A team choosing a warehouse is also choosing how it pays for compute and how much of the infrastructure it wants to manage. For teams weighing redshift vs snowflake vs bigquery 2026, the best choice depends on cloud ecosystem, query patterns, budget, and day-to-day analytics needs.
Each platform handles storage and compute differently, so the same workload can bring different costs and tasks for your team. This comparison offers direct starting points for common workload types, then shows how to check them against your own data and query habits.
Cloud fit matters, but it is not the only factor. A team with steady reporting needs may value a different operating model than one running irregular analysis or serving several independent workloads.
Key Takeaways
- BigQuery suits variable analytics in Google Cloud.
- Snowflake suits teams that need separate compute for different workloads.
- Redshift suits AWS-centered teams with steady demand or a preference for provisioned capacity.
- Each platform offers a different way to manage compute and storage.
- Test actual query patterns and capacity settings before you commit.
The most useful comparison starts with each platform’s operating model, then tests that model against your workload.
Quick Answer: Which Warehouse Fits Your Team?
For a GCP-native team running irregular analytics, BigQuery is the simplest default; AWS-heavy teams should start with Redshift, while multi-cloud teams should evaluate Snowflake. These are starting points, not universal rankings.
A slot is a unit of computational capacity BigQuery uses to execute queries. Snowflake runs on AWS, Azure, and Google Cloud; Redshift is AWS-native, and BigQuery is Google Cloud’s data warehouse.
- BigQuery: Choose it for serverless, variable ad hoc analytics, especially when your data and tools already sit in Google Cloud.
- Snowflake: Consider it for multi-cloud teams that need separate compute for distinct workloads.
- Redshift: Start here if your team is invested in AWS and prefers provisioned capacity for steady demand.
Treat the shortlist as a hypothesis to test against real query patterns, not as a platform ranking.
Redshift also has a Serverless option, so AWS teams can avoid managing a provisioned cluster. That option still has capacity settings and operating costs to consider.
Cloud ecosystem can shape data movement, team skills, and service connections as much as the warehouse itself. A broader AWS, Azure, and Google Cloud comparison can help frame those wider trade-offs.
The likely fit changes when your workloads shift from steady scheduled jobs to unpredictable analysis, or when teams share data across clouds. Treat each platform as a practical choice for a set of conditions, not a winner for every task.
How Do Redshift, Snowflake, and BigQuery Separate Storage and Compute?
Redshift offers two distinct operating modes, while Snowflake separates workload compute and BigQuery abstracts cluster management altogether. The main difference is how much capacity you control directly and how much the platform manages.
Choose an architecture based on who will monitor capacity and how often workloads change. The storage compute relationship affects scaling, workload isolation, and the daily work needed to keep queries moving.
Redshift’s provisioned and serverless options
In a provisioned Redshift cluster, nodes hold and process data across the cluster. Your team selects node capacity and can scale it as demand changes.
That model gives you direct control, but provisioned capacity can need attention even when query demand shifts. Redshift Serverless removes the need to manage a cluster, while using Redshift Processing Units, or RPUs, as its capacity measure.
Provisioned clusters can suit steady, known workloads where your team wants control over capacity. Serverless can reduce cluster tasks when demand varies, though you still need to understand its capacity settings.
BigQuery’s serverless capacity
BigQuery is serverless, so users do not provision clusters or resize nodes. Google Cloud manages the underlying infrastructure while you run queries and set up capacity choices.
Its slot model represents compute available to execute queries. A slot reservation lets you set aside capacity for workloads, rather than managing a group of warehouse nodes yourself.
This abstracted approach can limit routine infrastructure work. It also means teams must understand how their selected capacity model responds to changing analytics demand.
Snowflake’s independent virtual warehouses
Snowflake uses virtual warehouses, which are compute clusters that can scale independently while accessing shared data. You can assign separate virtual warehouses to tasks such as dashboards, data loading, and analyst queries.
This separation can keep one workload from taking all the compute used by another. Teams can adjust each virtual warehouse for its own demand, but they must still set rules for sizing and use.
The storage and compute model changes how much direct control your team has over scaling and workload separation. Match the architecture to who will monitor capacity, not just to the cloud where your data already lives.
Redshift vs Snowflake vs BigQuery 2026: How Does Pricing Work?
The most important pricing difference is what starts the meter: scanned bytes, active warehouse time, or provisioned capacity. In 2026, BigQuery on-demand query pricing is about $6.25 per TB scanned, with the first 1 TB scanned per month typically free.
Compare compute and storage as separate costs when the platform bills them separately. Region, edition, capacity choice, and the balance of storage and compute all affect your final bill.
| Platform | Compute model | Example 2026 compute rate | Storage or scan rate |
|---|---|---|---|
| BigQuery | Bytes-scanned pricing or slot reservation | $6.25 per TB scanned; Standard slots $0.04 per slot-hour | $20/TB-month active; $10/TB-month long-term |
| Snowflake | Credits for virtual warehouse time | Standard credit $2.00 in AWS US-East; X-Small 1 credit/hour, Medium 4 credits/hour | About $23/TB-month on-demand storage in US regions |
| Redshift | RA3 node-hours or Serverless RPU-seconds | RA3.xlplus node $1.086/hour; Serverless entry $1.50/hour | About $24.58/TB-month managed storage; Spectrum $5/TB scanned |
BigQuery scan charges and slot reservations
BigQuery on-demand pricing is based on bytes scanned. Standard slot reservations cost $0.04 per slot-hour in 2026; Enterprise reservations cost $0.06, and Enterprise Plus costs $0.10.
Active BigQuery storage costs $20 per TB each month, while long-term storage costs $10 per TB each month. A reservation shifts the compute bill from individual scan volume to reserved slots.
Snowflake credits and storage
Snowflake charges for warehouse compute time rather than directly for bytes scanned. A Snowflake Standard credit costs $2.00 in AWS US-East in 2026, while prices vary by region and edition.
A Snowflake X-Small virtual warehouse uses one credit per hour, and a Medium uses four credits per hour. Storage in US regions costs about $23 per TB monthly on demand.
The Snowflake compute billing rules explain how warehouse size and runtime affect credit use, including the minimum charge when a warehouse starts or resumes.
Redshift node-hours, RPUs, and storage
RA3 provisioned clusters charge by node-hour, even when queries are not running. A ra3.xlplus node costs $1.086 per hour on demand in 2026.
Redshift Serverless charges per RPU-second while queries run, with a minimum capacity of four RPUs and an entry rate of $1.50 per hour. Redshift managed storage costs about $24.58 per TB monthly.
Redshift Spectrum charges $5 per TB scanned when querying data in S3. Budget storage and compute as separate line items wherever the platform bills them separately.
What Does a Query Actually Cost on Each Platform?
A short query’s bill can be driven by the time needed to resume its compute, not just by the seconds the SQL takes to run. For Snowflake, warehouse compute bills by the second, with a typical 60-second minimum each time a warehouse resumes.
Frequent brief jobs can pay for more compute time than their SQL runtime suggests. The same concern does not apply in the same way to BigQuery bytes-scanned pricing or Redshift Serverless RPU-seconds.
Small and frequent Snowflake queries
A Snowflake Medium virtual warehouse uses four credits per hour. At $3 per credit, 30 seconds of compute costs about $0.10.
One 30-second query that resumes a suspended warehouse may face the typical 60-second minimum. That charge can make a small job cost more than its run time alone would suggest.
In a 2026 example, a 30-second query on a Medium warehouse costs about $0.03-$0.07, depending on the credit price. This range reflects different credit prices, not a fixed charge for every account.
A queue of small scheduled jobs can cause repeated wake-ups if the virtual warehouse suspends between them. Grouping jobs may reduce starts, while keeping a warehouse active can add idle compute time.
BigQuery scan examples and cache hits
BigQuery on-demand pricing scales with bytes scanned. In a 2026 example, scanning 1 GB costs about $0.00625, 100 GB costs $0.625, and 1 TB costs $6.25.
Partitioning and filtering can reduce the data scanned, which lowers charges under bytes-scanned pricing. A query-result cache hit is free under BigQuery on-demand pricing.
A cached dashboard query may therefore have no on-demand scan charge, while a fresh query that reads a large table may cost more. Scan volume, not elapsed time alone, drives this pricing model.
Redshift Serverless offers a different example: a 30-second query at 32 RPUs costs about $0.13-$0.16 in 2026. That figure and the Snowflake example do not represent identical workloads or a direct performance test.
Check how often automated jobs wake compute before estimating the cost of frequent, brief queries.
How Do Modeled Workloads Change the Total Cost?
The modeled 10TB and 100TB scenarios produce different cost leaders, so workload size alone is not enough to predict a bill. At 10TB, BigQuery has the lowest modeled three-year total; at 100TB, it still has the lowest modeled total.
These estimates depend on workload assumptions and platform configuration. The Redshift vs Snowflake vs BigQuery cost comparison can change with utilization and the chosen pricing model.
| Platform | 10TB three-year TCO | 100TB three-year TCO | Modeled monthly compute at 10TB |
|---|---|---|---|
| BigQuery | $29K | $244K | $565 on-demand |
| Snowflake | $124K | $331K | $3,168 |
| Redshift | $63K | $411K | $1,450 |
The modeled 10TB three-year totals are $29K for BigQuery, $63K for Redshift, and $124K for Snowflake. Monthly compute estimates for that workload are about $565 for BigQuery on-demand, $1,450 for Redshift, and $3,168 for Snowflake.
At 100TB, the modeled three-year totals are $244K for BigQuery, $331K for Snowflake, and $411K for Redshift. The modeled monthly compute estimates are about $4,320 for BigQuery Enterprise, $6,336 for Snowflake, and $8,500 for Redshift.
Those 100TB compute estimates use different capacity choices, including BigQuery Enterprise. They are not a universal price forecast for every team with 100TB of data.
Utilization changes the result because active compute and idle periods differ across pricing models. A team that runs queries all day may see a different balance than one with a few scheduled batches.
Storage needs, query frequency, selected capacity, and scan volume can all shift a modeled total. A model can guide a first budget, but it cannot replace your workload history.
Rebuild any estimate with your own query history and the exact capacity settings you expect to use.
Which Platform Is Fastest in a Comparable Benchmark?
In the supplied SF100 run, Snowflake’s 4X-Large configuration finished fastest, while BigQuery’s 2,000-slot run had a lower compute charge than the listed Snowflake 4X-Large result. Speed alone does not show which platform will suit your query performance needs.
The benchmark ran 22 queries over 100 GB and 866 million rows. Its configurations differed, so the results are not a controlled head-to-head test of equal capacity.
| Platform and configuration | Elapsed time | Compute cost |
|---|---|---|
| BigQuery, 2,000 slots | 26.2 seconds | $0.163 |
| Redshift Serverless, 128 RPUs | 30.7 seconds | $0.436 |
| Snowflake Large | 15.9 seconds | $0.143 |
| Snowflake 4X-Large | 14.7 seconds | $2.121 |
BigQuery and Redshift Serverless results
BigQuery with 2,000 slots completed the workload in 26.2 seconds, with $0.163 in compute cost. Redshift Serverless with 128 RPUs finished in 30.7 seconds and cost $0.436.
Those timings show the result for the selected capacity and this particular 22-query workload. A different mix of joins, filters, and table sizes can change the order.
Snowflake results by warehouse size
Snowflake Large finished in 15.9 seconds and cost $0.143 in compute. Snowflake 4X-Large finished in 14.7 seconds and cost $2.121.
The 4X-Large virtual warehouse finished sooner than Large, but its listed compute cost was much higher. Faster runtime does not automatically mean lower compute cost.
Benchmark results are useful for narrowing the configurations you test, not for predicting every production query. Use the benchmark to shortlist configurations, then repeat the test with your own queries and data.
Which Integrations and Data Features Matter?
Teams already invested in AWS can connect Redshift to S3, Glue, SageMaker, Lake Formation, and IAM, while Snowflake and BigQuery bring different cloud ecosystems and ingestion paths. For AWS teams, the choice is not a simple yes-or-no decision.
Redshift has the closest native AWS service integration, while Snowflake runs across cloud providers and BigQuery centers on Google Cloud. The right fit depends on how your pipelines move data and which services your team already uses.
AWS services, Redshift, and streaming
Redshift connects with AWS services such as S3, Glue, SageMaker, Lake Formation, and IAM. For an AWS workload, those links can fit existing data access and analytics patterns.
Redshift supports streaming through Kinesis Data Firehose and Redshift Streaming Ingestion. It also supports JSON through SUPER, a data type for semi-structured values.
Redshift Spectrum can query data stored in S3 without first loading it into warehouse tables. Spectrum charges $5 per TB scanned in 2026, so querying external files is different from loading them.
Google Cloud services and BigQuery ingestion
BigQuery connects with Google Cloud services such as Looker, Dataflow, and Pub/Sub. Its Storage Write API and Dataflow support streaming ingestion.
BigQuery supports STRUCT, ARRAY, and JSON data types. Those types can help teams work with nested records and semi-structured fields in analytics queries.
Google Cloud also offers BigQuery ML and Vertex AI integration for analytics and machine-learning tasks. Teams that build models may also want to consider the programming languages used for data science and machine learning.
Snowflake data types and ingestion options
Snowflake supports semi-structured data through VARIANT. Its ingestion options include Snowpipe and Snowpipe Streaming, which support different ways to load incoming data.
Snowflake Cortex and Snowpark add analytics and machine-learning capabilities. Snowpark lets teams work with data through supported programming languages and tools.
For Redshift vs Snowflake vs BigQuery for AWS, compare the services each pipeline actually needs, not only the platform name. Test the exact file formats and ingestion route your pipelines use, especially when semi-structured fields drive important queries.
How Do Concurrency and Maintenance Affect Day-to-Day Work?
A warehouse that performs well for one analyst may queue or slow down when dashboards and scheduled jobs run together. Concurrency changes how users experience the platform during busy periods.
Test peak traffic with multiple workload types running at once. Redshift workload management uses WLM queues, BigQuery capacity planning can use slot reservations, and Snowflake can use multi-cluster virtual warehouses.
Managing simultaneous workloads
These controls are not interchangeable features. WLM queues help manage Redshift workloads, slot reservations set BigQuery capacity, and Snowflake multi-cluster warehouses can add compute for concurrency.
A dashboard user and a scheduled ETL job may need different response times. Separating interactive analytics from batch work can make the pressure points easier to spot.
- Identify peak concurrent users, including dashboard viewers and analysts.
- Separate interactive analytics from scheduled ETL workloads.
- Set workload-specific capacity or queue controls.
- Review idle compute and routine maintenance tasks.
Keeping maintenance and idle time under control
Maintenance tasks can compete with user queries if they run during busy periods. Track when refreshes, loads, and other recurring jobs use warehouse resources.
Provisioned Redshift RA3 clusters keep charging by node-hour when queries are not running. Redshift Serverless charges while queries run, so steady utilization changes the operational trade-off.
Snowflake virtual warehouses can suspend when idle, while separate warehouses can isolate jobs that need different capacity. The value of that separation depends on how often each workload runs.
Review concurrency and idle-capacity behavior during the same peak window your users actually experience.
What Security and Data-Residency Checks Matter?
Start the security review by mapping each user group to the data it needs and the audit trail your organization must retain. Access and audit needs should shape the platform setup before sensitive data arrives.
Check identity, audit events, private connectivity, encryption, and supported regions for each platform. AWS IAM, Google Cloud IAM, and Snowflake roles provide platform-specific ways to manage identity and access.
Identity, permissions, and audit trails
Map users and service accounts to the data they need, then review how each role grants access. AWS IAM and Google Cloud IAM follow their cloud environments, while Snowflake uses role-based access controls.
Check whether audit events capture the activity your organization needs to review. Confirm who can view and retain those records, including events tied to data access and administrative changes.
NIST SP 800-53 includes control families for access control, audit, and communications protection. Use those areas as reference points when your organization maps technical settings to security policy.
Network access and regional controls
Review private connectivity options and confirm how each platform encrypts data in transit and at rest. Check that the network design supports the users and services that need access.
Supported regions differ by platform and service, and data residency rules may affect where workloads can run. Confirm region availability and applicable organizational or legal obligations with the appropriate security and compliance owners.
A role that works in a test environment may grant too much access in production. Validate permissions and private network access in a production-like environment before loading sensitive data.
What Should You Plan Before Migrating?
A warehouse migration is not complete when the data files arrive; reports, transformations, permissions, and scheduled jobs must still behave correctly. File transfer is only one part of the work.
Inventory the connected data, code, jobs, and tools before moving a production workload. A migration can affect downstream models and users even when source files load without errors.
Moving data and pipelines
Start with source tables and formats, including files in S3 or Parquet. Identify which data must move, which data can stay in place, and how the target warehouse will read it.
Scheduled ingestion and transformation jobs need their own review. A job may depend on a source path, SQL behavior, or permission that changes during migration.
Reworking queries and downstream tools
SQL dialects and data types can differ across platforms. Check functions, date handling, nested fields, and data conversions that affect important queries.
BI dashboards and application queries also need testing. A dashboard can load successfully but still show a different result if a model or filter changes.
- List source tables, files, and formats, including S3 and Parquet where used.
- Review SQL dialects and data-type differences.
- Inventory scheduled ingestion and transformation jobs.
- Identify BI dashboards and application queries that depend on the warehouse.
Include permissions and downstream models in the test plan, not just the data itself. Teams should compare key results across systems before turning off the old warehouse.
Run old and new systems side by side until critical reports and recurring pipelines produce verified results.
Which Warehouse Should Your Team Choose?
Choose according to the constraint your team most wants to remove: infrastructure management, cloud separation, or a mismatch with its existing ecosystem. For most teams, the strongest choice is the platform that fits both current skills and repeated workloads.
BigQuery fits serverless Google Cloud analytics, Redshift fits AWS-centered workloads, and Snowflake fits multi-cloud teams that need separate compute pools. Each choice has a condition that could point you elsewhere.
Choose BigQuery for serverless GCP analytics
Choose BigQuery if your team prioritizes serverless analytics in Google Cloud. It does not require users to provision clusters or resize nodes, which can reduce routine capacity management.
An established non-GCP environment could make another platform a better fit, especially if data and tools already depend on a different cloud. Google Cloud’s wider service mix matters when you compare the full workload, not just query execution.
Choose Redshift for AWS-centered workloads
Choose Redshift when your team is centered on AWS and its existing services and capacity needs fit Redshift’s operating modes. Provisioned clusters suit teams that want direct capacity control; Redshift Serverless offers another mode.
If minimizing cluster management is a priority, provisioned Redshift may not fit as well. Compare its Serverless option with other platforms against how steady or variable your analytics demand is.
Choose Snowflake for multi-cloud and workload isolation
Choose Snowflake if your team works across clouds or benefits from separately scaled compute. Snowflake runs on AWS, Azure, and Google Cloud, and its virtual warehouses can scale independently while accessing shared data.
A team with a strong single-cloud fit may prefer its cloud’s native warehouse instead. Snowflake is a stronger candidate when distinct groups need separate compute or the cloud plan spans providers.
Your choice should reflect how your team operates now and what its analytics workloads will need repeatedly. Pick the platform that fits both the cloud you operate today and the workload you expect to run repeatedly.
How Should You Validate Your Choice in a Pilot?
A useful pilot measures a small set of representative workloads on the same data, not a single showcase query. Realistic tests reveal whether performance and cost hold up beyond one clean run.
Use identical data, comparable capacity, and realistic concurrency to compare results. A toy dataset or one query can hide both cost patterns and contention behavior.
- Select representative queries from dashboards, ETL jobs, and analyst ad hoc work.
- Use equivalent data and comparable capacity settings across platforms.
- Record runtime, bytes scanned, and billed compute.
- Repeat the tests under realistic concurrency.
Record elapsed seconds alongside bytes scanned, compute credits, or RPU-seconds. Note idle periods too, since they can change the cost of running the same workload.
A single query cannot show how a warehouse handles a busy workday. Test distinct workload types together before choosing capacity.
Keep the data size and query text consistent when you compare results. Capacity settings should be close enough to make the test useful, while still reflecting how your team would run each platform.
Include dashboards, scheduled ETL, and ad hoc analyst queries as separate workload types. A system that handles one well may behave differently when all three compete for capacity.
Keep the test log of SQL, data size, capacity settings, runtimes, and billed compute so the final choice can be reviewed later.
Conclusion
There is no single winner for every team: the best fit follows from your cloud environment, workload pattern, and tolerance for capacity management. BigQuery suits serverless Google Cloud analytics, Redshift fits AWS-centered workloads, and Snowflake is a strong candidate for multi-cloud teams needing independent compute pools.
Before you commit, test representative queries and check expected utilization across busy and quiet periods. Those results can show whether the platform’s operating model fits your team as well as its feature set.
Choose two finalists, run the same representative workload on both, and compare runtime, billed compute, and operating effort.
FAQ
Is BigQuery cheaper than Snowflake for a small team?
BigQuery can cost less for a small team with low scan volume, especially because its first 1 TB scanned per month is typically free in 2026. After that, bytes scanned and query design affect charges. Snowflake bills warehouse compute, and a typical 60-second minimum applies when a warehouse resumes, which can matter for frequent short jobs.
Is Redshift Serverless cheaper than BigQuery?
Neither is always cheaper; compare Redshift Serverless RPU-seconds with BigQuery bytes scanned or slot reservations. A team running a few small queries may see a different result from one scanning large tables all day. Query frequency, scan volume, capacity settings, and idle periods all affect which pricing model works better.
How much does a 30-second Snowflake query cost?
A 30-second query on a Snowflake Medium virtual warehouse costs about $0.03-$0.07, depending on the credit price. At $3 per credit, the modeled compute cost is about $0.10 for 30 seconds. A typical 60-second minimum when the warehouse resumes can affect the bill for a short query.
Can Snowflake run on AWS and Google Cloud?
Yes, Snowflake runs on AWS, Azure, and Google Cloud. Your account’s region affects where its data and compute operate, and data movement needs can shape the setup. Consider where users, source data, and connected services run before choosing a cloud region for a new workload.
Is Redshift Serverless fully cluster-free?
Redshift Serverless removes the need to manage a provisioned cluster, but it still uses capacity measured in RPUs. Its minimum capacity is four RPUs, with a stated entry price of $1.50 per hour in 2026. Teams still need to understand capacity settings and how their query patterns use resources.
Which platform is best for an AWS-based analytics team?
Redshift is the AWS-native warehouse, making it a natural starting point for teams already using AWS services. Snowflake also runs on AWS, so it may fit teams that need multi-cloud options or separate compute pools for distinct workloads. Compare service integration, capacity needs, and team operations before deciding.
