Redshift vs Snowflake vs BigQuery 2026: Price & Speed

August 21, 2026

21 min read

Three teams migrated off Snowflake in the past year and landed in three different places. GetYourGuide moved its Looker workloads to Databricks and cut costs 20%. SmarterX shifted from Snowflake to BigQuery and cut its data warehouse bill roughly in half. Travelpass Group went the other direction, moving BI and SQL analytics off Databricks and onto Snowflake, and reported a 65% drop in compute costs. Redshift, meanwhile, just quietly became the cheapest entry point of the three, with a 4-RPU serverless tier that starts at $1.50 an hour. None of these companies were wrong. They were solving different problems with different data shapes, and the pricing model each platform uses rewards a completely different kind of workload.

That is the real story behind Amazon Redshift vs Snowflake vs BigQuery in August 2026, and it’s still the case Estuary’s own June 2026 comparison landed on when it ranked these same three platforms as the top cloud data warehouses on the market today. This is not a “pick the best one” comparison. It is a “pick the one that matches how your queries actually run” comparison, and the gap between the right and wrong choice can run into six figures a year at scale. Below is the current pricing, the newest features each vendor shipped in the last few months, independent benchmark data, migration guidance, and a verdict backed by numbers rather than vendor marketing.

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Redshift vs Snowflake vs BigQuery: The Quick Answer

If you need the short version before the deep dive: Amazon Redshift is the cheapest way to get a serverless data warehouse running today, with a $1.50-per-hour entry tier and tight native integration with the rest of AWS. Snowflake is the most workload-flexible platform, with per-second billing, four editions, and the deepest AI tooling story through Cortex. Google BigQuery is the most operationally hands-off option, a fully serverless architecture with no clusters to size, backed by ongoing on-demand pricing of $6.25 per TiB scanned in 2026. Each platform wins different benchmark categories depending on query type, and none of them wins every category. That’s the part vendor pricing pages leave out.

What Changed in 2026: The Latest Releases

All three vendors shipped meaningful updates in the last few months, and the changes shift the calculus for anyone comparing them today rather than reading a stale 2024 review.

Amazon Redshift introduced a 4-RPU minimum serverless configuration across seven regions in May 2026, dropping the effective entry price to $1.50 an hour of active compute, down from a much higher previous baseline. AWS followed that in June 2026 with multi-warehouse enhancements that let zero-ETL ingestion pipelines use concurrency scaling automatically, a direct answer to Snowflake’s multi-cluster warehouses. Redshift Serverless also picked up three-year reservation pricing in February 2026, and AWS has been layering Amazon Q into the query editor for natural-language SQL assistance.

Snowflake’s biggest 2026 change was billing architecture, not a feature. On April 1, 2026, the company split AI workloads into a new “AI Credit” system priced flat at $2.00 per credit globally or $2.20 for region-pinned requests, fully decoupled from which edition (Standard, Enterprise, Business Critical) a customer runs. Cortex Agents, Snowflake Intelligence, and Cortex Analyst all moved onto this AI Credit model, while core compute, storage, and data transfer stayed on the traditional per-credit pricing. It’s a meaningful shift: teams that were paying inflated Business Critical rates just to access AI features can now get AI pricing independent of their edition tier.

Google Cloud made the most aggressive on-demand pricing move of the three. In a change effective retroactively for billing cycles starting on or after March 1, 2026, BigQuery cut its on-demand query price 25%, from $6.25 to $4.69 per terabyte scanned. That’s the first double-digit percentage cut to BigQuery’s flagship pricing model in years, and it directly targets teams running ad hoc, unpredictable query loads who don’t want to commit to slot reservations. Google is pairing that price cut with a cleanup of legacy behavior: BigQuery’s own release notes confirm that usage limits on legacy SQL take effect June 1, 2026, meaning any project still running legacy SQL queries between November 2025 and that cutoff needs to migrate to Standard SQL or risk hitting throttling.

Full Specs and Pricing Comparison Table

Category	Amazon Redshift	Snowflake	Google BigQuery
Architecture	Provisioned clusters (RA3) or Serverless (RPU-based)	Multi-cluster virtual warehouses, separate compute/storage	Fully serverless, no cluster management
Entry-level pricing	Serverless from $1.50/hr (4 RPU minimum)	Standard edition from ~$2.00/credit	On-demand from $4.69/TiB scanned
Mid-tier compute rate	$0.375/RPU-hour on-demand (US East)	Enterprise edition ~$3.00/credit	Enterprise Edition $0.06/slot-hour
Top-tier compute rate	ra3.16xlarge at $13.04/hr per node	Business Critical ~$4.00/credit	Enterprise Plus $0.10/slot-hour
Storage cost	~$24-25/TB/month (Managed Storage)	~$23/TB/month (compressed)	From $0.02/GiB (~$20.48/TB) physical storage
Billing granularity	Per-second, 60-second minimum	Per-second, 60-second minimum	Per-second (Standard/Ent/Ent Plus), 1-min minimum
Reserved discount	Up to ~23% off with 1-yr serverless reservation	Multi-year contract discounts (custom)	1-yr: up to 20% off; 3-yr: up to 40% off slot rate
AI/ML native tooling	Amazon Q integration, SageMaker link	Cortex AI, Cortex Agents, Snowflake Intelligence	Gemini in BigQuery, BigQuery ML, Vertex AI link
Zero-ETL ingestion	Yes, from Aurora and operational sources	Snowpipe Streaming, Iceberg tables	Yes, via Datastream and BigQuery continuous queries
Elasticity model	Auto-scaling RPUs, concurrency scaling	Multi-cluster auto-scale (Enterprise+)	Autoscaling per edition, Fluid scaling opt-in
Primary cloud	AWS only	AWS, Azure, GCP (multi-cloud)	Google Cloud only
Latest 2026 release	4-RPU serverless minimum (May 2026)	AI Credit billing split (April 2026)	25% on-demand price cut (March 2026)

A note on that table: cloud vendor pricing pages rarely show a true apples-to-apples rate because Redshift bills by RPU-hour, Snowflake bills by credit (a proxy for compute-seconds across warehouse sizes), and BigQuery offers both a pay-per-TB-scanned model and a slot-hour capacity model. The numbers above reflect on-demand, US-region, list pricing as published by each vendor and cross-checked against independent 2026 pricing guides. Actual bills depend heavily on workload shape, which is exactly why the benchmark section below matters more than the sticker price.

Total Cost of Ownership: A Sample Workload

Sticker prices don’t mean much until they’re applied to an actual workload. Here’s a rough monthly cost estimate for a mid-size analytics team running 10 TB of stored data and roughly 200 hours of active compute per month, using published on-demand list pricing for each platform at a comparable mid-tier configuration. This is a directional estimate, not a quote — actual bills shift with concurrency, query complexity, and compression ratios, but it illustrates how differently each platform’s billing model behaves at the same rough scale.

Cost Component	Amazon Redshift	Snowflake	Google BigQuery
Compute model used	Serverless, ~32 RPU average	Enterprise edition, Medium warehouse	Enterprise Edition, on-demand slots
Est. monthly compute	~$2,400 (32 RPU x 200 hrs x $0.375)	~$2,880 (960 credits x $3.00)	~$2,880 (48,000 slot-hrs at $0.06)
Monthly storage (10 TB)	~$245	~$230	~$205
Estimated monthly total	~$2,645	~$3,110	~$3,085
Where this workload wins	Predictable, always-on AWS-native pipelines	Bursty concurrency spikes with auto-scale	Ad hoc, exploratory analytics with no fixed schedule

The gap narrows or widens fast depending on utilization. A team that leaves warehouses running 24/7 instead of auto-suspending idle compute can blow past these estimates on Snowflake and Redshift alike, while a team scanning full tables instead of using partitioning and clustering on BigQuery can turn a cheap on-demand bill into an expensive one overnight. The platform with the lowest list price is rarely the platform with the lowest actual bill — query discipline and warehouse-sizing hygiene matter more than the underlying rate card.

Amazon Redshift Pricing Breakdown

Redshift Serverless bills in Redshift Processing Units, or RPUs, at $0.375 per RPU-hour in US East (N. Virginia), with per-second metering and a 60-second minimum per query. AWS’s May 2026 rollout of the 4-RPU minimum tier across seven regions is the headline change: a small analytics team can now spin up Redshift Serverless for as low as $1.50 an hour while active, and pay nothing when idle. That undercuts both Snowflake’s smallest Standard-edition warehouse and BigQuery’s on-demand rate for light, bursty workloads.

Provisioned Redshift still exists for teams that want predictable, always-on capacity. RA3 node pricing in US East runs $1.086/hour for ra3.xlplus (4 vCPUs, 32 GiB RAM, up to 32 TB managed storage), $3.26/hour for ra3.4xlarge (12 vCPUs, 96 GiB RAM), and $13.04/hour for ra3.16xlarge (48 vCPUs, 384 GiB RAM). Managed storage on top of that runs roughly $0.024 per GB-month, or about $24-25 per TB-month. Three-year Serverless reservations, introduced in February 2026, knock the RPU-hour rate down further for teams willing to commit.

Snowflake Pricing Breakdown

Snowflake’s compute pricing runs on a credit system layered across four editions. Standard edition lists at roughly $2.00 per credit on-demand in US AWS regions, Enterprise at roughly $3.00 per credit (adding multi-cluster auto-scale, materialized views, and extended Time Travel), and Business Critical at roughly $4.00 per credit for regulated workloads needing enhanced data protection. A fourth tier, Virtual Private Snowflake, is priced by custom contract for the most security-sensitive customers. Storage pricing is where the sticker-price comparisons get sloppy: a CloudZero pricing audit verified in April 2026 puts Snowflake’s on-demand storage rate at $40 per TB per month in AWS us-east-1, while customers who pre-purchase capacity still lock in roughly $23 per TB per month for compressed data, independent of edition — a nearly 2x gap that most “Snowflake storage costs $23/TB” claims gloss over.

The bigger 2026 story is Snowflake’s AI Credit split, introduced April 1, 2026. Cortex-related AI services, including Cortex Agents, Cortex Analyst, and Snowflake Intelligence, now bill at a flat $2.00 per AI Credit for global routing or $2.20 for region-pinned requests, regardless of whether the account runs Standard or Business Critical edition. Before this change, AI feature pricing was tangled up with edition tier, which meant regulated customers on Business Critical paid a premium just to use Cortex. Decoupling that is a real cost reduction for compliance-heavy teams that want AI features without upgrading their entire warehouse edition.

Google BigQuery Pricing Breakdown

BigQuery offers two pricing paths: on-demand, billed per terabyte of data scanned, and capacity-based Editions, billed per slot-hour. On-demand pricing remains $6.25 per TiB scanned, with no confirmed price cut for 2026, a 25% cut that Google positioned as a direct response to teams shifting toward Snowflake and Redshift for cost-sensitive analytics.

The Editions model, meanwhile, gives three slot-hour tiers: Standard Edition at $0.04/slot-hour pay-as-you-go (dropping to about $0.032/slot-hour on a 3-year commitment), Enterprise Edition at $0.06/slot-hour ($0.038 on 3-year commit), and Enterprise Plus at $0.10/slot-hour ($0.06 on 3-year commit) for regulated and mission-critical workloads. Physical storage starts at $0.02 per GiB, translating to roughly $20.48 per TB — the cheapest storage rate of the three platforms, though logical (uncompressed) storage billing can push that higher depending on data density. That storage edge shows up in independent comparisons too: a widely shared r/dataengineering breakdown pegged Snowflake storage at $23/TB against BigQuery’s roughly $20/TB, a gap practitioners were still citing as recently as January 2026 despite the original discussion dating back to December 2024.

Benchmark Data: Query Performance Across Platforms

Independent benchmarks on Redshift, Snowflake, and BigQuery consistently reach the same conclusion: there is no universal winner, and the “fastest” platform flips depending on query type, dataset size, and whether you’re comparing on-demand or committed pricing. Firebolt’s own 2025 head-to-head, run across standard TPC-style benchmark suites, found Snowflake holding a marginal lead over both Redshift and BigQuery on most queries tested — but the margin was thin enough that Firebolt itself framed it as a case for workload-specific testing rather than a blanket recommendation.

A widely cited independent test running identical SQL workloads found Snowflake roughly 58% faster and 28% cheaper than Databricks on that particular workload — and dramatically cheaper than BigQuery on-demand for the same queries, according to a 2026 Fivetran comparison guide, which also notes that BigQuery suits teams wanting a fully serverless platform with no infrastructure to size, while ClickHouse-style engines win on raw real-time query speed. A separate independent benchmark run in September 2025 and still referenced in 2026 discussions found Snowflake coming in 95% cheaper than BigQuery on-demand and 60% cheaper than BigQuery’s reserved standard tier for that specific test scenario, though the author flagged the result as highly workload-dependent rather than universal.

A third data point, Tech-Insider’s own query-level benchmark comparing runtime and cost across platforms as of September 2025, put BigQuery at 500 committed slots finishing in 1 minute 18 seconds (78 seconds) with $0 marginal query cost, versus BigQuery on-demand finishing in 1 minute 24 seconds (84 seconds) for $40.00. The same test showed Snowflake Large completing in 3 minutes 30 seconds for $28.00 and Databricks with Photon acceleration finishing in 4 minutes 10 seconds for $8.50, illustrating just how much cost and runtime diverge depending on whether a platform is running committed capacity or on-demand billing for the exact same query set. The consistent theme across all three independent tests: BigQuery’s committed-slot model wins on raw speed-per-dollar when fully utilized, Snowflake wins on flexibility and cold-start simplicity, and cost outcomes swing wildly based on configuration rather than any inherent platform superiority.

Real-World Migration Examples

Vendor case studies are marketing material, but the specific, dated, named examples below give a sense of what actually moves the needle for real engineering teams migrating between these three platforms in 2025 and 2026.

SmarterX (Snowflake to BigQuery, July 2025): Migrated more than 80 databases and thousands of tables across 21 data sources from Snowflake to BigQuery in under a month, cutting data warehouse costs roughly in half according to a Google Cloud case study.
Leading mobile game creator (Snowflake to BigQuery, August 2025): A gaming company with roughly 300 TB of historical data moved from Snowflake to BigQuery to improve ML capabilities, accelerating time to insights by 25% per a Persistent Systems case study.
GetYourGuide (Snowflake to Databricks, January 2025): Migrated its Looker data source from Snowflake to Databricks to centralize data warehousing, reporting a 20% reduction in infrastructure costs.
Travelpass Group (Databricks to Snowflake, 2026): Moved BI and SQL analytics workloads from Databricks back to Snowflake, documenting a 65% reduction in compute costs for that specific workload class — a reminder that migration direction isn’t one-way.
WhatIf Media Group (Snowflake to Databricks): Migrated dimensional tables and streaming workloads from Snowflake to Databricks, reporting a 76% cut in data infrastructure costs, a figure still frequently cited in 2026 cost-comparison discussions.

The pattern across these five migrations is not “platform X is better.” It’s that teams moving toward serverless, pay-per-scan models (BigQuery) tend to save money on unpredictable, bursty analytics, while teams moving toward committed-capacity models (Snowflake, Databricks) tend to save money on steady, high-utilization pipelines. Redshift, notably, shows up less often in these migration case studies specifically because most teams choosing it are already deep in the AWS ecosystem and never needed to migrate away from anything — they started there because of existing S3, Glue, and Aurora integration.

Feature Comparison: AI, Storage Formats, and Ecosystem

Beyond raw pricing, the three platforms are increasingly differentiated by how deeply they’ve built in AI tooling and open table format support.

Snowflake’s Cortex suite is the most mature native AI layer of the three, spanning Cortex Analyst (natural language to SQL), Cortex Agents (multi-step AI workflows over structured and unstructured data), and Snowflake Intelligence, all now billed through the decoupled AI Credit system described above. Snowflake also added preview support for storing Apache Iceberg tables directly on AWS and Azure in spring 2026, a move that lets teams query open-format lakehouse data without a full migration into Snowflake’s proprietary storage layer.

BigQuery leans on Gemini integration for AI-assisted query generation and analysis, plus native BigQuery ML for training models directly against warehouse data without exporting it. At Google Cloud Next in April 2026, Google went further, announcing fluid scaling for BigQuery with true per-second autoscaling billing and a new TabularFM model family inside BigQuery AI for structured-data forecasting. Its serverless architecture means there is no node sizing decision to make at all — Google handles all capacity allocation behind the scenes, which is either a major convenience or a loss of control, depending on how much a team wants to tune performance manually.

Redshift’s AI story runs through Amazon Q, which brings natural-language query assistance into the Redshift query editor and ties into the broader AWS analytics stack (Glue, Athena, SageMaker). Redshift’s June 2026 multi-warehouse enhancements also extended zero-ETL ingestion to support concurrency scaling automatically, closing a gap with Snowflake’s multi-cluster warehouses for teams running high-concurrency dashboards against live operational data.

Security, Compliance, and Data Governance

For regulated industries, the compliance certification list often matters more than the price-per-credit. All three platforms carry SOC 2 Type II attestations and support HIPAA-eligible workloads on their higher-tier editions, but the details of how each vendor implements isolation differ enough to matter for procurement teams.

Snowflake’s Business Critical edition is purpose-built for this tier, adding customer-managed encryption keys, private connectivity through AWS PrivateLink or Azure Private Link, and extended Time Travel windows for audit trails, all wrapped into that roughly $4.00-per-credit pricing. Because Business Critical is a full edition rather than an add-on, teams get the compliance controls bundled with the rest of Snowflake’s feature set, including Cortex AI access under the same decoupled AI Credit billing described above.

Redshift’s compliance posture runs through the standard AWS shared-responsibility model: VPC isolation, KMS-managed encryption at rest, and IAM-based access control apply uniformly across RA3 and Serverless deployments, with no separate “compliance edition” the way Snowflake and BigQuery structure it. That makes Redshift’s compliance story simpler to reason about for teams already operating inside an AWS security boundary, since it inherits the same controls used across the rest of an organization’s AWS footprint rather than requiring a distinct governance model.

BigQuery’s Enterprise Plus edition is Google’s equivalent regulated-industry tier, layering in higher availability SLAs, disaster recovery options, and support for customer-managed encryption keys (CMEK) at the $0.10-per-slot-hour rate. Google Cloud’s compliance documentation also points to BigQuery’s native support for column-level and row-level security policies, which apply across all three edition tiers rather than being gated to the top tier alone — a structural difference from Snowflake, where some governance features are edition-locked.

Ecosystem and Third-Party Tool Support

None of these platforms operates in isolation. The strength of the surrounding ecosystem, ETL/ELT tools, BI connectors, orchestration frameworks, often decides adoption as much as raw pricing does.

Snowflake has the broadest third-party connector footprint of the three, reflecting its multi-cloud position. Fivetran, dbt, Airbyte, and virtually every major ELT and transformation tool ships a first-class Snowflake connector, and the Snowflake Marketplace adds a data-sharing layer that lets customers subscribe to external datasets without building their own ingestion pipeline. Its native support for Apache Iceberg tables (in preview on AWS and Azure since spring 2026) also gives teams an escape hatch into open lakehouse formats without a full data migration.

Redshift benefits from being a first-party AWS service, which means everything else in AWS, Glue, Lake Formation, QuickSight, SageMaker, EventBridge, treats it as a native target with minimal configuration. That tight coupling is a strength for AWS-committed teams and a limitation for anyone who might later want to run workloads outside AWS, since Redshift-specific SQL extensions and RA3 storage formats don’t port cleanly to other clouds.

BigQuery sits at the center of Google Cloud’s analytics and AI stack, connecting natively to Looker, Vertex AI, and Gemini without extra configuration. Third-party connectivity got a boost in August 2026 when Google’s open-source BigQuery JDBC driver reached general availability, giving BI tools and custom applications a standards-based way to connect without relying on Google’s proprietary client libraries. Its public dataset marketplace and tight integration with Google Sheets and Data Studio-style reporting tools also make it a common default for teams that already live inside Google Workspace, even before considering raw pricing or performance.

Common Mistakes Teams Make When Choosing a Data Warehouse

A few patterns show up repeatedly in the migration case studies and cost post-mortems referenced throughout this piece, and they’re worth flagging before committing to a platform.

Sizing for peak load instead of average load. Provisioning a Redshift RA3 cluster or Snowflake warehouse for the busiest hour of the month, then leaving it running 24/7, is the single most common source of runaway cloud data warehouse bills.
Ignoring query patterns before picking a pricing model. Teams running mostly small, frequent queries tend to overpay on BigQuery’s per-TB-scanned on-demand model; teams running occasional, large batch jobs tend to overpay on committed-capacity models like Snowflake or Redshift RA3.
Treating the migration as a lift-and-shift. Every named case study in this piece that reported strong savings also reported rewriting stored procedures and UDFs rather than porting them directly, since none of the three platforms share a fully compatible SQL dialect.
Skipping the parallel-run validation window. Cutting over without running old and new platforms side-by-side for two to four weeks is how teams discover data type mismatches and rounding errors after go-live instead of before it.
Comparing list prices instead of workload-adjusted costs. As the TCO table above shows, the platform with the lowest advertised rate isn’t always the platform with the lowest actual bill once storage, idle time, and query inefficiency are factored in.

Use-Case Recommendations

Here’s how the choice typically shakes out based on workload pattern and existing infrastructure, drawn from the pricing and benchmark data above.

Already deep in AWS (S3, Glue, Aurora, Lambda): Redshift wins on integration friction alone. Zero-ETL from Aurora and native Glue Catalog support mean less pipeline plumbing, and the new 4-RPU serverless tier makes the entry cost trivial to justify.
Bursty, unpredictable analytics with no dedicated data engineering team: BigQuery’s fully serverless model and 25% on-demand price cut make it the lowest-friction choice — no cluster sizing, no warehouse suspension logic to manage, pay only for bytes scanned.
Multi-cloud or cloud-agnostic requirements: Snowflake is the only one of the three that runs natively on AWS, Azure, and GCP, which matters for enterprises with M&A history or regulatory mandates against single-cloud lock-in.
Heavy in-warehouse AI/ML workflows: Snowflake’s Cortex suite, now with decoupled AI Credit pricing, gives the most mature native AI tooling without needing to export data to a separate ML platform.
High-concurrency BI dashboards serving hundreds of simultaneous users: Redshift’s multi-warehouse enhancements and Snowflake’s multi-cluster auto-scale (Enterprise+) both handle this well; BigQuery’s Enterprise Plus edition is the equivalent tier on the Google side, but costs more per slot-hour.
Cost-sensitive startups running lean: Redshift Serverless at $1.50/hour minimum or BigQuery on-demand with no reservation commitment are both viable entry points; Snowflake’s Standard edition can also work but typically requires more warehouse-sizing tuning to avoid overpaying for idle compute.
Regulated industries (finance, healthcare) needing enhanced compliance controls: Snowflake Business Critical and BigQuery Enterprise Plus both target this tier explicitly; Redshift’s RA3 with VPC isolation and encryption covers similar ground within the AWS compliance boundary.

Getting Started: Free Tiers and Trial Credits

Before committing engineering time to a full evaluation, it’s worth knowing what each platform offers for a no-risk test drive. Snowflake offers a 30-day free trial with $400 in usage credits applied automatically on signup, enough to run a meaningful proof-of-concept against a sample dataset without touching a production workload. Google Cloud’s standard free-tier program extends to BigQuery, including a recurring 1 TB of free query processing per month on top of any broader Google Cloud trial credit, which makes it the easiest of the three to poke at without any commitment at all. AWS doesn’t offer a dedicated Redshift trial credit in the same structured way, but the Redshift Serverless free trial provides 750 hours of usage per month on a small configuration for two months, which for most evaluation-scale testing covers the cost entirely.

For teams running a genuine bake-off between all three platforms, a practical approach is loading the same 1-10 GB sample dataset into each one, running an identical set of 10-15 representative queries drawn from actual production workloads, and comparing wall-clock time and estimated cost side by side. None of the published benchmarks in this piece will perfectly match any specific team’s data shape or query patterns, which is exactly why the independent tests cited above show such different results depending on the workload tested.

Migration Guide: Moving Between Platforms

Migrating a production data warehouse is not a weekend project, but the pattern below, drawn from the named case studies above and vendor migration documentation, holds across most Redshift-Snowflake-BigQuery moves.

Audit current workloads and cost hotspots. Inventory every scheduled query, dashboard refresh, and ETL job. Identify which queries are scan-heavy (favors BigQuery pay-per-scan) versus steady-state compute (favors Snowflake/Redshift committed capacity).
Map schema and data types. Redshift and Snowflake both use SQL dialects close to standard ANSI SQL with vendor extensions; BigQuery’s Standard SQL has more divergence, especially around nested/repeated fields and array handling.
Choose an ingestion path. Google’s own migration tooling recommends running the BigQuery migration assessment tool first for Snowflake-to-BigQuery moves; AWS offers native Snowflake connectors for Redshift-bound migrations via Glue.
Run a parallel validation period. Most successful migrations documented above ran two to four weeks of parallel queries against both old and new platforms before cutting over, catching data type mismatches and rounding differences early.
Convert stored procedures and UDFs. This is consistently the most time-consuming step. Snowflake JavaScript/Python UDFs, Redshift PL/pgSQL procedures, and BigQuery’s JavaScript UDFs are not directly portable and require manual rewrites.
Re-point BI tools and dashboards. Looker, Tableau, and Power BI connections need updated connection strings and, in some cases, revalidated semantic layers if column types shifted during migration.
Right-size compute on the new platform. Don’t just replicate the old warehouse size. Redshift RPUs, Snowflake warehouse sizes, and BigQuery slot counts are not equivalent units — benchmark actual query performance before locking in a reserved tier.
Decommission the old platform gradually. Keep read access to the legacy warehouse for 30-90 days post-cutover in case of missed dependencies, a step every case study above that reported clean savings numbers also followed.

Pros and Cons: Amazon Redshift

Pros: Lowest serverless entry price of the three at $1.50/hour; deepest native integration with AWS services (S3, Glue, Aurora, SageMaker); zero-ETL ingestion from operational databases; per-second billing with no long-term lock-in required.

Cons: AWS-only, so multi-cloud or cloud-migration strategies are a non-starter; RA3 provisioned pricing gets expensive fast at the top end ($13.04/hour for ra3.16xlarge); AI tooling (Amazon Q) is less mature than Snowflake’s Cortex suite; managed storage costs run slightly above BigQuery’s raw storage rate.

Pros and Cons: Snowflake

Pros: Only platform of the three that runs natively across AWS, Azure, and GCP; most mature native AI suite via Cortex, now with decoupled AI Credit pricing independent of edition; strong multi-cluster auto-scaling for concurrent BI workloads; broad third-party ecosystem and connector support.

Cons: Compute pricing scales up fast across editions (Standard to Business Critical roughly doubles the per-credit rate); requires more active warehouse-sizing management than BigQuery’s fully serverless model; storage and compute pricing complexity can make cost forecasting harder without dedicated FinOps tooling.

Pros and Cons: Google BigQuery

Pros: Fully serverless with zero cluster or node management; cheapest raw storage rate of the three at $0.02/GiB; 25% on-demand price cut in March 2026 makes bursty workloads notably cheaper; tight Gemini and Vertex AI integration for in-warehouse ML.

Cons: Google Cloud-only, no multi-cloud option; on-demand pricing can spike unpredictably for poorly optimized queries scanning full tables; slot-based Editions pricing requires understanding a genuinely different mental model than credit- or RPU-based billing; fewer regions than AWS for data residency requirements.

The Verdict: Which Platform Wins in 2026

There is no single winner, and any comparison article claiming otherwise is selling something. But the data does point to clear defaults. For teams already committed to AWS, Redshift’s new $1.50/hour serverless floor and zero-ETL integration with Aurora and S3 make it the default choice — there’s rarely a reason to add a second vendor relationship just for the data warehouse layer. For teams that need multi-cloud flexibility or want the most mature native AI tooling, Snowflake’s decoupled AI Credit pricing (launched April 2026) removed one of the biggest objections to its AI features, and its cross-cloud portability remains unmatched. For teams that want to think about analytics as little as possible operationally, BigQuery’s fully serverless model plus its 25% on-demand price cut make it the lowest-friction option, especially for unpredictable or spiky query patterns.

The numbers back this up: independent benchmarks show Snowflake beating Databricks by roughly 58% on speed and 28% on cost for identical SQL workloads, while committed-slot BigQuery configurations can complete the same query in under half the time of an on-demand run for zero marginal cost once slots are paid for. Redshift’s RA3 storage runs about a dollar more per TB than BigQuery’s raw physical storage rate, but that gap disappears against the value of avoiding a second vendor’s data egress fees entirely. Pick based on your cloud commitment first, your query pattern second, and the sticker price last — the sticker price is the part that changes the least once you actually run production workloads.

Frequently Asked Questions

Is Amazon Redshift cheaper than Snowflake?

For small, bursty workloads, yes — Redshift Serverless starts at $1.50/hour with a 4-RPU minimum, undercutting Snowflake’s Standard edition entry point. For steady, high-concurrency workloads, the gap narrows significantly and can flip depending on warehouse sizing and how efficiently each platform’s auto-scaling handles the load.

Can BigQuery run on AWS or Azure?

No. BigQuery is exclusive to Google Cloud. If multi-cloud flexibility is a requirement, Snowflake is currently the only one of the three major platforms that runs natively across AWS, Azure, and Google Cloud.

What is Snowflake’s AI Credit pricing and how does it differ from regular credits?

Introduced April 1, 2026, AI Credits are a separate billing currency for Cortex-related AI services (Cortex Agents, Cortex Analyst, Snowflake Intelligence), priced flat at $2.00 per credit globally or $2.20 for region-pinned requests. This is decoupled from Snowflake edition, meaning Standard and Business Critical customers now pay the same AI rate. Core compute, storage, and data transfer still use the traditional Platform Credit pricing tied to edition.

How much did BigQuery’s on-demand pricing drop in 2026?

Google Cloud cut BigQuery’s on-demand query price by 25%, from $6.25 to $4.69 per terabyte scanned, effective retroactively for billing cycles beginning on or after March 1, 2026.

Which platform is best for teams already using Databricks?

It depends on the workload. Some teams have migrated from Databricks to Snowflake and reported cost reductions (Travelpass Group documented a 65% compute cost cut), while others moved the opposite direction from Snowflake to Databricks (WhatIf Media Group reported a 76% infrastructure cost reduction). The direction that saves money depends heavily on whether the workload is steady-state SQL analytics (favors Snowflake) or ML-heavy pipeline processing (favors Databricks).

Does Redshift support zero-ETL integrations like the other platforms?

Yes. Redshift’s zero-ETL ingestion pulls directly from Amazon Aurora and other operational data sources, and AWS’s June 2026 multi-warehouse enhancements added concurrency scaling support to that pipeline, letting ingestion scale automatically without manual warehouse resizing.

What is the cheapest storage option among the three platforms?

BigQuery’s physical storage, starting at $0.02 per GiB (roughly $20.48/TB), is the cheapest of the three on paper. Snowflake runs about $23/TB/month for compressed storage, and Redshift’s Managed Storage runs about $24-25/TB/month. The gap is small enough that it rarely drives platform choice on its own.

Do independent benchmarks agree on which platform is fastest?

No, and that’s the key finding across multiple 2025-2026 tests. Results vary by query type, dataset size, and whether the comparison uses on-demand or committed-capacity pricing. BigQuery’s committed-slot tier tends to win on raw speed-per-dollar when fully utilized; Snowflake tends to win on flexibility and simpler cold-start behavior; Redshift wins on cost for AWS-native, bursty workloads. No single independent benchmark found in 2025-2026 crowns one platform the universal winner.

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Nadia Dubois

AI & Innovation Editor

Nadia Dubois is the AI & Innovation Editor at Tech Insider, where she tracks the rapid evolution of artificial intelligence, from foundation models to real-world enterprise deployment. She previously covered AI and startups for La Tribune and contributed to MIT Technology Review's European coverage. Nadia specializes in generative AI, AI regulation, and the intersection of technology and European industrial policy. She holds a dual degree in Computational Linguistics and Journalism from Sciences Po Paris.

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Redshift vs Snowflake vs BigQuery: $1.50/hr vs $4.69/TB [2026]