Snowflake vs BigQuery vs Redshift 2026 — Best Pick

Snowflake, Google BigQuery, and Amazon Redshift are the three cloud data warehouses most teams shortlist, and picking the wrong one costs money in a specific way: not through slow queries, but through a bill that behaves differently than you expected. All three store data in columns, run SQL, and scale well past a single server. They differ in where they run, how they charge, and how much of the machinery you manage. This guide compares them on architecture, pricing mechanics, concurrency, recovery features, and ecosystem, then maps each to the situations where it fits.

The short answer

If your situation is...	Start by looking at
Already standardized on Google Cloud, or heavy Google Analytics and Ads data	BigQuery
Standardized on AWS, with data lake files in S3	Redshift
Multiple clouds, or data sharing with outside organizations	Snowflake
Spiky, unpredictable query load and no appetite to size clusters	BigQuery on-demand, or Redshift Serverless
Steady, heavy workload where you want predictable spend	Provisioned Redshift, BigQuery capacity (Editions), or a sized Snowflake setup
Many teams competing for compute at once	Snowflake multi-cluster warehouses, or BigQuery reservations

If you are starting fresh with no strong constraint, the cloud you already pay for usually decides it. Moving data between clouds costs money and time, and warehouses are the biggest data gravity wells in most companies. This is advice about fit, not a ranking. All three are mature products that large companies run in production.

How a cloud warehouse differs from a normal database

A traditional database such as PostgreSQL stores each row together and is optimized for many small reads and writes, like looking up one customer or updating one order. A data warehouse stores each column together and is optimized for scanning huge numbers of rows to compute aggregates, like total revenue by region across three years.

Think of two ways to keep a library's records. A row store is a card catalog with one drawer per book. A column store keeps one drawer for all titles, another for all authors, another for all publication years. Asking "how many books were published after 1990?" means opening one drawer, not every card.

The cloud generation added a second idea: separate storage from compute. Data sits in cheap, durable object storage, and you rent compute only when you need it. That lets you scale each independently and lets several compute clusters read the same data. All three products now work this way in their current forms, though they got there by different routes.

Before comparing them, it helps to understand where each came from.

Three origins, three personalities

BigQuery grew out of Google's internal Dremel technology and was announced in May 2010 at Google I/O, according to Wikipedia. It was serverless from the start, meaning there are no clusters for users to create. That heritage still shapes it: you submit SQL, Google allocates capacity, and billing follows either the data your query processes or the slot capacity you reserve.

Redshift was built on technology from ParAccel, a company that made a massively parallel processing (MPP) warehouse. It was based on an older PostgreSQL version (8.0.2), which Amazon modified. AWS announced it as a limited preview at its first re:Invent conference on November 28, 2012 (see AWS What's New), and it became fully available on February 15, 2013 (see Amazon Redshift). It began as a cluster you size and manage, and added decoupled storage (RA3 nodes) and a serverless option later.

Snowflake was founded on July 23, 2012, in San Mateo, California, by Benoit Dageville, Thierry Cruanes, and Marcin Zukowski, and went public in September 2020, raising $3.4 billion (see Snowflake Inc.). It was built for the cloud from the beginning, separates compute from storage, and runs on Amazon Web Services, Microsoft Azure, and Google Cloud.

The personalities that follow from this: BigQuery is the "no infrastructure" option, Redshift is the "inside AWS" option, and Snowflake is the "independent, runs anywhere" option. Each has moved toward the others over time, so the differences are narrower than the marketing suggests.

Snowflake: three layers and independent warehouses

Snowflake's documentation describes its architecture as a hybrid of shared-disk and shared-nothing designs. Persistent data lives in one central repository that all compute can reach, and queries run on MPP compute clusters where each node holds part of the data locally. It has three layers:

• Database storage. Tables are stored in an optimized, compressed columnar format in cloud storage, automatically divided into micro-partitions. Apache Iceberg tables can instead keep data in external cloud storage that you manage.
• Compute (virtual warehouses). "Each virtual warehouse is an independent compute cluster," so one team's heavy query does not slow another team's dashboard.
• Cloud services. Authentication, access control, metadata, and query parsing and optimization.

The independence of warehouses is the feature people mention first. You can run a warehouse for nightly loads, another for analysts, and another for a BI tool, all reading the same tables, and none of them waiting on the others.

Snowflake cannot run on-premises or on private cloud infrastructure; the documentation says so explicitly.

How Snowflake charges

Compute is billed in credits. Credits per hour depend on warehouse size, and each size step roughly doubles both the compute power and the credits billed per hour. The documentation lists Small at two credits per hour and Medium at four. Billing is per second with a 60-second minimum each time a warehouse starts or resumes, and a suspended warehouse uses no credits.

That last point is the heart of Snowflake cost management. A warehouse left running while idle burns credits. Consider a Medium warehouse (four credits per hour):

Usage pattern	Credits per month (approx.)
Runs 8 hours a day, 22 working days	4 x 8 x 22 = 704
Left running 24 hours a day, 30 days	4 x 24 x 30 = 2,880

Same warehouse, same queries, roughly four times the credits. Multiply by your contracted price per credit, which depends on your edition, cloud, and region and is set in Snowflake's consumption table and your agreement. Setting an auto-suspend timeout so warehouses stop when idle is the single most effective control.

Storage is billed separately, on average compressed usage per month. Snowflake's pricing page also describes two purchase options: on-demand, month to month, or pre-paid capacity.

Editions matter for features

Snowflake sells Standard, Enterprise, Business Critical, and Virtual Private Snowflake editions. Several headline features are edition-gated:

• Multi-cluster warehouses (which add clusters automatically when queries queue) are an Enterprise feature. In auto-scale mode Snowflake starts and stops clusters based on load, and the documentation suggests starting with a small range, such as a maximum of two or three clusters.
• Extended Time Travel, up to 90 days, requires Enterprise or higher. Standard allows zero or one day.
• Business Critical adds items such as Tri-Secret Secure, private connectivity, and failover and failback for disaster recovery.

A comparison that quotes Snowflake's strengths without naming the edition can mislead you into budgeting for the wrong tier.

Secure data sharing

Snowflake's data sharing lets a provider expose tables, views, and functions through a share, and, per the documentation, "no actual data is copied or transferred between accounts." Consumers pay only for the compute they use to query it. Providers can revoke access at any time, and reader accounts let you share with a party that has no Snowflake account, with the provider paying for that account. Direct shares work only within the same region; cross-region or cross-cloud sharing uses listings.

This is a real differentiator for companies that exchange data with partners, customers, or vendors, because the alternative is usually exporting files and keeping them in sync.

BigQuery: serverless, with two ways to pay

BigQuery has no cluster to create. Its billing, however, gives you a choice between two models, and the choice has more effect on your bill than any query tuning.

On-demand charges by the bytes your queries process, with a free monthly allowance. Capacity-based pricing through BigQuery Editions charges for slot capacity, where a slot is a unit of compute, billed in slot-hours with a one-minute minimum by default. Editions come in three tiers, according to Google's editions documentation:

Edition	Availability target	Capacity model	Notable differences
Standard	At least 99.9%	Autoscaling only, no baseline, 1,600-slot maximum reservation, no commitments	No BI Engine, BigQuery ML, Omni, CMEK, or VPC Service Controls
Enterprise	At least 99.99%	Autoscaling plus baseline	Adds BI Engine, ML, Omni, CMEK, VPC Service Controls, commitments
Enterprise Plus	At least 99.99%	Same as Enterprise	Adds managed disaster recovery and Assured Workloads compliance controls

Commitments are optional. Enterprise and Enterprise Plus offer a one-year commitment at a 20% discount and a three-year commitment at a 40% discount. The documentation also notes that on-demand pricing can be used alongside editions on a per-project basis, and that changing a reservation's edition means deleting and recreating it. Current per-TiB and per-slot-hour rates are on Google's BigQuery pricing page, and they vary by region, so check them rather than trust a number in a blog post.

Why on-demand bills surprise people

With on-demand pricing you pay for bytes processed, not time spent and not rows returned. Google's cost-control guidance spells out the consequences:

• A LIMIT clause does not reduce cost on a non-clustered table, because the data is read before the limit applies. On clustered tables it can reduce bytes scanned.
• Because storage is columnar, selecting fewer columns processes fewer bytes. SELECT * on a wide table is the classic expensive mistake. If a query touches three of 60 equally sized columns, it reads on the order of 5% of the table.
• A dry run or the console's query validator shows how many bytes a query would process before you run it. The estimate is an upper bound.
• You can cap spending per query with maximum bytes billed (a query over the cap fails without charge) and per day with custom quotas per project or user.
• Clustering enables block pruning, which can cut bytes billed on filtered queries.

These controls turn on-demand into a safe default for small teams, because a careless query hits a cap instead of a surprise invoice. The risk is real when many people query a large table with no guardrails.

Free tier and the sandbox

BigQuery's sandbox needs no credit card or billing account. Per Google's sandbox documentation, it gives a lifetime limit of 10 GiB of storage and the same free compute limit as the BigQuery free tier, 1 TiB of processed query data each month. Tables, views, and partitions expire after 60 days, and streaming, DML statements, and the Data Transfer Service are not supported. It is a good way to learn, and it is not a place to run anything that must persist.

Time travel in BigQuery

BigQuery's time travel window defaults to seven days and is configurable from two to seven days at the dataset or project level. After it ends, deleted data stays in a seven-day fail-safe that you cannot configure or query directly; recovery requires Cloud Customer Care. For longer retention, the documentation points to table snapshots.

Redshift: from clusters to Serverless

Redshift's architecture combines massively parallel processing, columnar storage, and compression encodings. You can run it two ways.

Provisioned clusters use RA3 nodes, which separate managed storage from compute. AWS's pricing page gives an example on-demand rate of $3.26 per hour for an ra3.4xlarge node in US East (N. Virginia) and a managed storage price of $0.024 per GB-month.

Redshift Serverless bills by RPU-hours (Redshift Processing Units). The pricing examples use $0.375 per RPU-hour in us-east-1. AWS offers a $300 credit, expiring after 90 days, for customers who have never used Serverless.

Worked example for provisioned pricing, assuming four ra3.4xlarge nodes running all month (about 730 hours) at the on-demand rate:

4 nodes x $3.26 per hour x 730 hours = about $9,519 per month for compute, before storage and before any reserved-instance discount.

For Serverless, the arithmetic is simpler. The bill is RPU-hours consumed times the rate, so a workload that uses 100 RPU-hours in a month costs about $37.50 at $0.375. The attraction is that idle time costs nothing. The risk is a runaway query consuming more RPU-hours than you planned, so cap the maximum capacity in the workgroup settings.

Spectrum and the S3 data lake

Redshift Spectrum queries data directly in S3 without loading it, priced at $5.00 per TB scanned in US East (N. Virginia), rounded up to the next megabyte with a 10 MB minimum per query. For AWS-centered teams whose raw data already sits in S3 as Parquet, this makes Redshift a natural query layer over the lake.

A migration note

AWS stopped allowing new Python user-defined functions on November 1, 2025, and ended support for Python UDFs after June 30, 2026, enforcing the change in phases (AWS documentation). AWS points users to Lambda UDFs instead. If you are evaluating Redshift for a codebase that leans on Python UDFs, plan the migration before committing.

Concurrency: what happens when everyone queries at once

Concurrency is where the architectures diverge most in daily life.

Snowflake solves it by giving each workload its own warehouse and, on Enterprise, letting a warehouse grow into a multi-cluster setup. The documentation says multi-cluster warehouses exist to "scale compute resources to manage your user and query concurrency needs." Allowed maximum cluster counts depend on warehouse size; for example, XSMALL through MEDIUM allow up to 300 clusters, while 6XLARGE allows up to 10.

BigQuery allocates slots. Under on-demand pricing you share a pool; under Editions you can reserve and autoscale slots, and assign them to projects so that production dashboards are not starved by experiments.

Redshift uses workload management queues to prioritize queries and offers Concurrency Scaling to add transient capacity under load. Check the current Redshift documentation for eligibility and pricing of that feature, because both have changed over time.

The practical test is simple. List your concurrent workloads, such as nightly ELT, analyst exploration, a customer-facing dashboard, and a data science team. Then ask how each warehouse isolates them, and what it costs to keep them isolated.

Recovery features: time travel, fail-safe, and clones

All three let you recover from mistakes, but with different windows.

Feature	Snowflake	BigQuery	Redshift
Query past data	Time Travel: up to 1 day (Standard), up to 90 days (Enterprise and higher)	Time travel: 2 to 7 days, default 7	Check current docs; snapshot-based recovery is the long-standing mechanism
Fail-safe after the window	7 days, recoverable only by Snowflake	7 days, via Cloud Customer Care	Not applicable in the same form
Clone or snapshot	Zero-copy clones of tables, schemas, databases, at a past point in time	Table snapshots for longer retention	Snapshots
Undo a dropped table	UNDROP	Within the time travel window	From a snapshot

Sources: Snowflake Time Travel, BigQuery time travel. Redshift's recovery model is snapshot-oriented; confirm details against current AWS documentation, since the feature set changes.

Snowflake's Time Travel lets you query, clone, and undrop objects as they existed at a past point, and setting retention to zero turns it off for that object. Retention is not free: the data kept for time travel and fail-safe counts toward storage. A 90-day window on a table that changes heavily can add real storage cost.

Ecosystem and integrations

Warehouses do not live alone. They sit between loading tools and consumers.

Sources -> Extract/Load tools -> Warehouse -> Transform tools -> BI tools
         (Fivetran, Airbyte)   (this layer)  (dbt, SQL)       (Looker, Tableau)

Transformation. All three work with dbt, the SQL-based transformation tool covered in our dbt guide. BigQuery also has Dataform, Google's own SQL workflow tool.

Machine learning. BigQuery ML lets you create and run models with SQL. Note that Google's editions documentation lists BigQuery ML among the features not available in the Standard edition. Snowflake runs Python, Java, and Scala inside the warehouse through Snowpark. For AWS, Redshift connects with SageMaker. If your ML plans point at managed foundation models, our AWS Bedrock vs Google Vertex AI comparison covers the surrounding platforms.

BI. Looker is Google's, so it pairs naturally with BigQuery, and QuickSight is AWS's, so it pairs with Redshift. Tableau, Power BI, and other tools connect to all three. See data visualization tools for how those tools differ.

Data lake formats. Open table formats such as Apache Iceberg keep data in files you own, readable by several engines. Snowflake supports Iceberg tables whose data lives in external cloud storage you manage, and Google's documentation mentions Iceberg external tables for BigQuery. Redshift's Iceberg support has been expanding; verify current status against AWS documentation. The direction of travel reduces lock-in, though it does not remove it, because performance features usually work best with the native format.

Multi-cloud. Snowflake spans AWS, Azure, and Google Cloud, and Snowgrid connects data across regions and providers. BigQuery and Redshift are tied to their home clouds for the warehouse itself. BigQuery Omni, listed among Enterprise-edition features, is Google's route to analyzing data that lives in other clouds.

Benchmarks: why this article does not publish a speed table

Plenty of comparison pages print a table with cold-query seconds and concurrency counts. Treat those with suspicion. Query time depends on schema design, sort and cluster keys, data volume, warehouse size, caching, and how the benchmark queries were written. A result from a vendor-run test reflects that vendor's tuning, and published numbers go stale quickly as engines change.

A better approach is to run your own test, which does not take long:

1. Pick 10 to 20 real queries, including your slowest and your most frequent.
2. Load a representative slice of data into each candidate, using each platform's recommended layout (clustering, sort keys, or partitioning).
3. Run each query cold and warm, several times, and record both time and cost.
4. Test concurrency by running your dashboard queries in parallel.
5. Price the result at your realistic monthly volume, including idle time.

Cost per query at your concurrency is the number that matters, and no published table can tell you that.

What it costs: how to think about total spend

Because published dollar ranges vary by region, edition, discount, and contract, this section gives you the mechanics rather than a made-up total.

Cost driver	Snowflake	BigQuery	Redshift
Compute unit	Credits per warehouse-hour, billed per second, 60-second minimum	Bytes processed (on-demand) or slot-hours (Editions)	Node-hours (provisioned) or RPU-hours (Serverless)
What makes it grow	Warehouses left running, oversized warehouses	Wide scans, no caps, unclustered tables	Always-on clusters, runaway Serverless queries
Main control	Auto-suspend, right-sizing, resource monitors	Maximum bytes billed, quotas, clustering, reservations	Reserved capacity, Serverless max capacity
Storage	Billed monthly, compressed	Logical or physical billing models	Managed storage per GB-month on RA3
Discount path	Pre-paid capacity	1-year (20%) or 3-year (40%) commitments on Enterprise tiers	Reserved pricing on provisioned nodes

The common failure is not choosing the "expensive" platform. It is choosing a platform whose cost control you did not set up. Snowflake punishes idle warehouses, BigQuery on-demand punishes unbounded scans, and Redshift provisioned punishes oversized clusters.

Where each one fits

Choose Snowflake when

• You use more than one cloud, or want freedom to change clouds without changing your warehouse.
• Several teams need isolated compute against the same data.
• You share live data with customers, partners, or vendors.
• You want to run Python or Java next to the data through Snowpark.

Choose BigQuery when

• You are on Google Cloud, especially with Google Analytics, Ads, or YouTube data feeding the warehouse.
• You want no clusters to size, and your load is irregular enough that paying per bytes processed beats paying for idle capacity.
• You want to train simple models with SQL.

Choose Redshift when

• Your stack and security model are built on AWS, with data in S3 and identity in IAM.
• You want to query S3 data in place through Spectrum.
• You can predict your load enough to benefit from provisioned or reserved capacity, or want Serverless for variable work.

Where each loses

Honest weaknesses matter as much as strengths.

• Snowflake has more knobs to get wrong, and credit-based billing rewards discipline. It also introduces a vendor between you and your cloud provider.
• BigQuery ties the warehouse to Google Cloud, and on-demand costs depend on query habits you may not control. Some capabilities depend on edition.
• Redshift keeps more infrastructure thinking in your hands, and it is tied to AWS. Check feature changes such as the end of Python UDF support in 2026 before relying on a capability.

Edge cases and exceptions

Small data. If your data fits comfortably on one machine, you may not need a cloud warehouse at all. An embedded engine such as DuckDB handles surprisingly large analytical work on a laptop; see the DuckDB vs pandas comparison and the DuckDB explainer.

Streaming and sub-second dashboards. Cloud warehouses are built for broad analytical queries, not for many concurrent user-facing queries with second-level freshness. That workload often goes to a specialized store; our real-time analytics stack guide covers ClickHouse, Druid, and Pinot.

Heavy Spark workloads. If your center of gravity is large-scale data engineering in Spark, a lakehouse approach may fit better than a warehouse. The Apache Spark explainer covers that engine.

Multi-warehouse reality. Some organizations run two. A common pattern is BigQuery for Google-sourced marketing data and another platform for everything else. It works, but you pay in duplicated governance, access control, and data movement.

Migrating. Moving between warehouses means translating SQL dialects, reloading data, rebuilding permissions, and revalidating numbers. Budget for the validation step, which is where migrations run long. If your transformations live in dbt, much of the logic is portable, though warehouse-specific functions need rewriting. Data contracts, described in our data contracts guide, make validation easier because the expected shape of each table is written down.

Common mistakes

Underestimating the bill. Set budgets and alerts in week one, not month three.

Choosing on benchmarks alone. Your queries are not a benchmark. Test your own.

Ignoring data movement. Moving data between clouds or regions can add cost and latency. Keep compute near data.

Leaving compute running. On Snowflake, that is a warehouse without auto-suspend. On Redshift provisioned, that is a cluster sized for peak load.

Treating the warehouse as a transactional database. It is for analytics. Use an operational database for application writes.

Skipping the people question. The best warehouse on paper is the wrong one if nobody on the team has used it. Existing SQL skills matter more than most feature lists. If you are staffing the work, the data engineering vs data science comparison explains who typically owns which part, and the SQL essentials guide covers the shared language.

Questions people ask

Q: Which is the cheapest? A: It depends on your workload and how well you control it, so no honest answer names a winner. BigQuery on-demand is often economical for irregular, small-to-moderate use, while steady heavy workloads may favor capacity pricing, reserved Redshift nodes, or a well-managed Snowflake setup. Estimate using your own query volume.

Q: Which is the fastest? A: There is no reliable, neutral, current ranking. Performance depends on schema design and tuning more than on platform choice. Run a test with your own queries before deciding.

Q: Which has the best free option for learning? A: BigQuery's sandbox needs no credit card and offers 1 TiB of query processing each month and a 10 GiB lifetime storage limit, with tables expiring after 60 days. Redshift Serverless offers a $300 credit that expires after 90 days for new customers. Snowflake offers trials; check its current terms.

Q: Can I use more than one warehouse? A: Yes, and some companies do, for example BigQuery for Google data and another platform for the rest. Expect extra work for access control, governance, and data movement.

Q: Do open table formats like Iceberg remove lock-in? A: They reduce it, because data stays in files readable by several engines. Snowflake supports Iceberg tables stored in external cloud storage you manage, and BigQuery documents Iceberg external tables. Native tables generally offer more performance features, so full portability has a cost.

Q: Is on-premises still worth considering? A: For most analytics workloads, cloud warehouses are the default. Regulatory or data-residency limits are the usual exceptions. Snowflake itself cannot run on-premises, so if that is a requirement, it is a disqualifier for that product.

Q: Where do AI and vector workloads fit? A: Warehouses increasingly offer in-database ML and AI functions, but similarity search over embeddings is a distinct job. See our vector database explainer for how those systems differ from a warehouse.

The takeaway

Snowflake, BigQuery, and Redshift are closer than their marketing suggests. All three scan columns in parallel, separate storage from compute in their current forms, and handle terabytes without drama. The real differences are structural. Snowflake gives you independent compute and cross-cloud reach, BigQuery gives you the least infrastructure to manage, and Redshift gives you the tightest fit with AWS.

What separates a good outcome from a bad one is rarely the product. It is the cost controls and isolation you configure on day one: auto-suspend and right-sizing on Snowflake, maximum bytes billed and clustering on BigQuery, capacity limits on Redshift. Choose the platform that matches the cloud you already live in, test it on your own queries, and set the guardrails before anyone gets a login.

Sources: Snowflake key concepts, Snowflake Time Travel, Snowflake compute cost, BigQuery editions, BigQuery cost best practices, Amazon Redshift pricing, Amazon Redshift architecture.

Last verified: October 22, 2026. Prices and features change often, so confirm against each provider's pricing page before committing.

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