How does Snowflake compute pricing compare to BigQuery and Redshift?

Direct Answer

There is no universal winner — the answer depends on workload shape, team SQL discipline, and which cloud you already live in. At small scale with bursty ad-hoc analyst queries, BigQuery on-demand wins because you pay $0 when no one queries (you only pay storage). At predictable, always-on warehouse workloads with multi-year visibility, Snowflake credits + a capacity contract win — the per-second billing on suspended warehouses plus 30-50% multi-year discounts beat list-price comparisons.

At AWS-native shops with tight Lake Formation / S3 / IAM integration, Redshift Serverless wins on data-gravity — egress alone often kills the Snowflake/BigQuery alternative. The gotchas: BigQuery on-demand can explode if a junior analyst writes SELECT * against a partitioned 50TB table; Snowflake credit consumption is opaque until you instrument warehouse-level monitoring; Redshift's RA3 vs.

Serverless vs. Legacy DC2 SKU sprawl confuses procurement. *List price approximations below — actual contract pricing varies by region, edition, commit tier, and negotiation.*

The Three Pricing Models Explained

Snowflake (credit-based)

• Compute = credits/hour × warehouse size (XS=1, S=2, M=4, L=8, XL=16, ... 4XL=128, 6XL=512), billed per-second after a 60-second minimum
• Credit price varies by edition: Standard (~$2/credit on AWS US East), Enterprise (~$3), Business Critical (~$4), VPS (negotiated)
• Multiplier by cloud region — Azure and GCP regions sometimes price 5-15% higher
• Storage billed separately (~$23/TB/month on-demand, ~$40/TB/month for capacity-purchased Time Travel + Fail-safe)
• Capacity contracts unlock 10-50% off list when you pre-purchase credits (1yr, 2yr, 3yr tiers)

BigQuery (two pricing modes)

• On-demand: ~$6.25/TB scanned (US multi-region, list) — no compute SKU at all, you pay per byte read
• Editions (capacity slots): Standard (~$0.04/slot-hr), Enterprise (~$0.06), Enterprise Plus (~$0.10) — autoscale + 1yr / 3yr commits give 20-40% off
• Storage: ~$0.02/GB/month active, ~$0.01/GB/month for partitions untouched 90+ days (auto-tier)
• Streaming inserts ~$0.01/200MB; Storage Write API is cheaper and now the default for high-throughput ingest

Redshift (two SKUs)

• RA3 nodes (provisioned): ra3.xlplus ~$1.086/hr, ra3.4xlarge ~$3.26/hr, ra3.16xlarge ~$13.04/hr (US East, list) — separates compute from managed storage (~$24/TB/mo)
• Redshift Serverless: billed per RPU-second (~$0.375/RPU-hour list in US East), 60-second minimum, 8 RPU floor — autoscales for variable workloads
• Reserved Instances on RA3 give 1yr (~30% off) or 3yr (~60% off) — Serverless does not have RIs, but commits are now negotiable per AWS reInvent 2025
• Concurrency Scaling credits — first hour/day free, then ~$1/hr per cluster

Apples-to-Apples Comparison Math

*All figures are list-price approximations for US-East regions, May 2026. Actual contract pricing varies.*

Workload 1: 5 analysts, ~50 ad-hoc queries/day, ~500GB scanned/day, 2TB stored

• Snowflake: XS warehouse ~2 hrs/day × $2 + storage ~$50/mo ≈ $170/mo
• BigQuery on-demand: ~15TB/mo scanned × $6.25 + storage $40 ≈ $135/mo
• Redshift Serverless: 8 RPU × ~3 hrs/day × $0.375 ≈ $280/mo
• *Winner: BigQuery on-demand*

Workload 2: Mid-market BI dashboard, 24/7 light read, 10TB stored

• Snowflake: S warehouse on auto-suspend, ~6 credit-hrs/day × $2 ≈ $580/mo
• BigQuery Enterprise: 100 slot baseline ~$2,900/mo (or on-demand: ~$1,500 if scans stay disciplined)
• Redshift: 1× ra3.xlplus reserved 1yr ≈ $540/mo
• *Winner: Redshift RA3 (or Snowflake if multi-cloud)*

Workload 3: Streaming ETL, 500GB/day ingest, hourly transforms, 30TB stored

• Snowflake: M warehouse 8 hrs/day × $2 × 4 credits/hr ≈ $2,200/mo + storage $700
• BigQuery: Storage Write API + 200 slots ≈ $5,800/mo + storage $600
• Redshift Serverless: 32 RPU × 8 hrs × $0.375 × 30 ≈ $2,880/mo
• *Winner: Snowflake (per-second billing on suspended warehouse helps)*

Workload 4: Enterprise ML feature engineering, 50TB scanned/wk, 200TB stored

• Snowflake: L warehouse w/ Snowpark ~$18,000/mo + storage ~$4,600
• BigQuery: Enterprise Plus 500 slots reserved ~$36,000/mo + storage $4,000
• Redshift: ra3.4xlarge × 4 nodes reserved 3yr ~$5,600/mo + RMS $4,800
• *Winner: Redshift RA3 reserved (if AWS-native)*

Workload 5: AI inference + LLM-generated SQL, 1M queries/mo via Cortex/Gemini/Bedrock

• Snowflake Cortex: charged in credits, varies wildly by model — Llama 3.1 70b ~$1.21/1M tokens out; expect $8K-25K/mo at this volume
• BigQuery ML + Gemini integration: ~$0.0025/1K tokens for Gemini 1.5 Flash, generally $3K-12K/mo
• Redshift ML (SageMaker passthrough): ~$5K-15K/mo + SageMaker endpoint costs separately
• *Winner: BigQuery (Gemini pricing is currently most aggressive)*

Hidden Costs That Bite

Snowflake gotchas

• Cloud Services compute (metadata ops) — free up to 10% of warehouse spend, then billed
• Materialized View auto-refresh consumes credits silently
• Marketplace listings can be billed in credits — Snowflake Cortex AI functions especially
• Time Travel + Fail-safe storage stacks on top of base storage (90 days × big tables = real money)
• Cross-region/cross-cloud replication: you pay egress *and* destination storage *and* replication credits
• Search Optimization Service: opt-in but easy to forget — adds 5-15% to credit bill on enabled tables

BigQuery gotchas

• Slot reservation idle time still bills — reservations are not auto-suspending like Snowflake warehouses
• Streaming inserts cost extra over batch loads; many teams forget to migrate to Storage Write API
• Materialized Views re-compute costs hit your slot capacity (or on-demand bytes)
• Data egress to non-Google clouds: $0.08-0.12/GB — kills multi-cloud architectures
• Long-term storage tier kicks in at 90 days untouched but a single SELECT bumps it back to active pricing
• BI Engine reservations are separate from query slots — easy to double-pay

Redshift gotchas

• RA3 Managed Storage (RMS) is cheap (~$24/TB) but cross-AZ traffic for replicas isn't free
• Concurrency Scaling beyond the free hour can quietly 2-3x bills during reporting peaks
• Serverless 8 RPU floor means even tiny workloads cost ~$60/mo minimum if always-on
• Spectrum queries against S3 charge $5/TB scanned — separate from Redshift compute
• DataShare consumer-side compute is billed to consumer; producers often surprised when partners complain
• Backups beyond the free retention period bill at S3 standard rates

Negotiation Levers In 2026

Snowflake (post-Sridhar Ramaswamy era, more aggressive on price)

• Multi-year capacity commits: 25-40% off list at $1M+/yr; 40-55% at $5M+
• Multi-cloud commit (AWS + Azure or AWS + GCP): adds 5-10% additional discount
• Migration credits if coming from BigQuery/Redshift — Snowflake will fund a POC
• Cortex AI commit carve-out: negotiate AI credits as separate line item with usage-based true-up
• Procurement signal: mention Databricks evaluation in writing — discounts move 10-15%

BigQuery / Google Cloud

• Committed Use Discounts on Editions: 20% (1yr) / 40% (3yr) standard
• Enterprise Agreement gets you another 5-10% if bundled with GCP infra spend
• Gemini token commit: Google is buying market share — ask for AI credits parity vs. OpenAI
• Multi-region storage discount: data sovereignty asks (EU, India) move pricing
• Procurement signal: mention Snowflake-on-GCP — Google sales will discount to keep workload native

Redshift / AWS

• 3yr Reserved Instance on RA3: ~60% off list, no upfront option available
• Enterprise Discount Program (EDP) — bundled S3, EC2, Redshift commit gets 15-25% across the board
• Serverless commit announced at reInvent 2025: now negotiable per-RPU pricing for $500K+/yr commits
• Free DMS migration + Professional Services credits if migrating from competitor warehouse
• Procurement signal: mention Iceberg + open-table strategy — AWS will negotiate to keep you in Redshift vs. Open-source Trino/Athena

The AI Workload Question

• Snowflake Cortex prices LLM inference in credits — convenient billing but opaque; Llama 3.1 70b runs ~$1.21/1M output tokens, fine-tunes priced in credit-hours. Embedding functions cheap, generative LLM calls expensive at scale.
• BigQuery + Gemini integration is currently the cheapest path for high-volume LLM enrichment — Gemini 1.5 Flash at ~$0.30/1M output tokens beats Cortex by ~3-4x for equivalent quality on summarization/classification.
• Redshift ML outsources to SageMaker — you pay SageMaker endpoint costs (instance-hour pricing) plus Redshift compute for the SQL wrapper. Worst $/inference of the three for ad-hoc generative work but best for batch scoring at predictable volume.
• The overcharge today: Snowflake Cortex is the most expensive per-token for top-tier models (GPT-4-class), justified by zero data movement. If your AI workload is 60%+ of platform spend, BigQuery + Gemini or Redshift + Bedrock will beat Snowflake on raw economics — but factor in egress and security review costs.
• Vector search: Snowflake (native Cortex Search), BigQuery (Vector Search GA 2025), Redshift (pgvector via Aurora integration) — Snowflake currently has the simplest TCO story for embedding-heavy RAG workloads kept inside the warehouse.

Pricing Comparison Table

*All figures list-price approximations May 2026; actual contract pricing varies 30-60% with commits.*

Decision Tree

Bottom Line

Stop comparing list prices — they lie. The real pricing question is (a) how predictable is your workload, (b) which cloud holds your data gravity, and (c) how disciplined is your SQL? BigQuery on-demand wins for small disciplined teams; Snowflake wins for predictable multi-cloud enterprises willing to commit; Redshift wins for AWS-native shops with reserved-instance budgets.

AI workload mix is the new wildcard — if 50%+ of your spend is going to LLM inference by 2027, BigQuery + Gemini currently has the most aggressive token economics, but lock-in considerations matter. Always model 3-year TCO including egress, storage tiers, and one major workload-pattern change.

*(see also: q1567, q1568, q1577)*