Top 6 Cloud Data Warehouse Solutions in 2025

Cloud data warehouses are growing, with the market on track to almost triple by 2026. Businesses are pushing hard to modernize their data management, and selecting the right platform can be confusing.

With over 19 years of experience in data warehouse consulting, we have supported numerous organizations in understanding cloud solutions. Services like Amazon Redshift and Snowflake bring unique benefits. They allow SQL queries on vast amounts of different types of data. They also come with features like advanced data transformation and scaling that adjusts.

In this blog post, we have provided a side-by-side comparison in a table of the six leading cloud data warehouse options for 2025. Read on to explore their pricing, key features, main strengths, and best-use scenarios.

Platform	Model	Scale	Elasticity	Semi‑Structured Support	Strengths	Limitations
Redshift	Cluster (serverless too)	Petabytes	Manual + Concurrency Scaling	Partial	AWS synergy, fast, cost-effective	AWS‑only, concurrency/delete issues
BigQuery	Fully serverless	Petabytes	Automatic	Yes	No infra mgmt, ML, global replication	Cost spikes, compute tuning limits
Snowflake	Multi-cloud clusters	Petabytes	Auto-scaling, elastic	Yes	Cloud-agnostic, Time Travel, sharing	Pricing variability, migration effort
Azure Synapse	Unified SQL/Spark	Petabytes	Provisioned + serverless	Yes	Integrated Azure + BI, unified workspace	Azure‑only, usability quirks
Azure SQL DB	Serverless DB	Moderate	Auto-scale/pause	Limited	OLTP, app-centric, automated management	Not intended for large analytics
ClickHouse	Open-source columns	Variable-extreme	Manual	Yes	Ultra-fast reads, open-source cost	Poor joins, concurrency, updates

1. Amazon Redshift (First introduced in 2012)

Redshift brought groundbreaking changes to cloud data warehousing by offering scalable and fast analytics at a competitive price.

Core Design & Speed

• Redshift relies on Parallel Processing (MPP), which spreads data across multiple nodes to handle queries quickly and in parallel.
• It utilizes columnar storage and effective data compression methods, such as AZ64. Compared to LZO, AZ64 can compress data about 35% better and run 40% faster.
• Uses machine learning to optimize tasks like sorting and choosing distribution keys by studying workload patterns over time.

Scaling & Performance

• Concurrency Scaling can increase capacity to handle sudden user demand. It offers up to 35 times better performance during peak times, with one free hour of typical use included.

Ease of Use & Compatibility

• Supports the PostgreSQL SQL dialect, making it easier to learn.
• Fits into the AWS ecosystem, working with services like S3, Glue, and Kinesis.

Advantages

• Delivers strong performance that keeps getting better without needing much manual effort.
• It costs much less than other data warehouses, around 50 to 75 percent lower.
• You can scale clusters without dealing with complex admin work.
• It offers robust security measures, including HIPAA SOC 2, FedRAMP, and PCI compliance.

Drawbacks

• It has limited native support for managing semi-structured data formats, such as XML or JSON.
• Being tied to AWS restricts flexibility in working across multiple cloud platforms.
• Performance might drop during high traffic or when deleting data often.

Costs

• On-demand pricing starts at $0.25 per node every hour.
• Reserved Instances can save up to 75 percent if you choose a 3-year plan.
• The Serverless Option allows you to pay based on how much you use the computing power.
• Concurrency Scaling offers 01 free credit hour every 24 hours.
• RA3 node storage costs approximately $0.024 per GB per month, depending on the region.

Best Use Cases

• Handling massive data: It works well with petabyte-scale queries and billions of rows.
• Consistent reporting needs: Ideal for tasks like daily financial updates used by platforms such as NASDAQ.
• Live data analysis: It connects with streaming tools like Kinesis for real-time analytics.
• Analytics platforms: Good for those offering cost-efficient multi-tenant setups.

2. Google BigQuery (Introduced in 2010)

This is a serverless and managed tool built to manage petabyte-scale analytics using Google’s infrastructure.

Setup & Speed

• Columnar storage uses separate computing and storage systems.
• The Dremel engine runs SQL queries across distributed systems.
• The Capacitor engine and Google’s Jupiter network ensure fast data access.

Advanced Features

• Use BigQuery ML to create and deploy machine learning models with SQL.
• added AI tools include semantic search and tracking data origins.

Advantages

• Serverless, with no need to manage or scale clusters.
• Handles massive datasets, even those reaching petabytes.
• Includes features like query caching and pipelining to improve performance.
• High-level security options and automatic regional data backups.

 Drawbacks

• Managing costs can be hard with big or slow queries eating up resources.
• Customizing compute resources comes with limits.
• Data exports face some restrictions, and batch scheduling starts at a minimum of 15 minutes.
• New users might struggle due to the steeper learning needed.

Cost

• On-demand: Pay per terabyte processed. You get 1 TiB free each month. Charges round up to the nearest megabyte, with a minimum of 10 MB per table.
• Slot-based (capacity): Buy fixed compute units. It helps with budgeting. Choose from Standard, Enterprise, or Enterprise+ tiers, and get discounts for commitments.
• Storage: Active tables cost $0.02 per GB per month dropping to $0.01 per GB per month after 90 days.

Best Use Cases

• Teams can use ad hoc analysis to work with big detailed queries.
• Merging both real-time streams and past records into active dashboards helps track data.
• Companies in the Google Cloud environment rely on Looker, Dataflow, and Pub/Sub.
• Machine Learning tools are built into the warehouse to support data science tasks.

3. Snowflake (Launched 2014)

A flexible cloud platform with a modern structure that keeps components separate.

Design & Speed

• Breaks down the roles of Storage, Compute, and Cloud Services into distinct layers.
• Scales compute resources across clusters to handle multiple tasks at once.
• Leverage micro-partitions and automated clustering to make queries faster.

Performance Metrics

• The system scans 6–60 million rows in just 2–10 seconds.

Advantages

• You can scale computing and storage with elastic independent scaling.
• It works across multiple clouds, including AWS, Azure, and GCP.
• It allows users to roll back data up to 90 days with its Time Travel feature.
• You can share data across accounts without having to duplicate it.
• It offers strong security, encryption, and governance features.

Drawbacks

• It might get costly and hard to predict compute costs.
• It has a smaller user community compared to open-source platforms.
• You won’t find an on-premise version available.
• Moving to the platform can be tricky because of its specific Snowflake constructs.

Cost

• Pay-per-use model offers $2 to $4 per credit based on the edition.
• Storage costs $23 per terabyte each month.
• Compute charges applied by the second, with a 60-second minimum fee.

Best Use Cases

• Flexible workloads that require scaling compute.
• Handling semi-structured or unstructured data in data lakes and warehouses.
• Collaborating on data across multiple teams or companies.
• Enabling data science tasks while working with machine learning tools like TensorFlow or PyTorch.

4. Azure Synapse Analytics (Azure SQL DW)

This platform unites data warehousing, integration, and large-scale data processing.

• Combines big data analytics and data warehouse into one platform.
• Includes both provisioned and serverless SQL pools, with options for Spark and pipeline tools.
• Relies on MPP technology to handle large-scale analytics and works well with Power BI.

Advantages

• Works with the Azure ecosystem.
• Offers a single workspace for BI ETL, SQL, and Spark users.
• Handles real-time data ingestion and analysis while focusing on built-in security features.

Drawbacks

• reliant on Azure—less adaptable to other clouds.
• Lacks user-friendly features like T-SQL MERGE support or SQL Profiler.
• Spark clusters are restricted to Synapse’s own setup.

 Cost

• Serverless SQL: You pay based on the data scanned, with 10 MB included free in each query.
• Dedicated pools: You get reserved performance and can save as much as 65%.
• Synapse Compute Units (SCUs): Prepaying allows you to get discounts.
• Storage costs are charged .

Best Use Cases

• Use it to run analytics in real time on both batch and streaming data.
• Perfect option for teams that need a shared analytics workspace.
• Helps big companies combine regional data from different sources.
• Lets businesses modernize workflows and save 30 to 40% on costs.

5. Azure SQL Database

This is a transactional database offered as a PaaS. It includes a serverless mode and tools to improve performance .

Key Features

• The platform offers a managed engine where backups, patching, and monitoring are automated.
• The Serverless compute tier adjusts its scale and pauses during inactivity meaning you pay for storing data.
• It includes smart features like performance tuning, threat detection, Always Encrypted technology, and Microsoft Entra (AAD) integration.

Advantages

• Works well for apps with changing workloads—serverless helps cut costs.
• Delivers 99.995 percent uptime with its Business Critical tier.
• Makes SQL Server migrations easier.
• Offers strong security and works well with enterprise tools.

Drawbacks

• Doesn’t fit large-scale data warehousing due to OLAP limitations.
• Pricing can be confusing with vCore and DTU models.
• T-SQL features don’t fully match what on-prem SQL Server offers.

Cost

• vCore-based: Costs $0.52 per vCore per hour available as serverless or provisioned.
• DTU-based: Bundles together CPU, storage, and IO in Basic, Standard, or Premium tiers.
• Provides free backup storage up to the size of the database.

Best Use Cases

• OLTP workloads with irregular or occasional usage.
• Businesses shifting from on-prem SQL Server.
• Applications with changing or seasonal workloads

6. ClickHouse (open-source by Yandex used in open-source community)

This high-speed columnar database is open-source and built for analytics.

Core structure and design

• Uses columnar storage with the MergeTree engine to boost read speeds and reduce data size.
• Relies on vectorized computations that help optimize CPU performance.
• Employs data-skipping indexes instead of traditional ones to cut down on unnecessary I/O.

Advantages

• Delivers very fast query performance often outperforming commercial data warehouses by 2–10 times.
• Reduces infrastructure expenses through efficient compression and indexing methods.
• Handles real-time data ingestion and supports flexible formats like JSON, Map, and Array.

Drawbacks

• Struggles with joins leading users to often restructure tables into a simpler format.
• Handles limited concurrency, which can cause issues during many active sessions.
• Changes or deletions happen at merge-time. This method does not work well with data that updates .
• Adjusting cluster size requires manual effort and rebalancing of data.

Cost (Cloud)

• Basic Plan costs about $66.52 per month. This includes 8 GiB RAM and $25.30 per terabyte each month for storage.
• Scale Plan is priced at $499 per month. It provides auto-scaling and better reliability features.
• Enterprise Plan charges around $0.39 per unit per hour. It offers advanced tools and options.

Best Use Cases

• Perfect to analyze data with very low delays, like catching fraud in finances or tracking ad impressions.
• Works well to handle streaming or time-series data that needs results in milliseconds.
• Media telecom, and cloud-monitoring systems depend on fast data intake and quick searches.

Conclusion

Picking a cloud data warehouse comes down to what you need, your tasks, and how much you can spend. Redshift works well for analytics, BigQuery gives scalable serverless options, and Snowflake makes it easy to use across multiple cloud platforms. Azure Synapse fits best in Microsoft setups, and ClickHouse focuses on real-time speed. Providers differ when it comes to costs, integrations, and security. Focus on platforms that match your tasks, can expand as you grow, and encourage progress; don’t just go with what’s popular. If you are still unsure and require guidance on choosing the best cloud data warehouse solution tailored to your business needs, connect with cloud app development experts to leverage our result-oriented cloud app development services for strategic planning, migration, and development to seamless integration.