoft Fabric: Simplicity through consolidation

Fabric takes a broader SaaS approach, bringing data engineering, data integration, warehousing, analytics, and Power BI into a unified platform. Its real-time capabilities extend this model to scenarios that require continuously updated operational and analytical data.

The operational advantage is less about managing a better warehouse and more about managing fewer separate services.

Best for: Microsoft-centric organizations looking to consolidate their data and BI stack.

Databricks: More flexibility, more responsibility

Databricks combines data engineering, SQL analytics, ML, and AI in a lakehouse architecture. Its Structured Streaming capabilities support continuous data processing alongside traditional batch workloads. That breadth gives technical teams considerable flexibility, but it can also require more expertise to design and operate effectively.

Best for: Data- and ML-intensive organizations with mature engineering teams.

2. Pricing & Total Cost of Ownership 

Cloud warehouse pricing has become less about a single price per query and more about how your workload behaves.

Compute model, storage, concurrency, data movement, AI consumption, and capacity commitments can all change the economics. That’s why comparing headline prices alone can be misleading.

Snowflake: Flexible consumption, but model the whole workload

Snowflake’s consumption-based model lets organizations scale compute around demand, while separate virtual warehouses can isolate workloads. However, AI services introduce another cost dimension: Snowflake now distinguishes Platform Credits from AI Credits for applicable AI services.

Watch for: Compute consumption, storage, data transfer, and growing AI workloads.

Amazon Redshift: Strong economics for AWS-aligned workloads

Redshift offers both provisioned and Serverless pricing, giving organizations options based on workload predictability. Reservations can further reduce costs for workloads with consistent demand.

For AWS-heavy organizations, existing commitments and the cost of keeping data within the AWS ecosystem can also materially influence TCO.

Watch for: Provisioned capacity, Serverless usage, reservations, and data movement.

Google BigQuery: Simple entry, workload-sensitive economics

BigQuery combines on-demand pricing with capacity-based pricing across Standard, Enterprise, and Enterprise Plus editions. Capacity is measured in slot-hours, with autoscaling available to adjust resources to workload demand.

Watch for: Edition requirements, slot consumption, autoscaling, storage, and data processed under on-demand pricing.

Microsoft Fabric: Consolidation can change the equation

Fabric’s economics are different because the value proposition extends beyond the warehouse. Organizations already using Power BI and Microsoft data services may be able to consolidate multiple capabilities under a unified platform.

Watch for: Capacity utilization, workload contention, and whether consolidation actually replaces existing tools and licenses.

Databricks: Broad platform, broader cost model

Databricks pricing spans compute, SQL warehouses, data engineering, and other workloads. Its flexibility can be valuable for organizations running analytics, ML, and AI together, but TCO needs to account for the full lakehouse architecture rather than SQL warehouse costs alone.

Watch for: Compute consumption across workloads, engineering infrastructure, and platform complexity.

3. AI & ML Native Capabilities 

Snowflake: Strongest on governed enterprise AI

Snowflake has made AI a core part of its platform through Cortex, bringing LLM-powered functions, search, agents, and AI-assisted development closer to enterprise data.

Its Cortex Agents can work across structured and unstructured data, making Snowflake particularly relevant for organizations exploring governed, data-aware AI agents.

Best for: Enterprises looking to bring AI and agentic workflows directly into their governed data environment.

Amazon Redshift: AI through the AWS ecosystem

Redshift’s AI capabilities are closely connected to the broader AWS ecosystem, while its Serverless architecture automatically scales compute capacity to match workload requirements. AWS also provides workload-based autoscaling and optimization to balance price and performance.

Best for: AWS-first organizations building AI applications across the AWS ecosystem.

Google BigQuery: Deep integration with Google’s AI stack

BigQuery has a natural advantage for organizations already invested in Google Cloud’s AI ecosystem. Gemini capabilities are integrated into BigQuery, while Vertex AI provides a broader environment for developing and deploying ML and generative AI applications.

Best for: Organizations building data and AI workloads primarily on Google Cloud.

Microsoft Fabric: AI meets the Microsoft ecosystem

Fabric brings AI capabilities into a broader analytics environment that includes OneLake, Power BI, and Microsoft’s Copilot ecosystem.

For Microsoft-heavy organizations, the attraction is less about one standout AI feature and more about having data, analytics, BI, and AI operating within the same platform.

Best for: Microsoft-centric enterprises looking to connect AI, analytics, and BI.

Databricks: Deepest data science flexibility

Databricks remains particularly strong where machine learning and AI are tightly coupled with data engineering.

Its lakehouse architecture supports data engineering, SQL analytics, ML, and AI workloads on a common platform, giving technical teams significant flexibility.

Best for: ML-heavy organizations and teams building sophisticated data and AI applications.

4. Governance, Security, and Compliance 

Snowflake: Strong governance across a multi-cloud environment

Snowflake provides granular controls including role-based access, row-level policies, column masking, tagging, and access history. Its governance capabilities are particularly valuable for organizations managing sensitive data across teams and clouds.

Best for: Enterprises that need centralized, granular governance across a multi-cloud data environment.

Amazon Redshift: Deep AWS security integration

Redshift provides role-based access, row-level and column-level security, encryption, audit logging, and integration with AWS identity and governance services.

Its advantage is the broader AWS security ecosystem, particularly for organizations already using IAM, Lake Formation, and CloudTrail.

Best for: AWS-first enterprises that want warehouse governance integrated with their existing security architecture.

Google BigQuery: Fine-grained control at scale

BigQuery supports granular access controls, including row-level security, column-level security, and dynamic data masking. Its integration with Google’s broader security and data governance services provides an additional layer for enterprise environments.

Best for: Organizations that need fine-grained data controls without adding significant infrastructure overhead.

Microsoft Fabric: Governance across the analytics estate

Fabric extends Microsoft’s governance capabilities across data, analytics, and Power BI. This can be particularly valuable when organizations want consistent policies and discovery across a consolidated analytics environment.

Best for: Microsoft-centric organizations looking to govern data and BI through a unified platform.

Databricks: Governance across the lakehouse

Databricks uses Unity Catalog as its centralized governance layer across data and AI assets. It supports fine-grained access control, auditing, lineage, and data discovery across the lakehouse environment.

Best for: Organizations managing complex data, analytics, and AI workloads across a shared lakehouse.

5. Ecosystem Integration & Lock-in Risk

A warehouse sits alongside your data lake, ETL pipelines, BI tools, identity systems, AI services, and governance stack. The deeper those integrations become, the more important it is to understand both ecosystem value and exit friction.

Snowflake: Strongest multi-cloud flexibility

Snowflake runs across AWS, Azure, and Google Cloud, giving enterprises flexibility across cloud environments. Its broad partner and connector ecosystem also makes it relatively easy to integrate with heterogeneous data and BI environments. Support for Apache Iceberg and Delta Lake adds flexibility in how data is stored and accessed, while Snowflake’s secure data sharing and Marketplace capabilities make it easier to discover, share, and consume data across organizational boundaries.

Trade-off: Snowflake’s proprietary capabilities can still make migration more difficult if you eventually move workloads elsewhere.

Amazon Redshift: Maximum value inside AWS

Redshift’s greatest integration advantage is AWS itself. Native connections to services such as S3, Glue, IAM, Lake Formation, and Bedrock can simplify architectures for AWS-first organizations. Support for open table formats such as Apache Iceberg and Delta Lake also helps organizations work across warehouse and data lake architectures.

Trade-off: The deeper your architecture depends on AWS-specific services, the greater the potential migration effort if your cloud strategy changes.

Google BigQuery: Deep GCP integration

BigQuery fits naturally into the Google Cloud ecosystem, including Cloud Storage, Dataflow, Dataplex, Vertex AI, and Gemini capabilities. Support for Apache Iceberg and Delta Lake provides additional flexibility across data platforms, while BigQuery Sharing, formerly known as Analytics Hub, allows organizations to share and discover datasets across organizational boundaries without replicating the underlying data.

Trade-off: Similar to Redshift, deep dependence on cloud-native services can increase switching costs.

Microsoft Fabric: Integration through consolidation

Fabric’s differentiator is breadth within the Microsoft ecosystem. OneLake, Power BI, Data Factory, and other Fabric workloads operate within a unified environment. OneLake supports both Delta Lake and Apache Iceberg, improving interoperability across analytics engines while keeping data within the broader Microsoft data and analytics environment.

Trade-off: That consolidation can simplify operations, but it can also increase dependence on the Microsoft ecosystem.

Databricks: Broad ecosystem, lakehouse flexibility

Databricks is designed to work across major cloud environments and integrates with a wide range of data sources and tools. Its lakehouse approach can reduce dependence on traditional warehouse architecture, with Delta Lake at its core and support for Apache Iceberg providing flexibility across data engineering, analytics, and AI workloads.

Trade-off: Its flexibility can introduce more architectural choices, which may increase implementation complexity.

6. BI Tool Compatibility: Tableau and Power BI 

A warehouse can deliver excellent query performance and still create a poor analytics experience if it doesn’t work well with the BI tools your teams actually use.

For most enterprises, the evaluation comes down to more than whether a connector exists. Connector maturity, query performance, semantic models, refresh behavior, and concurrency can all affect the end-user experience.

Snowflake: Strong across both ecosystems

Snowflake has mature integrations with both Tableau and Power BI, making it a strong option for organizations running a mixed BI environment.

Its workload isolation also helps when BI queries need to coexist with other analytical workloads.

Best for: Enterprises using both Tableau and Power BI or operating a heterogeneous BI stack.

Amazon Redshift: Strong AWS foundation

Redshift integrates with major BI platforms, including Tableau and Power BI, while offering tight connectivity with the broader AWS analytics ecosystem.

For BI-heavy workloads, however, buyers should benchmark concurrency and dashboard performance rather than relying on connector availability alone.

Best for: AWS-centric organizations with established Tableau or Power BI environments.

Google BigQuery: Strong serverless BI foundation

BigQuery supports both Tableau and Power BI and can handle highly variable analytical workloads without traditional infrastructure management.

Its serverless architecture can be particularly useful when dashboard demand fluctuates significantly.

Best for: Organizations that want elastic analytics behind their BI layer.

Microsoft Fabric: The Power BI advantage

Fabric has a structural advantage when Power BI is central to the organization’s analytics strategy.

Power BI’s integration with Fabric, including Direct Lake, allows semantic models to work directly with data in OneLake, reducing the need for traditional import-based data movement in suitable scenarios.

Best for: Microsoft-heavy organizations where Power BI is the primary BI platform.

Databricks: Strong for modern data stacks

Databricks integrates with major BI tools and supports SQL warehouses designed for BI and analytics workloads.

Its advantage is particularly relevant when BI sits alongside data engineering, ML, and AI workloads within the same lakehouse architecture.

Best for: Data-driven organizations combining BI with broader lakehouse and AI workloads.

The Decision Framework: Which Platform Fits Which Buyer?

The right platform choice depends on where your data lives, how your teams work, what workloads you expect to add, and how much platform complexity you’re prepared to operate.

Use the following as a starting point for narrowing your shortlist.

Choose Snowflake if you prioritize multi-cloud flexibility and governed enterprise analytics

Snowflake is a strong fit for organizations that want a highly managed platform with strong governance, workload isolation, broad BI compatibility, and the flexibility to operate across multiple clouds.

Best fit: Large enterprises, multi-cloud environments, and organizations making governed AI and data sharing strategic priorities.

Choose Amazon Redshift if AWS is already your strategic foundation

If your data, identity, security, and analytics infrastructure already run primarily on AWS, Redshift can reduce integration friction and make it easier to build around existing AWS services.

Best fit: AWS-first enterprises with established AWS data and analytics investments.

Choose Google BigQuery if serverless analytics and GCP-native AI are priorities

BigQuery is particularly compelling when teams want minimal infrastructure management, highly elastic analytics, and close integration with Google’s data and AI ecosystem.

Best fit: Data-intensive organizations with significant GCP adoption and AI/ML workloads.

Choose Microsoft Fabric if Power BI and Microsoft are central to your stack

Fabric becomes especially compelling when the goal is to consolidate data engineering, warehousing, analytics, and BI rather than optimize each layer independently.

Best fit: Microsoft-centric enterprises with substantial Power BI adoption.

Choose Databricks if data engineering, ML, and AI drive the architecture

Databricks is strongest when the organization needs a flexible platform spanning data engineering, analytics, machine learning, and AI and has the technical expertise to operate it effectively.

Best fit: Data- and ML-intensive organizations with mature engineering teams.

The final decision should come from your own workloads, costs, governance requirements, and technical environment.

And before you sign a multi-year contract, there are five questions every vendor should be able to answer.

Before You Sign: Five Questions to Run Past Your Shortlist 

A vendor demo can make almost any platform look impressive. The real test comes when you put your own workloads, users, and constraints into the equation.

Before signing, ask these five questions.

1. What will our three-year TCO actually look like?

Don’t stop at the advertised compute rate. Ask the vendor to model your expected compute, storage, BI usage, data movement, AI workloads, and capacity commitments across three years.

2. Which features are included in our edition?

A feature being “supported” doesn’t necessarily mean it’s included in your plan. Get clarity on security, governance, AI, monitoring, and advanced workload capabilities before comparing final costs.

3. What happens when our workloads change?

Your warehouse may start with SQL analytics and dashboards and later support AI agents, real-time workloads, or hundreds of additional users. Understand how the platform and its pricing behave as usage evolves.

4. How difficult would it be to leave?

Ask what happens if your cloud strategy changes. Evaluate data portability, SQL compatibility, proprietary features, pipelines, BI dependencies, and egress costs.

You may never migrate, but you should know what migration would involve.

5. Can we test our workloads, not your benchmark?

Run a proof of concept using representative queries, dashboards, data volumes, concurrency, and AI use cases.

Measure performance, cost, reliability, and operational effort together.

That’s the only way to turn a platform comparison into a buying decision you can defend.

Conclusion 

There is no one-size-fits-all winner in the 2026 cloud data warehouse market.

Snowflake, Redshift, BigQuery, Fabric, and Databricks each bring different strengths across performance, cost, AI, governance, ecosystem, and BI. The right choice depends on how those strengths align with your workloads, existing technology investments, and growth plans.

The key is to look beyond the feature checklist.

A platform that works well for your current workloads may not be the best fit as data volumes grow, AI becomes part of everyday analytics, or your BI and governance requirements become more complex.

The right platform should meet today’s requirements while giving your data strategy room to evolve.

If you’re evaluating your next data platform, we can help you assess the options against your actual workloads, architecture, and TCO, and build a roadmap that fits your business.

Benchmark Your Options Against What Matters to Your Business. Let’s Talk! 

Statistics Reference: 

^[i] Microsoft