7 min read

Gen1 vs Gen2 Snowflake Warehouses : When Does the 25% Premium Pay Off?

Yaniv Leven

Oct 20, 2025

A data engineer, sitting in a modern high-rise office with a city skyline visible through large windows, is deep in thought. In front of him, a holographic interface displays a comparison between Gen1 and Gen2 Snowflake Warehouses. On the left, Gen1 Snowflake Warehouse is represented by stacked layers, implying its older architecture. On the right, Gen2 Snowflake Warehouse is shown with a cloud icon labeled with key features like

Snowflake Gen2 warehouses deliver 30-60% faster performance through improved CPU architecture, wider vector throughput, and faster memory bandwidth. But they cost 25% more per second (35% on AWS). The critical question: Which queries run fast enough on Gen2 to offset the higher cost?

Answer: Compute-intensive queries (aggregations, filters, DML operations) benefit significantly. Memory-bound queries with spillage to remote storage see minimal improvement. This post provides a classification framework to identify which workloads justify Gen2 migration.

Understanding Warehouse Performance: The Foundation

Before evaluating Gen1 vs Gen2, you need to understand what drives query performance in Snowflake. When we assess the right warehouse configuration for any workload, we analyze key metrics that correlate with runtime predictability and cost efficiency.

As we discussed in our vertical scaling guide, the decision to scale up (larger warehouse) versus maintaining your current size depends on understanding your query’s bottlenecks.

Key Performance Indicators

Query Load PercentageHow much of the warehouse’s capacity is actively processing queries vs. idle.
Data Scanned VolumeThe amount of data read from storage to execute the query.
Spillage to DiskWhen query processing exceeds available memory:
- Local spillage (SSD): Slower but manageable
- Remote spillage (S3/cloud storage): Severe performance degradation
Execution Time BreakdownWhere does the query spend time?
- CPU processing: Aggregations, transformations, calculations
- I/O operations: Reading from disk, network transfers
- Memory operations: Sorting, joining, window functions

These same KPIs apply when evaluating Gen1 vs Gen2, but the underlying hardware differences change the equation.

Gen2 Architecture: What Actually Changed

Snowflake Gen2 warehouses aren’t just “faster Gen1.” They represent a fundamental hardware upgrade with specific architectural improvements.

Core Gen2 Enhancements

Faster Instructions Per Cycle (IPC)
- What it means: Each CPU core processes more instructions in a single clock cycle.
- Impact: Compute-heavy operations (aggregations, filters, transformations) complete 30-60% faster.
- Example:
  
  SELECT
    product_category,
    SUM(revenue) as total_revenue,
    AVG(quantity) as avg_quantity
  FROM sales
  WHERE sale_date >= ‘2025-01-01’
  GROUP BY product_category;
  
  Gen1 execution: 120 seconds
  Gen2 execution: 50 seconds (58% faster)
  Cost comparison: Gen1 = 0.033 credits | Gen2 = 0.017 credits × 1.25 = 0.021 credits
  Net result: 36% cost reduction despite 25% higher per-second rate
Increased Cache Per Processing Node
- What it means: More data can be held in fast-access CPU cache (L1, L2, L3).
- Impact: Repeated scans of hot data (frequently accessed tables) see dramatic speedups.
- Workloads that benefit:
  - BI dashboards hitting the same fact tables repeatedly
  - Iterative analytics on recent data
  - Real-time queries with narrow time windows
Wider Vector Throughput
- What it means: SIMD (Single Instruction, Multiple Data) operations can process more data elements simultaneously.
- Impact: Columnar scans, Snowflake’s bread and butter – become significantly faster.
- Example: Scanning 1 billion rows to filter by date range completes 40% faster on Gen2.
Faster DML Operations (DELETE, MERGE, UPDATE)
- What it means: Write operations leverage improved processing and memory bandwidth.
- Impact: ETL pipelines with heavy INSERT/UPDATE/DELETE operations see 50-67% runtime reductions.
- Real-world example:
  
  MERGE INTO target_table t
  USING source_table s ON t.id = s.id
  WHEN MATCHED THEN UPDATE SET t.value = s.value
  WHEN NOT MATCHED THEN INSERT VALUES (s.id, s.value);
  
  Gen1: 300 seconds
  Gen2: 100 seconds (67% faster, 55% lower cost)
50% Faster Memory Bandwidth
- What it means: Improved DDR (data transfer protocol) between RAM and CPU.
- Impact: Moving data between memory and processor happens twice as fast.
- Workloads that benefit:
  - Large in-memory sorts
  - Hash joins with data sets that fit in RAM
  - Window functions with large partition

What Didn’t Change

Critical limitation: Gen2 made almost no improvements to SSD or remote storage I/O.
Implication: Queries bottlenecked by disk I/O (especially remote spillage) won’t see meaningful speedups.

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The Cost-Performance Equation

Gen2 warehouses cost 25% more per second on Azure and 35% more per second on AWS ². The paradox: A query that runs faster can cost less if the speedup exceeds the premium.

Break-Even Analysis

For Gen2 to be cost-effective, the query must complete at least:

Azure: 20% faster (1 ÷ 1.25 = 0.80, meaning 80% of Gen1 time)
AWS: 26% faster (1 ÷ 1.35 = 0.74, meaning 74% of Gen1 time)

Example calculation (Azure):
Gen1 Large warehouse:

Query duration: 100 seconds
Cost: (100 sec ÷ 3600 sec/hour) × 8 credits = 0.222 credits

Gen2 Large warehouse (40% faster):

Query duration: 60 seconds
Cost: (60 sec ÷ 3600 sec/hour) × 8 credits × 1.25 = 0.167 credits

Savings: 25% lower cost despite 25% premium rate

When Gen2 doesn’t pay off:
Gen1 Large warehouse:

Query duration: 100 seconds
Cost: 0.222 credits

Gen2 Large warehouse (only 15% faster):

Query duration: 85 seconds
Cost: (85 ÷ 3600) × 8 × 1.25 = 0.236 credits

Loss: 6% higher cost

Below is the table with Query types.

Query Type	Bottleneck	Gen2 Advantage	Speed Gain	Cost/Perf?	Explanation
Filtered table scans	CPU + memory bandwidth	Faster CPU + 50% RAM bandwidth	✅ Medium–High	✅ Yes	Large scans benefit from vectorized CPU and faster RAM fetch.
Joins (small in-memory)	CPU + cache	Faster CPU + 2× L2 cache	✅ High	✅ Yes	Small joins fit in cache and compute.
Joins (medium in-memory)	RAM BW + cache	CPU + RAM BW + 2× cache	⚠️ Medium	⚠️ Mixed	Benefits vary depending on joint size and memory behavior.
Joins (large in-memory)	RAM capacity	No RAM size gain	⚠️ Small	❌ No	Heavy joins may not improve due to unchanged RAM size.
Joins (with spill)	Disk / SSD	No disk improvement	⚠️ Small	❌ No	SSD performance unchanged.
CTEs, nested queries	RAM BW + CPU	CPU + RAM BW + cache	⚠️ Medium	⚠️ Mixed	Logical CTEs benefit, but RAM-heavy ones may not.
Wide-table SELECTs	RAM BW + CPU	RAM BW + CPU	⚠️ Medium	⚠️ Mixed	Gen2 helps if RAM BW is the issue, not RAM size or spill.
Update / Delete / Merge	Disk I/O + CPU	DML engine optimized + CPU	✅ Very High	✅ Often	Optimized execution and CPU gains boost DML Operations (DELETE, MERGE, UPDATE) speed.
Write-heavy inserts	Disk I/O	No SSD gain	⚠️ Small	❌ No	The disk remains a bottleneck – no real improvement.
Insert‑select	CPU + RAM + disk	CPU, RAM BW, cache (not SSD)	⚠️ Medium	⚠️ Mixed	SELECT benefits, but the write path may be RAM/SSD-limited.
Materialized views	CPU + cache	CPU + cache	✅ High	✅ Yes	Pre-aggregated results are CPU/cache-bound.
Window functions	CPU + RAM BW	CPU + RAM BW + cache	✅ Medium	✅ Yes	Sort/partition logic benefits from Gen2 speed.
Sorting large datasets	RAM or disk	CPU + RAM BW	⚠️ Medium	⚠️ Mixed	Helps if in-RAM; no gain if spilling to disk.
Large subqueries with temp tables	Disk I/O	No disk gain	⚠️ Small	❌ No	Disk-bound
UDFs / JSON parsing	CPU-heavy	Faster CPU + higher IPC	✅ Medium–High	✅ Yes	CPU bound logic runs faster on Gen2.
Concurrency-heavy	Scheduler pressure	Per-core strength + cache	✅ Medium	✅ Yes	Handles more users efficiently.
Queries with spill	Disk I/O	No SSD gain	⚠️ Small	❌ No	No SSD boost in Gen2.

Query Classification Framework

Not all queries benefit equally from Gen2. Here’s how to categorize your workload.

Category 1: Clear Winners (Migrate to Gen2)

Characteristics:

Compute-intensive (aggregations, filters, transformations)
Minimal or no disk spillage
Scan large volumes of data with narrow filters
Heavy DML operations (MERGE, DELETE, UPDATE)

Query patterns:

Simple aggregations:

SELECT
  DATE_TRUNC(‘month’, order_date) AS month,
  COUNT(*) AS order_count,
  SUM(revenue) AS total_revenue
FROM orders
WHERE order_date >= ‘2024-01-01’
GROUP BY month;
Filtered table scans:

SELECT * FROM clickstream_events
WHERE event_date = CURRENT_DATE
AND user_country = ‘US’;
DML-heavy ETL:

DELETE FROM staging_table WHERE processed = TRUE;
INSERT INTO fact_table SELECT * FROM staging_table;

Expected improvement: 40-60% faster, 20-45% cost reduction

Category 2: No Benefit (Stay on Gen1)

Characteristics:

Memory-bound with remote spillage (S3/cloud storage)
Complex multi-table joins exceeding available RAM
Queries where I/O time dominates execution

Query patterns:

Large joins with spillage:

SELECT *
FROM large_fact_table f
JOIN dimension_1 d1 ON f.dim1_id = d1.id
JOIN dimension_2 d2 ON f.dim2_id = d2.id
JOIN dimension_3 d3 ON f.dim3_id = d3.id— If this spills to remote storage, Gen2 provides <10% speedup
Subqueries with massive temp tables:

WITH large_intermediate AS (
  SELECT * FROM huge_table_1
  JOIN huge_table_2 USING (key)
  — Temp result: 500 GB, exceeds warehouse memory
)
SELECT * FROM large_intermediate
WHERE condition = TRUE;

Why Gen2 doesn’t help: The bottleneck is moving data between remote storage and a compute. From HDD into I/O.
Expected improvement: <10% faster, 10-15% higher cost
Action: Fix the query instead—add clustering keys, reduce data volume, or materialize intermediate results.

Category 3: Maybe (Test Before Migrating)

Characteristics:

Medium-sized joins (data fits in RAM or spills only to local SSD)
Sorting large datasets
Mixed workloads with both compute and I/O operations

Query patterns:

Medium joins:

SELECT
c.customer_name,
SUM(o.order_value) AS total_spent
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
WHERE o.order_date >= ‘2025-01-01’
GROUP BY c.customer_name
ORDER BY total_spent DESC;
Large sorts:

SELECT *
FROM transactions
ORDER BY transaction_date DESC, customer_id
LIMIT 10000;

Decision metric: Calculate the processing-to-local-disk ratio.

Key Takeaways

Gen2 delivers 30-60% speedups for compute-intensive queries through improved CPU, cache, and memory bandwidth
Gen2 costs 25-35% more per second, so queries must run fast enough to offset the premium
Break-even threshold: 20-26% faster execution (depending on cloud provider)
Three query categories:
- Clear winners: Aggregations, filters, DML → Migrate
- No benefit: Remote spillage, I/O-bound → Stay Gen1
- Maybe: Use processing-to-disk ratio to decide
Processing ratio > 70% → Gen2 likely pays off
Fix queries before throwing Gen2 at them—clustering keys and query optimization often deliver better ROI than hardware upgrades

What’s Next?

Understanding Gen1 vs Gen2 is one piece of warehouse optimization. To build a complete cost-control strategy:

Continue reading:

Take action:

Audit your workload using the classification query above
Identify top 10 most expensive queries
Test Gen2 on “clear winner” queries for one week
Calculate ROI and migrate if cost-effective

Seemore Data’s Smart Pulse feature automatically identifies which warehouses benefit from Gen2, tests configurations, and recommends optimal sizing—saving teams weeks of manual analysis. Start your free trial →

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Gen1 vs Gen2 Snowflake Warehouses : When Does the 25% Premium Pay Off?

Understanding Warehouse Performance: The Foundation

Key Performance Indicators

Gen2 Architecture: What Actually Changed

Core Gen2 Enhancements

SELECT product_category, SUM(revenue) as total_revenue, AVG(quantity) as avg_quantity FROM sales WHERE sale_date >= ‘2025-01-01’ GROUP BY product_category;

MERGE INTO target_table t USING source_table s ON t.id = s.id WHEN MATCHED THEN UPDATE SET t.value = s.value WHEN NOT MATCHED THEN INSERT VALUES (s.id, s.value);

What Didn’t Change

The Cost-Performance Equation

Break-Even Analysis

Query Classification Framework

Category 1: Clear Winners (Migrate to Gen2)

SELECT DATE_TRUNC(‘month’, order_date) AS month, COUNT(*) AS order_count, SUM(revenue) AS total_revenue FROM orders WHERE order_date >= ‘2024-01-01’ GROUP BY month;

SELECT * FROM clickstream_events WHERE event_date = CURRENT_DATE AND user_country = ‘US’;

DELETE FROM staging_table WHERE processed = TRUE; INSERT INTO fact_table SELECT * FROM staging_table;

Category 2: No Benefit (Stay on Gen1)

SELECT * FROM large_fact_table f JOIN dimension_1 d1 ON f.dim1_id = d1.id JOIN dimension_2 d2 ON f.dim2_id = d2.id JOIN dimension_3 d3 ON f.dim3_id = d3.id— If this spills to remote storage, Gen2 provides <10% speedup

WITH large_intermediate AS ( SELECT * FROM huge_table_1 JOIN huge_table_2 USING (key) — Temp result: 500 GB, exceeds warehouse memory ) SELECT * FROM large_intermediate WHERE condition = TRUE;

Category 3: Maybe (Test Before Migrating)

SELECT c.customer_name, SUM(o.order_value) AS total_spent FROM customers c JOIN orders o ON c.customer_id = o.customer_id WHERE o.order_date >= ‘2025-01-01’ GROUP BY c.customer_name ORDER BY total_spent DESC;

SELECT * FROM transactions ORDER BY transaction_date DESC, customer_id LIMIT 10000;

Key Takeaways

What’s Next?

Understanding Snowflake Credits: The Currency of Cloud Data Warehousing

Best Snowflake Tools for Cost Optimization & Management

Frictionless Adoption Across Teams: How Tipalti Use Seemore for Scalable, Risk-Free Data Operations

Cool, now what can you DO with this?

SELECT
product_category,
SUM(revenue) as total_revenue,
AVG(quantity) as avg_quantity
FROM sales
WHERE sale_date >= ‘2025-01-01’
GROUP BY product_category;

MERGE INTO target_table t
USING source_table s ON t.id = s.id
WHEN MATCHED THEN UPDATE SET t.value = s.value
WHEN NOT MATCHED THEN INSERT VALUES (s.id, s.value);

SELECT
DATE_TRUNC(‘month’, order_date) AS month,
COUNT(*) AS order_count,
SUM(revenue) AS total_revenue
FROM orders
WHERE order_date >= ‘2024-01-01’
GROUP BY month;

SELECT * FROM clickstream_events
WHERE event_date = CURRENT_DATE
AND user_country = ‘US’;

DELETE FROM staging_table WHERE processed = TRUE;
INSERT INTO fact_table SELECT * FROM staging_table;

SELECT *
FROM large_fact_table f
JOIN dimension_1 d1 ON f.dim1_id = d1.id
JOIN dimension_2 d2 ON f.dim2_id = d2.id
JOIN dimension_3 d3 ON f.dim3_id = d3.id— If this spills to remote storage, Gen2 provides <10% speedup

WITH large_intermediate AS (
SELECT * FROM huge_table_1
JOIN huge_table_2 USING (key)
— Temp result: 500 GB, exceeds warehouse memory
)
SELECT * FROM large_intermediate
WHERE condition = TRUE;

SELECT
c.customer_name,
SUM(o.order_value) AS total_spent
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
WHERE o.order_date >= ‘2025-01-01’
GROUP BY c.customer_name
ORDER BY total_spent DESC;

SELECT *
FROM transactions
ORDER BY transaction_date DESC, customer_id
LIMIT 10000;

Cool, now
what can you DO with this?