7 things to consider when choosing a cloud data warehouse

Selecting a cloud data warehouse is one of the most consequential architectural decisions your team will make. The platform you choose will influence everything from query performance and development velocity to your monthly cloud bill and the types of applications you can build.

Most vendor comparisons focus on surface-level metrics, such as price per terabyte-hour, claimed query speeds, or the number of integrations. But these specs rarely tell the whole story. The fundamental differences emerge when you dig into architectural trade-offs, hidden costs, and how platforms perform under your specific workload patterns.

This guide examines seven critical factors that actually matter when evaluating data warehouses for production use. We'll compare how ClickHouse, Snowflake, BigQuery, and Redshift handle each consideration, drawing from documented capabilities, architectural designs, and real-world customer experiences.

Whether you're building internal analytics for a data team, embedding dashboards in your product, powering AI agents with real-time data, or scaling to thousands of concurrent users, these factors will help you identify which platform aligns with your actual requirements.

Deployment flexibility: Lock-In vs. optionality #

Not all cloud data warehouses give you the same deployment choices. Some platforms are cloud-only and available on a single provider, limiting your architectural options. Others offer true multi-cloud flexibility with multiple deployment models.

When evaluating platforms, consider the full spectrum of deployment options:

Cloud provider choice matters: #

• Multi-cloud support: Deploy on AWS, GCP, or Azure based on your existing infrastructure
• Leverage existing commitments: Use cloud credits or enterprise agreements with your preferred provider
• Regional coverage: Deploy where your users are, regardless of which cloud has the best presence there
• Avoid cloud vendor lock-in: Maintain negotiating leverage and optionality as cloud pricing evolves

Deployment model flexibility adds control: #

• Fully-managed SaaS: Run in the vendor's cloud account for maximum simplicity
• Bring Your Own Cloud (BYOC): Deploy the managed service in your own AWS/GCP/Azure account, giving you data ownership, direct control over security policies, and visibility into infrastructure costs while still getting managed operations.
• Self-managed open source: Run the database entirely under your control - on-premises, in your cloud account, on bare metal, or spot instances for maximum cost optimization

The BYOC model is particularly compelling for organizations with strict data governance requirements. Your data never leaves your cloud account, you maintain full audit trails, and you can apply your own encryption keys and network policies - while still benefiting from automated operations and updates.

Here's how the major platforms compare on deployment flexibility:

Only ClickHouse offers the full spectrum of deployment options - from open-source, self-managed to BYOC to fully managed cloud across all three major providers. The other platforms are either cloud-only, limited to specific providers, or both.

Proprietary cloud-only solutions may offer simplicity, but they remove optionality. As your needs evolve - whether scaling internationally, managing costs, or addressing compliance requirements - deployment flexibility becomes increasingly valuable.

Query concurrency at scale: Internal vs. user-facing workloads #

Traditional data warehouses were designed for internal analytics teams running scheduled reports and ad-hoc queries. ClickHouse was built for both internal analytics and high-concurrency, user-facing applications. This architectural difference has profound implications.

Concurrency limits reveal design philosophy: #

Why this matters for different use cases: #

For internal analytics, where a few dozen analysts run queries during business hours, these limits are often sufficient. A team of 20 analysts generating occasional queries can comfortably work within an 8-query concurrency limit.

However, for user-facing applications - such as customer dashboards, embedded analytics, product analytics, operational dashboards, or AI agents querying data - the math changes significantly. When you expose analytics to hundreds or thousands of users, concurrency demands explode:

• 100 concurrent users × 3-5 queries per interaction = 300-500 queries/second
• 1,000 concurrent users generate 3,000-5,000 queries/second
• Customer-facing applications during peak traffic can hit 10,000+ concurrent users

The cost of scaling traditional warehouses: #

Snowflake addresses concurrency through multi-cluster warehouses, which can scale out to handle more queries, but at a significant additional cost. Each warehouse you add to handle increased concurrency multiplies your compute spending. For applications with unpredictable, bursty traffic patterns, such as chat interfaces or customer-facing dashboards, you're forced to overprovision to handle peak loads, resulting in the payment for idle capacity during quieter periods.

Redshift maxes out at 50 concurrent queries across all queues, making it extremely challenging to build customer-facing applications.

BigQuery's slot-based model can handle higher concurrency, but it requires large slot reservations to avoid queries queuing or being rejected. Typically, latency is minimal, in the 1-2 second range, even under ideal conditions.

ClickHouse's concurrency architecture: #

ClickHouse handles 1,000+ concurrent queries per node without artificial limits or performance degradation. The query pipeline processes multiple queries simultaneously using vectorized execution across all available CPU cores. Concurrency scales linearly - add nodes, multiply throughput: no queueing, no rejected queries, no exponential cost curves.

This makes ClickHouse suitable for workloads that traditional warehouses struggle with:

• Embedded analytics in SaaS products, where every customer has their own dashboard
• Operational dashboards are refreshing every few seconds for hundreds of users
• AI agents and chatbots are generating multiple queries per user interaction
• Customer-facing applications with unpredictable traffic patterns
• Product analytics with real-time event tracking and querying

If your use case is purely internal analytics with predictable query patterns, traditional warehouses work fine. However, if you're building anything user-facing, concurrency limits become the hidden constraint that determines whether your application can scale effectively.

Real-time data ingestion: Latency and limitations #

All modern data warehouses support some form of real-time or near-real-time data ingestion, but the latency, operational complexity, and cost implications vary significantly.

Ingestion latency comparison: #

Why ingestion latency matters:

For batch analytics workloads where data is loaded nightly or hourly, ingestion latency is barely a concern. But for operational dashboards, real-time monitoring, fraud detection, or AI agents querying live data, the difference between sub-second and 5-10 second latency compounds quickly.

The hidden costs and trade-offs: #

Snowflake achieves 5-10 second latency with Snowpipe Streaming, but this comes with additional charges beyond standard compute costs. The streaming ingestion service runs separately and is billed separately.

BigQuery can achieve ~1 second latency with streaming inserts, but there's a critical limitation: streaming inserts invalidate the query result cache. This creates a trade-off where real-time data ingestion degrades query performance, forcing you to choose between fresh data and fast queries. Additionally, recently streamed data cannot be modified, limiting your ability to handle late-arriving data or corrections. Streaming also incurs extra charges.

ClickHouse delivers sub-second ingestion latency natively through ClickPipes and maintains full compatibility with the query cache. You can continuously ingest streaming data while maintaining sub-second query performance. There are no architectural trade-offs between real-time ingestion and query speed.

When real-time ingestion matters: #

• Operational dashboards displaying metrics that update every few seconds
• Fraud detection and security monitoring, where delays can be costly
• IoT and sensor data from manufacturing, logistics, or smart devices
• User behavior analytics powering real-time personalization
• AI agents that need to query the most current data
• Event-driven applications reacting to business events as they occur

If your analytics workloads are primarily batch-oriented with scheduled ETL pipelines, the differences in streaming latency won't significantly impact your use case. But if you're building applications that depend on querying fresh data with minimal delay, ingestion latency becomes a critical architectural constraint.

Interactive query performance: Sub-second latency at scale #

Query performance isn't just about how quickly a single query completes; it's about whether your data warehouse can deliver consistently fast results at scale, especially for interactive applications where users expect immediate responses.

Query latency comparison: #

What "interactive" really means: #

When users interact with dashboards, explore data, or ask questions through a chat interface, they expect responses similar to a human conversation. A delay of even 2-3 seconds feels sluggish. Waiting 10-30 seconds completely breaks the conversational flow. This expectation applies whether you're building:

• Customer-facing analytics dashboards embedded in your product
• Operational dashboards for monitoring business metrics in real-time
• AI agents and chatbots that query data to answer user questions
• Product analytics where users slice and dice data interactively
• Internal tools where analysts explore data ad hoc

The performance gap: #

Even relatively simple queries in traditional data warehouses can take >1 second for basic operations, with more complex queries taking 5-30+ seconds. When chat applications or interactive dashboards generate multiple queries to answer a single user question, these delays cascade into minute-long waits.

ClickHouse delivers sub-100ms query latency for properly indexed queries. Aggregations over billions of rows complete in 50-500ms. This performance isn't achieved through caching tricks - it's the native query execution speed.

Hidden costs of "fast enough": #

Snowflake can achieve better performance, but it requires clustering (which incurs extra charges) and materialized views (available only in the enterprise tier). You're paying premium prices and managing additional complexity to achieve performance that may still not meet user expectations for truly interactive experiences.

BigQuery's architecture makes achieving sub-second latency consistently difficult. Even under ideal conditions, minimal latency is typically 1-2 seconds. For internal batch analytics, this is fine. For user-facing applications where every interaction generates multiple queries, it becomes a UX problem.

When query performance becomes critical: #

If your workload is scheduled reports, nightly ETL jobs, or occasional ad-hoc analysis by a small analytics team, query times measured in seconds are perfectly acceptable. But if you're building applications where data is queried in response to user actions - dashboards that refresh on every click, AI agents answering questions, real-time monitoring systems - sub-second performance stops being a nice-to-have and becomes a requirement.

The difference between a 200ms query and a 2-second query determines whether your application feels responsive or sluggish. Multiply that across hundreds or thousands of concurrent users, and performance becomes the defining characteristic of user experience.

Total cost of ownership: Beyond the sticker price #

The advertised price per TB-hour or per credit tells only part of the cost story. Hidden charges, architectural requirements, and workload-specific inefficiencies can significantly increase your actual spending beyond initial estimates.

Where hidden costs emerge: #

Feature gating and enterprise tiers: Do you need materialized views for acceptable query performance? In Snowflake, you’ll need to have the Enterprise Edition. Want sub-second latency at scale? You'll need clustering, along with materialized views, on the enterprise tier. These aren't optional optimizations - they're often requirements for production workloads, and they come with premium pricing.

Scaling for concurrency: Snowflake addresses high concurrency through multi-cluster warehouses, which multiply your compute costs. Each warehouse you add to handle concurrent users increases your spending. For unpredictable, bursty workloads, such as customer-facing analytics or AI agents, you're forced to overprovision for peak loads and pay for idle capacity during quieter periods.

BigQuery's slot-based model requires large reservations to achieve acceptable concurrency without queries queuing or being rejected. Without sufficient reserved slots, your application suffers; with them, you're paying whether you're using them or not.

Streaming and real-time ingestion: Snowflake charges an additional fee for Snowpipe Streaming beyond standard compute costs. BigQuery also adds streaming ingestion fees on top of base costs. These charges accumulate quickly for high-volume, real-time workloads—exactly the scenarios where streaming matters most.

Storage costs and compression efficiency: Compression ratios vary significantly between platforms, and these differences compound over time. Based on benchmarks, ClickHouse achieves 38% better compression than Snowflake and 60% better compression than BigQuery. Over years of data retention, this translates to substantial storage cost differences. Better compression also means faster query performance due to reduced input/output (I/O) operations.

Observability workloads: Logs, metrics, and traces generate massive write volumes. The comparison data shows that while ClickHouse natively supports observability workloads through ClickStack, platforms like Snowflake find observability use cases cost-prohibitive, and BigQuery's charges for continuous data writes make this expensive. If you're consolidating analytics and observability, the choice of platform has a significant impact on economics.

Real-world cost comparisons: #

Companies migrating from traditional data warehouses to ClickHouse report significant savings:

• 75% reduction in cost when moving from Redshift to ClickHouse
• Vantage cut their Redshift bill in half: "Moving over to ClickHouse, we were basically able to cut that (Redshift) bill in half" - Brooke McKim, Co-founder and CTO.
• Jerry achieved a 20x query performance improvement while significantly reducing costs by switching from Redshift.
• Rokt's benchmark analysis discovered that ClickHouse was three times less expensive than Redshift.

Calculate TCO for your actual workload: #

Don't rely on vendor calculators that assume ideal workloads. Instead, factor in:

• Concurrency patterns: How many concurrent queries do you actually need? What does scaling to that level cost?
• Real-time requirements: Do you need streaming ingestion? What are the additional fees?
• Performance requirements: What does achieving sub-second latency actually cost on each platform?
• Data retention: How does compression efficiency affect your multi-year storage costs?
• Enterprise features: Which features require premium tiers, and are they necessary for your use case?
• Workload characteristics: Batch analytics, real-time streaming, customer-facing applications, or observability each have different cost profiles

The platform with the lowest advertised price often becomes the most expensive once you layer in the features, performance, and scale your production workload actually requires. Calculate TCO based on your real usage patterns, not vendor benchmarks.

Data Format and Integration Versatility: Reducing Pipeline Complexity #

The modern data stack encompasses data in numerous formats across various systems. The ease with which your data warehouse integrates with existing data sources and file formats directly impacts pipeline complexity, development velocity, and operational overhead.

Format support comparison: #

Why format versatility matters: #

Every additional ETL step adds latency, complexity, and failure points. If your data warehouse natively supports reading from diverse sources and formats, you can:

• Query data where it lives: Read directly from PostgreSQL, MongoDB, or S3 without extracting and loading
• Reduce pipeline complexity: Fewer transformation steps mean fewer things that can break
• Accelerate time to insights: Skip the "wait for ETL to finish" step and query immediately
• Lower operational costs: Less data movement, fewer transformation jobs to maintain

ClickHouse's integration approach: #

ClickHouse can connect directly to external data sources through table engines. Want to join data from your PostgreSQL operational database with analytics data in ClickHouse? Create a PostgreSQL table engine and query it directly. Need to process streaming data from Kafka? Use the Kafka table engine. S3 data in Parquet format? Query it in place.

ClickHouse handles 70+ file formats out of the box, including:

• Columnar formats: Parquet, ORC, Arrow
• Row-based formats: CSV, JSON, Avro
• Specialized formats: Protobuf, MessagePack, Cap'n Proto
• Open table formats: Iceberg, Delta Lake, Hudi

The limitations of other platforms: #

Snowflake and BigQuery support the most common formats, including Parquet, CSV, JSON, and ORC. However, working with less common formats or querying external systems typically requires ingestion first. You can't simply point at a PostgreSQL database and query it; you need to extract, transform, and load the data into a suitable format.

BigQuery's BigLake offers some capabilities for querying data in object storage (S3, GCS, Azure Blob), but it's limited to specific formats and doesn't extend to querying live operational databases.

Support for open table formats: #

All major platforms now support open table formats, such as Apache Iceberg, which allows you to bring your preferred analytics engine to your data without needing to move it. However, ClickHouse's broader format support means you're not locked into a specific ecosystem or forced to standardize on particular formats before you can query your data.

When integration versatility matters: #

• Hybrid architectures: You need to query both your data warehouse and operational databases
• Data lake analytics: You want to query data in S3/GCS without loading it
• Streaming and batch: You're processing data from Kafka, Kinesis, or other streaming sources
• Polyglot data environments: Your data exists in many formats across different systems
• Rapid prototyping: You want to explore data without building ETL pipelines first

If your data is already in Parquet format on S3 and you're building traditional batch pipelines, format support differences may be a significant concern. But if you're working with diverse data sources, operationalizing analytics, or building data products that combine operational and analytical data, native integration capabilities become a significant differentiator.

Update and mutation capabilities: Handling data changes #

Analytics isn't always append-only. Real-world data warehouses must handle corrections, late-arriving data, regulatory compliance (such as GDPR deletion requests), and slowly changing dimensions. The efficiency and cost-effectiveness with which platforms handle updates and mutations vary significantly.

Why update capabilities matter: #

In practice, data isn't perfect. You'll encounter scenarios where mutations are essential:

• Late-arriving data: Events arrive out of order and need to update earlier records
• Data corrections: Source systems send corrections that need to overwrite existing data
• GDPR and compliance: Right-to-be-forgotten requests require deleting or anonymizing specific user records
• Slowly changing dimensions: Customer addresses, product categories, or organizational hierarchies change over time
• Change Data Capture (CDC): Replicating operational databases requires handling updates and deletes
• Deduplication: Removing duplicate records after ingestion

BigQuery's update limitations: #

BigQuery supports row-level updates, but with two significant constraints:

1. Extra charges for changes: Updates incur additional costs beyond standard query charges
2. Recently streamed data can't be modified: Data that was just ingested via streaming inserts is locked from modification for a period of time.

This second limitation creates a fundamental conflict: if you're streaming data in real-time (achieving that ~1 second latency for ingestion), you can't immediately correct or update the data if issues are discovered. You have to wait. For use cases requiring both real-time ingestion and the ability to handle corrections or late-arriving data, this creates an architectural constraint.

ClickHouse and Snowflake's approach: #

Both ClickHouse and Snowflake support efficient row-level updates without these streaming-related restrictions. You can continuously ingest data and modify it as needed without waiting periods or architectural trade-offs.

ClickHouse specifically optimizes for efficient mutations through its MergeTree engine family, which handles updates and deletes efficiently even at scale. Combined with ClickPipes CDC support for MySQL and PostgreSQL, you can replicate operational databases with full support for inserts, updates, and deletes.

When mutation capabilities become critical: #

• CDC pipelines: Replicating transactional databases into your warehouse
• Real-time + corrections: You need both streaming ingestion and the ability to fix data immediately
• Compliance requirements: GDPR, CCPA, or other regulations requiring timely data deletion
• Data quality workflows: Automated correction of data quality issues
• Deduplication strategies: Handling duplicate records in near real-time
• Incremental updates: Refreshing dimension tables with slowly changing attributes

If your data warehouse is purely append-only with scheduled batch loads, update capabilities may not matter much. But if you're handling streaming data, implementing CDC, managing compliance requirements, or building data quality workflows, the ability to update and delete data efficiently - without extra charges or waiting periods - becomes essential.