software
Databricks vs Snowflake 2026: Which Is Better?
Databricks excels at unified data analytics with Apache Spark-based processing and AI/ML capabilities, while Snowflake specializes in cloud data warehousing with superior query performance and broader SQL compatibility. Databricks is better for complex data processing pipelines, while Snowflake is better for traditional analytics and business intelligence.
Databricks
Unified lakehouse platform combining data warehousing with Apache Spark-based processing and native ML capabilities.
Data engineers, ML practitioners, organizations with complex ETL pipelines, companies seeking multi-workload unification
Score71%
VS
S
Snowflake
Cloud-native data warehouse with separated compute and storage, optimized for SQL queries and BI analytics.
Analytics teams, business intelligence professionals, enterprises with SQL-heavy workloads, organizations with mature data warehouse experience
Score71%
Quick Answer
AI SummaryDatabricks excels at unified data analytics with Apache Spark-based processing and AI/ML capabilities, while Snowflake specializes in cloud data warehousing with superior query performance and broader SQL compatibility. Databricks is better for complex data processing pipelines, while Snowflake is better for traditional analytics and business intelligence.
Our Verdict
AI-assistedChoose Databricks if you need unified data processing with strong ML/AI capabilities, require ETL/ELT pipeline flexibility, or want to avoid vendor lock-in with open data formats like Delta Lake. Choose Snowflake if you prioritize fast SQL queries for analytics, prefer a simpler data warehouse architecture, have primarily structured data, or need the broadest ecosystem of third-party BI tool integrations.
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Databricks
6.3/10
Snowflake
8.8/10
S
Choose Databricks if
Data engineers, ML practitioners, organizations with complex ETL pipelines, companies seeking multi-workload unification
S
Choose Snowflake if
Best pickAnalytics teams, business intelligence professionals, enterprises with SQL-heavy workloads, organizations with mature data warehouse experience
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Key Differences at a Glance
- Primary Architecture:Lakehouse (unified storage + compute) vs Data Warehouse (separate storage & compute)
- Query Performance (TPC-DS Benchmark):✓ Snowflake wins(~28 seconds (native) vs ~45 seconds (Delta Lake))
- ML/AI Integration:✓ Databricks wins(Native MLflow + Apache Spark ML vs Limited native ML (requires integrations))
Key Facts & Figures
106 numeric metrics compared
| Metric | Databricks | Snowflake | Ratio |
|---|---|---|---|
| Starting Monthly Cost(USD) | $1,500-$4,000 | $2,000-$5,000 | |
| Setup Time(minutes) | 3-7 days | 1-3 days | |
| Query Performance (TPC-DS)(seconds) | 18-25 | 15-20 | |
| ML/AI Integration Score(out of 10) | 9/10 | 4/10 | |
| Global Enterprise Customers(count (2026)) | 6,500+ | 10,000+ | |
| Starting Compute Cost (per hour)(USD) | $0.30 (1 DBU compute) | — | — |
| Pre-built AutoML Models(models) | 12+ model families via AutoML | — | — |
| Native AWS Service Integrations(services) | 15+ (S3, RDS, Kinesis) | — | — |
| Training Job Spot Instance Discount(%) | Up to 70% savings | — | — |
| SQL Query Performance (sample 1TB table)(seconds) | 8-15 (native optimizations) | — | — |
| Setup Time to Production(hours) | 1-2 weeks | — | — |
| Starting Monthly Cost (Small Team)(USD) | $500-2,000 | — | — |
| Supported Data Connectors(count) | 15+ native connectors | — | — |
| Enterprise SLA Uptime(percent) | 99.9% | — | — |
| Average Query Latency (Analytical)(seconds) | 1-5 seconds (on cached data) | — | — |
| Time to Deploy (Basic Setup)(days) | 3-7 days | — | — |
| Monthly Starting Cost(USD) | $600-900 | — | — |
| Apache Spark Query Performance Boost(x faster vs open-source) | 10x (Photon engine) | — | — |
| Available Services(services) | 25+ integrated | — | — |
| BigQuery/Equivalent Query Speed (1TB dataset)(seconds) | 15-30 sec (via Databricks SQL) | — | — |
| Organizations Using Platform(count (thousands)) | 30,000+ | — | — |
| Enterprise Customers(millions) | 10,000+ | — | — |
| Query Latency (Average)(milliseconds) | 40-100 ms | — | — |
| Compute Cost Per Hour(USD) | $0.40-0.50 | — | — |
| Setup Complexity (1=Simple, 10=Complex)(scale) | 7/10 | — | — |
| Typical Query Latency (Structured Data)(seconds) | 5-15 seconds | — | — |
| Cloud Providers(count) | 3 (AWS, Azure, GCP) | — | — |
| Minimum Learning Curve (months for competency)(months) | 2-3 months | — | — |
| Starting Monthly Cost (1 TB storage + compute)(USD) | $400-800 (variable compute) | — | — |
| Spark Performance (Query Speed)(x faster relative to standard Spark) | 10-100x faster (Photon engine) | — | — |
| Total Service Offerings(services) | ~15 core data/AI services | — | — |
| Compute Instance Cost (Standard)(USD per hour) | $0.50-$2.50 (depends on cloud provider) | — | — |
| Typical Enterprise Migration Time(months) | 3-6 months (focused data/AI projects) | — | — |
| Initial Setup Time to Production(weeks) | 1-2 weeks | — | — |
| Processing Speed vs MapReduce Baseline(times faster) | 10-100x faster | — | — |
| Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD) | $550-850 | — | — |
| Required Team Skills (FTE equivalents for operations)(FTE) | 0.25 (minimal management) | — | — |
| SQL Query Standards Compliance(% ANSI SQL support) | Full ANSI SQL (100%) | — | — |
| Query Latency (median, standard ETL workload)(seconds) | 3.5-8 seconds | — | — |
| Built-in Collaboration Tools (notebooks, dashboards, repos)(count) | Notebooks, Dashboards, SQL Editor, Repos, MLflow | — | — |
| Community Size (GitHub Stars)(stars) | 8,200 stars (databricks/databricks-cli) | — | — |
| SQL Query Performance (TPC-DS 100TB)(seconds) | 285 seconds | — | — |
| Spark Job Acceleration(multiplier) | 3-5x faster (Photon engine) | — | — |
| ML Frameworks Supported(count) | 8 frameworks (via MLflow ecosystem) | — | — |
| Global Region Availability(regions) | 60+ (via partner clouds) | — | — |
| Enterprise Service Count(services) | 50+ (data/AI focused) | — | — |
| Starting Monthly Cost (10TB workload)(USD) | $3,500-$5,000 | — | — |
| SQL Query Performance (1TB dataset)(seconds) | 8-15 seconds | 2-5 seconds | |
| Base Monthly Cost (minimum)(USD) | $500+ | $120-240 | |
| Data Format Support(format types) | Any format (structured, unstructured, images, video) | Structured (optimized for tables/CSV/JSON) | |
| Concurrent Users Support(users) | Unlimited (serverless SQL analytics) | Unlimited (multi-cluster shared warehouse) | |
| Data Warehouse Setup Time(minutes) | 15-30 minutes | 5-10 minutes | |
| Global Market Share (2024)(percent) | 18% of lakehouse market | 32% of cloud data warehouse market | |
| ML Model Training Cost Efficiency(relative cost index) | 1.0x baseline (integrated ML platform) | 2.8x baseline (external ML tools required) | |
| Starting Monthly Cost (10GB active data)(USD) | $650 | $480 | |
| SQL Query Performance (TPC-DS Benchmark)(seconds) | 45 | 28 | |
| BI Tool Native Connectors(count) | 65 | 150+ | |
| Maximum Concurrent Queries Per Warehouse(queries) | Unlimited (Spark clusters) | 8-128 (warehouse-dependent) | — |
| Customer Satisfaction Rating (G2 2025)(percent) | 82% | 85% | |
| Setup Complexity (1-10 scale)(scale) | 7 | 4 | |
| Supported Cloud Providers(number of platforms) | 3 (AWS, Azure, GCP) | 3 (AWS, Azure, GCP) | |
| Setup Time to First Query(minutes) | 20-30 minutes | 20-30 minutes | |
| Data Marketplace Size(number of datasets) | 1,000+ datasets | 1,000+ datasets | |
| Annual Customer Growth Rate (2025)(percent) | 22% YoY | 22% YoY | |
| Average Enterprise Contract Value(USD thousands per year) | $200-500 | $200-500 | |
| Base Cost per TB (Monthly)(USD) | $4-6 | $4-6 | |
| Available Cloud Providers(count) | AWS, Azure, GCP | AWS, Azure, GCP | |
| Average Query Response Time(seconds) | 2-4 seconds | 2-4 seconds | |
| Time to Production (median)(weeks) | 1-3 weeks | 1-3 weeks | |
| Market Share 2026(percent) | 32% | 32% | |
| Query Latency (1 billion rows)(seconds) | 30 seconds | 30 seconds | |
| Monthly Cost (100 GB compressed)(USD) | $1,500 | $1,500 | |
| Ingestion Throughput(events/sec) | 100,000 events/sec | 100,000 events/sec | |
| Data Retention for Time-Travel(days) | 90 days | 90 days | |
| Compression Ratio(ratio) | 4:1 to 8:1 | 4:1 to 8:1 | |
| Learning Curve (1-10 Scale)(scale) | 3/10 (very easy) | 3/10 (very easy) | |
| Data Warehouse Query Speed (Typical)(seconds) | <5 seconds | <5 seconds | |
| Query Latency (1TB dataset)(seconds) | 30-120 seconds | 30-120 seconds | |
| Deployment Time(minutes) | 0.3-0.5 weeks (1-2 days) | 0.3-0.5 weeks (1-2 days) | |
| Annual Cost (100TB storage, 10 users)(USD) | $120,000-180,000 | $120,000-180,000 | |
| Maximum Scalability(petabytes) | Up to 50+ PB (cloud limits) | Up to 50+ PB (cloud limits) | |
| Time to First Query (production)(days) | 1-3 days | 1-3 days | |
| Required Technical Expertise Level(years experience needed) | 1-2 years (SQL knowledge) | 1-2 years (SQL knowledge) | |
| Annual License Cost (100TB data)(USD) | $240,000 | $240,000 | |
| Uptime SLA Guarantee(%) | 99.99% | 99.99% | |
| Query Response Time (10TB scan)(seconds) | 8.2 | 8.2 | |
| Data Format Support Count(formats) | 8 (Parquet, CSV, JSON, ORC, AVRO, XML, PDF, Images) | 8 (Parquet, CSV, JSON, ORC, AVRO, XML, PDF, Images) | |
| Available Integrations(count) | 600+ | 600+ | |
| Time to Production(minutes) | 0.5 | 0.5 | |
| Query Latency (Typical)(milliseconds) | 1,000-10,000ms | 1,000-10,000ms | |
| SQL Standard Compliance(percent) | 95% (full ANSI) | 95% (full ANSI) | |
| Enterprise Customers (2025)(count) | ~10,000 enterprises | ~10,000 enterprises | |
| Base Setup Cost (Annual)(USD) | $10,000-1,000,000 (credits-based) | $10,000-1,000,000 (credits-based) | |
| Time to Insight (Complex Query)(seconds) | 3-15 (depends on data size) | 3-15 (depends on data size) | |
| Maximum Daily Data Volume(terabytes) | Unlimited (petabyte-scale) | Unlimited (petabyte-scale) | |
| Operational Complexity (1-10 scale)(score) | 3/10 (managed cloud service) | 3/10 (managed cloud service) | |
| Initial Setup Time(minutes) | 30-45 minutes | 30-45 minutes | |
| TPC-DS 100TB Query Performance(seconds) | 38 seconds | 38 seconds | |
| Base Hourly Cost (2-node cluster)(USD/hour) | $4.00-$6.00 (Medium warehouse) | $4.00-$6.00 (Medium warehouse) | |
| Storage Cost (per TB/month)(USD) | $23 (on-demand) | $23 (on-demand) | |
| Query Performance (TPC-DS 100GB)(seconds) | ~14 seconds | ~14 seconds | |
| Scaling Adjustment Time(minutes) | ~1 (auto-scaling, no downtime) | ~1 (auto-scaling, no downtime) | |
| Maximum Single Query Data Scanned(petabytes) | 20+ | 20+ | |
| Cloud Providers Supported(count) | 3 (AWS, Azure, GCP) | 3 (AWS, Azure, GCP) | |
| Annual Contract Discount(percent) | Up to 20% | Up to 20% | |
| Configuration Tuning Required(hours (estimated)) | 4-8 (clustering hints optional) | 4-8 (clustering hints optional) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
Snowflake leads1 tie
S
4SnowflakeDatabricks
Attribute
Snowflake
- Lakehouse (unified storage + compute)Primary ArchitectureData Warehouse (separate storage & compute)
- ~45 seconds (Delta Lake)Query Performance (TPC-DS Benchmark)~28 seconds (native)(winner)
- Native MLflow + Apache Spark ML(winner)ML/AI IntegrationLimited native ML (requires integrations)
- $500-800Starting Monthly Cost (Small Workload)$400-600(winner)
- ANSI SQL + Spark SQL extensionsSQL Support & CompatibilityANSI SQL (T-SQL dialect native)(winner)
- Parquet, Delta, Iceberg, Hudi (open formats)(winner)Data Format SupportProprietary internal format (data lock-in)
- 82%Users Reporting High Satisfaction (2025 G2)85%(winner)
- Primary Architecture
Databricks
Lakehouse (unified storage + compute)
Snowflake
Data Warehouse (separate storage & compute)
- Query Performance (TPC-DS Benchmark)
Databricks
~45 seconds (Delta Lake)
Snowflake
~28 seconds (native)(winner)
- ML/AI Integration
Databricks
Native MLflow + Apache Spark ML(winner)
Snowflake
Limited native ML (requires integrations)
- Starting Monthly Cost (Small Workload)
Databricks
$500-800
Snowflake
$400-600(winner)
- SQL Support & Compatibility
Databricks
ANSI SQL + Spark SQL extensions
Snowflake
ANSI SQL (T-SQL dialect native)(winner)
- Data Format Support
Databricks
Parquet, Delta, Iceberg, Hudi (open formats)(winner)
Snowflake
Proprietary internal format (data lock-in)
- Users Reporting High Satisfaction (2025 G2)
Databricks
82%
Snowflake
85%(winner)
Full Comparison
| Attribute | Databricks | Snowflake |
|---|---|---|
| Starting Monthly Cost(USD) | $1,500-$4,000(winner) | $2,000-$5,000 |
| Starting Compute Cost (per hour)(USD) | $0.30 (1 DBU compute) | — |
| Starting Monthly Cost (Small Team)(USD) | $500-2,000 | — |
| Monthly Starting Cost(USD) | $600-900 | — |
| Compute Cost Per Hour(USD) | $0.40-0.50 | — |
Show 15 more attributesStarting Monthly Cost (1 TB storage + compute)(USD) $400-800 (variable compute) — Compute Instance Cost (Standard)(USD per hour) $0.50-$2.50 (depends on cloud provider) — Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD) $550-850 — Starting Monthly Cost (10TB workload)(USD) $3,500-$5,000 — Base Monthly Cost (minimum)(USD) $500+ $120-240 Starting Monthly Cost (10GB active data)(USD) $650 $480 Base Query Cost(USD per TB scanned) $2-4 per credit — Average Enterprise Contract Value(USD thousands per year) $200-500 — Base Cost per TB (Monthly)(USD) $4-6 — Monthly Cost (100 GB compressed)(USD) $1,500 — Annual License Cost (100TB data)(USD) $240,000 — Base Setup Cost (Annual)(USD) $10,000-1,000,000 (credits-based) — Base Hourly Cost (2-node cluster)(USD/hour) $4.00-$6.00 (Medium warehouse) — Storage Cost (per TB/month)(USD) $23 (on-demand) — Annual Contract Discount(percent) Up to 20% — | ||
| Setup Time(minutes) | 3-7 days | 1-3 days(winner) |
| Customer Satisfaction Rating (G2 2025)(percent) | 82% | 85%(winner) |
| Initial Setup Time(minutes) | 30-45 minutes | — |
| Query Performance (TPC-DS)(seconds) | 18-25 | 15-20(winner) |
| SQL Query Performance (sample 1TB table)(seconds) | 8-15 (native optimizations) | — |
| Average Query Latency (Analytical)(seconds) | 1-5 seconds (on cached data) | — |
| Apache Spark Query Performance Boost(x faster vs open-source) | 10x (Photon engine) | — |
| BigQuery/Equivalent Query Speed (1TB dataset)(seconds) | 15-30 sec (via Databricks SQL) | — |
Show 24 more attributesQuery Latency (Average)(milliseconds) 40-100 ms — Typical Query Latency (Structured Data)(seconds) 5-15 seconds — Spark Performance (Query Speed)(x faster relative to standard Spark) 10-100x faster (Photon engine) — Processing Speed vs MapReduce Baseline(times faster) 10-100x faster — Query Latency (median, standard ETL workload)(seconds) 3.5-8 seconds — SQL Query Performance (TPC-DS 100TB)(seconds) 285 seconds — Spark Job Acceleration(multiplier) 3-5x faster (Photon engine) — SQL Query Performance (1TB dataset)(seconds) 8-15 seconds 2-5 seconds SQL Query Performance (TPC-DS Benchmark)(seconds) 45 28 Maximum Concurrent Queries Per Warehouse(queries) Unlimited (Spark clusters) 8-128 (warehouse-dependent) Maximum Query Timeout(hours) Limited by warehouse size — Concurrent User Support(scalability level) Limited by warehouse size, manual tuning — Average Query Response Time(seconds) 2-4 seconds — Query Latency (1 billion rows)(seconds) 30 seconds — Ingestion Throughput(events/sec) 100,000 events/sec — Data Warehouse Query Speed (Typical)(seconds) <5 seconds — Query Latency (1TB dataset)(seconds) 30-120 seconds — Deployment Time(minutes) 0.3-0.5 weeks (1-2 days) — Query Response Time (10TB scan)(seconds) 8.2 — Query Latency (Typical)(milliseconds) 1,000-10,000ms — Data Ingestion Rate(events per second) Batch-based (bulk loading) — Time to Insight (Complex Query)(seconds) 3-15 (depends on data size) — TPC-DS 100TB Query Performance(seconds) 38 seconds — Query Performance (TPC-DS 100GB)(seconds) ~14 seconds — | ||
| ML/AI Integration Score(out of 10) | 9/10(winner) | 4/10 |
| Native ML Framework Integration | MLflow + Spark ML | Cortex AI (basic) |
| Global Enterprise Customers(count (2026)) | 6,500+ | 10,000+(winner) |
| Global Market Share (2024)(percent) | 18% of lakehouse market | 32% of cloud data warehouse market(winner) |
| Market Share 2026(percent) | 32% | — |
| Supported Data Formats(types) | All formats (Delta, Parquet, Images, Videos, Audio) | Structured (Parquet, CSV, JSON) |
| Multi-Cloud Support(cloud providers) | AWS, Azure, GCP | — |
| Data Format Support(format types) | Any format (structured, unstructured, images, video)(winner) | Structured (optimized for tables/CSV/JSON) |
| Data Sharing Standard(technology) | Delta Sharing (open standard) | Snowflake Marketplace (proprietary) |
| SQL Query Standards Compliance(% ANSI SQL support) | Full ANSI SQL (100%) | — |
| Built-in Collaboration Tools (notebooks, dashboards, repos)(count) | Notebooks, Dashboards, SQL Editor, Repos, MLflow | — |
| Native ML/AI Capabilities | Native (MLflow, AutoML, Feature Store) | Limited (external integration required) |
| Data Sharing Capability | Native, cross-account/cross-cloud | — |
Show 3 more attributesZero-Copy Cloning Available (instant, free) — Data Retention for Time-Travel(days) 90 days — Data Format Support Count(formats) 8 (Parquet, CSV, JSON, ORC, AVRO, XML, PDF, Images) — | ||
| Multi-Language Support(languages) | SQL, Python, Scala, R, Java | SQL primarily |
| Supported Cloud Platforms | AWS, Azure, GCP | — |
| Cloud Providers(count) | 3 (AWS, Azure, GCP) | — |
| Global Region Availability(regions) | 60+ (via partner clouds) | — |
| Supported Cloud Providers(number of platforms) | 3 (AWS, Azure, GCP) | — |
| Available Cloud Providers(count) | AWS, Azure, GCP | — |
Show 1 more attributeCloud Platform Support AWS, Azure, Google Cloud — | ||
| Pre-built AutoML Models(models) | 12+ model families via AutoML | — |
| Real-Time Notebook Collaboration Users(concurrent users) | Unlimited simultaneous editing | — |
| Users Per Collaborative Project(concurrent users) | Unlimited with real-time sync | — |
| Native AWS Service Integrations(services) | 15+ (S3, RDS, Kinesis) | — |
| BI Tool Native Connectors(count) | 65 | 150+(winner) |
| Delta Lake Support | Native Delta Lake engine | — |
| Training Job Spot Instance Discount(%) | Up to 70% savings | — |
| Initial Licensing Cost(USD) | $2,000-$15,000/month | — |
| Annual Cost (100TB storage, 10 users)(USD) | $120,000-180,000 | — |
| Setup Time to Production(hours) | 1-2 weeks | — |
| Time to Deploy (Basic Setup)(days) | 3-7 days | — |
| Typical Enterprise Migration Time(months) | 3-6 months (focused data/AI projects) | — |
| Time to Production (median)(weeks) | 1-3 weeks | — |
| Time to First Query (production)(days) | 1-3 days | — |
| Cluster Management Required(hours/month) | Minimal (<5 hours/month) | — |
| Infrastructure Management Required | Manual cluster setup and scaling | — |
| Required Team Skills (FTE equivalents for operations)(FTE) | 0.25 (minimal management) | — |
| Operational Complexity (1-10 scale)(score) | 3/10 (managed cloud service) | — |
| Scaling Adjustment Time(minutes) | ~1 (auto-scaling, no downtime) | — |
Show 1 more attributeConfiguration Tuning Required(hours (estimated)) 4-8 (clustering hints optional) — | ||
| Built-in Security Features | 6+ (SSO, RBAC, audit logging, IP controls, encryption, workspace isolation) | — |
| Supported Data Formats(formats) | All Spark formats + native Delta Lake optimization | — |
| SQL Standard Compliance(percent) | 95% (full ANSI) | — |
| Community Size(members/stars) | 8,000+ questions | — |
| Community Size (GitHub Stars)(stars) | 8,200 stars (databricks/databricks-cli) | — |
| SQL Standard Compliance Level(null) | ANSI SQL with Spark extensions | — |
| Supported Data Connectors(count) | 15+ native connectors | — |
| Available Integrations(count) | 600+ | — |
| Enterprise SLA Uptime(percent) | 99.9% | — |
| Uptime SLA Guarantee(%) | 99.99% | — |
| Native ML/AI Features(null) | MLflow, Feature Store, AutoML included | — |
| ML Frameworks Supported(count) | 8 frameworks (via MLflow ecosystem) | — |
| Data Consolidation Required(null) | Yes, into Delta Lake | — |
| Deployment Options | Cloud-only (3 regions) | SaaS only (AWS/Azure/GCP) |
| Compute-Storage Decoupling | Complete separation | — |
| Available Services(services) | 25+ integrated | — |
| Organizations Using Platform(count (thousands)) | 30,000+ | — |
| Enterprise Customers (2025)(count) | ~10,000 enterprises | — |
| Native ML Pipeline Integration(rating) | MLflow + Databricks Intelligence Engine (built-in) | — |
| Data Lakehouse ACID Support(capability) | Native Delta Lake with ACID, time travel, schema evolution | — |
| Data Governance Features(key capabilities) | Unity Catalog, lineage, access control, Delta Lake | — |
| Enterprise Customers(millions) | 10,000+ | — |
| Annual Customer Growth Rate (2025)(percent) | 22% YoY | — |
| ML Feature Store(null) | Native MLflow Feature Store included | — |
| Native ML Framework Support | MLflow, Spark MLlib, TensorFlow, PyTorch | — |
| Native ML Ops Tools(tools included) | MLflow, Feature Store, Model Registry | — |
| Data Governance (Unity Catalog equivalent)(null) | Unity Catalog with lineage, tags, access control | — |
| Setup Complexity (1=Simple, 10=Complex)(scale) | 7/10 | — |
| Learning Curve (1-10 Scale)(scale) | 3/10 (very easy) | — |
| Fortune 500 Adoption(%) | 40% | — |
| Supported Data Types | Structured, semi-structured, unstructured | — |
| Minimum Learning Curve (months for competency)(months) | 2-3 months | — |
| Data Warehouse Setup Time(minutes) | 15-30 minutes | 5-10 minutes(winner) |
| Setup Complexity (1-10 scale)(scale) | 7 | 4(winner) |
| Setup Time(minutes) | 15 minutes | — |
| Time to Production(minutes) | 0.5 | — |
| Total Service Offerings(services) | ~15 core data/AI services | — |
| Enterprise Service Count(services) | 50+ (data/AI focused) | — |
| Microsoft Ecosystem Integration(native integrations) | Limited (Power BI via connector only) | — |
| Initial Setup Time to Production(weeks) | 1-2 weeks | — |
| Setup Time to First Query(minutes) | 20-30 minutes | — |
| Cloud Providers Supported(count) | 3 (AWS, Azure, GCP) | — |
| Data Governance Granularity(access level) | Column, row, and table-level with tags | — |
| ACID Transaction Support(boolean) | Native (Delta Lake) | — |
| Concurrent Users Support(users) | Unlimited (serverless SQL analytics) | Unlimited (multi-cluster shared warehouse) |
| Maximum Scalability(petabytes) | Up to 50+ PB (cloud limits) | — |
| Maximum Concurrent Users(users) | Unlimited (elastic scaling) | — |
| Maximum Daily Data Volume(terabytes) | Unlimited (petabyte-scale) | — |
| ML Model Training Cost Efficiency(relative cost index) | 1.0x baseline (integrated ML platform)(winner) | 2.8x baseline (external ML tools required) |
| Data Format Lock-in Risk | Low (open Delta/Iceberg formats) | High (proprietary format) |
| Data Marketplace Size(number of datasets) | 1,000+ datasets | — |
| Compression Ratio(ratio) | 4:1 to 8:1 | — |
| Licensing Model | Consumption-based (compute + storage) | — |
| Supported Query Languages(count) | SQL, Python, Java, JavaScript, Scala | — |
| Required Technical Expertise Level(years experience needed) | 1-2 years (SQL knowledge) | — |
| Real-time Analytics Capability | Yes (sub-second latency) | — |
| Maximum Single Query Data Scanned(petabytes) | 20+ | — |
DatabricksSSnowflake
Starting Monthly Cost(USD)
$1,500-$4,000
$2,000-$5,000
Starting Compute Cost (per hour)(USD)
$0.30 (1 DBU compute)
—
Starting Monthly Cost (Small Team)(USD)
$500-2,000
—
Monthly Starting Cost(USD)
$600-900
—
Compute Cost Per Hour(USD)
$0.40-0.50
—
Show 15 more attributes
Starting Monthly Cost (1 TB storage + compute)(USD)
$400-800 (variable compute)
—
Compute Instance Cost (Standard)(USD per hour)
$0.50-$2.50 (depends on cloud provider)
—
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)(USD)
$550-850
—
Starting Monthly Cost (10TB workload)(USD)
$3,500-$5,000
—
Base Monthly Cost (minimum)(USD)
$500+
$120-240
Starting Monthly Cost (10GB active data)(USD)
$650
$480
Base Query Cost(USD per TB scanned)
$2-4 per credit
—
Average Enterprise Contract Value(USD thousands per year)
$200-500
—
Base Cost per TB (Monthly)(USD)
$4-6
—
Monthly Cost (100 GB compressed)(USD)
$1,500
—
Annual License Cost (100TB data)(USD)
$240,000
—
Base Setup Cost (Annual)(USD)
$10,000-1,000,000 (credits-based)
—
Base Hourly Cost (2-node cluster)(USD/hour)
$4.00-$6.00 (Medium warehouse)
—
Storage Cost (per TB/month)(USD)
$23 (on-demand)
—
Annual Contract Discount(percent)
Up to 20%
—
Setup Time(minutes)
3-7 days
1-3 days
Customer Satisfaction Rating (G2 2025)(percent)
82%
85%
Initial Setup Time(minutes)
30-45 minutes
—
Query Performance (TPC-DS)(seconds)
18-25
15-20
SQL Query Performance (sample 1TB table)(seconds)
8-15 (native optimizations)
—
Average Query Latency (Analytical)(seconds)
1-5 seconds (on cached data)
—
Apache Spark Query Performance Boost(x faster vs open-source)
10x (Photon engine)
—
BigQuery/Equivalent Query Speed (1TB dataset)(seconds)
15-30 sec (via Databricks SQL)
—
Show 24 more attributes
Query Latency (Average)(milliseconds)
40-100 ms
—
Typical Query Latency (Structured Data)(seconds)
5-15 seconds
—
Spark Performance (Query Speed)(x faster relative to standard Spark)
10-100x faster (Photon engine)
—
Processing Speed vs MapReduce Baseline(times faster)
10-100x faster
—
Query Latency (median, standard ETL workload)(seconds)
3.5-8 seconds
—
SQL Query Performance (TPC-DS 100TB)(seconds)
285 seconds
—
Spark Job Acceleration(multiplier)
3-5x faster (Photon engine)
—
SQL Query Performance (1TB dataset)(seconds)
8-15 seconds
2-5 seconds
SQL Query Performance (TPC-DS Benchmark)(seconds)
45
28
Maximum Concurrent Queries Per Warehouse(queries)
Unlimited (Spark clusters)
8-128 (warehouse-dependent)
Maximum Query Timeout(hours)
Limited by warehouse size
—
Concurrent User Support(scalability level)
Limited by warehouse size, manual tuning
—
Average Query Response Time(seconds)
2-4 seconds
—
Query Latency (1 billion rows)(seconds)
30 seconds
—
Ingestion Throughput(events/sec)
100,000 events/sec
—
Data Warehouse Query Speed (Typical)(seconds)
<5 seconds
—
Query Latency (1TB dataset)(seconds)
30-120 seconds
—
Deployment Time(minutes)
0.3-0.5 weeks (1-2 days)
—
Query Response Time (10TB scan)(seconds)
8.2
—
Query Latency (Typical)(milliseconds)
1,000-10,000ms
—
Data Ingestion Rate(events per second)
Batch-based (bulk loading)
—
Time to Insight (Complex Query)(seconds)
3-15 (depends on data size)
—
TPC-DS 100TB Query Performance(seconds)
38 seconds
—
Query Performance (TPC-DS 100GB)(seconds)
~14 seconds
—
ML/AI Integration Score(out of 10)
9/10
4/10
Native ML Framework Integration
MLflow + Spark ML
Cortex AI (basic)
Global Enterprise Customers(count (2026))
6,500+
10,000+
Global Market Share (2024)(percent)
18% of lakehouse market
32% of cloud data warehouse market
Market Share 2026(percent)
32%
—
Supported Data Formats(types)
All formats (Delta, Parquet, Images, Videos, Audio)
Structured (Parquet, CSV, JSON)
Multi-Cloud Support(cloud providers)
AWS, Azure, GCP
—
Data Format Support(format types)
Any format (structured, unstructured, images, video)
Structured (optimized for tables/CSV/JSON)
Data Sharing Standard(technology)
Delta Sharing (open standard)
Snowflake Marketplace (proprietary)
SQL Query Standards Compliance(% ANSI SQL support)
Full ANSI SQL (100%)
—
Built-in Collaboration Tools (notebooks, dashboards, repos)(count)
Notebooks, Dashboards, SQL Editor, Repos, MLflow
—
Native ML/AI Capabilities
Native (MLflow, AutoML, Feature Store)
Limited (external integration required)
Data Sharing Capability
Native, cross-account/cross-cloud
—
Show 3 more attributes
Zero-Copy Cloning
Available (instant, free)
—
Data Retention for Time-Travel(days)
90 days
—
Data Format Support Count(formats)
8 (Parquet, CSV, JSON, ORC, AVRO, XML, PDF, Images)
—
Multi-Language Support(languages)
SQL, Python, Scala, R, Java
SQL primarily
Supported Cloud Platforms
AWS, Azure, GCP
—
Cloud Providers(count)
3 (AWS, Azure, GCP)
—
Global Region Availability(regions)
60+ (via partner clouds)
—
Supported Cloud Providers(number of platforms)
3 (AWS, Azure, GCP)
—
Available Cloud Providers(count)
AWS, Azure, GCP
—
Show 1 more attribute
Cloud Platform Support
AWS, Azure, Google Cloud
—
Pre-built AutoML Models(models)
12+ model families via AutoML
—
Real-Time Notebook Collaboration Users(concurrent users)
Unlimited simultaneous editing
—
Users Per Collaborative Project(concurrent users)
Unlimited with real-time sync
—
Native AWS Service Integrations(services)
15+ (S3, RDS, Kinesis)
—
BI Tool Native Connectors(count)
65
150+
Delta Lake Support
Native Delta Lake engine
—
Training Job Spot Instance Discount(%)
Up to 70% savings
—
Initial Licensing Cost(USD)
$2,000-$15,000/month
—
Annual Cost (100TB storage, 10 users)(USD)
$120,000-180,000
—
Setup Time to Production(hours)
1-2 weeks
—
Time to Deploy (Basic Setup)(days)
3-7 days
—
Typical Enterprise Migration Time(months)
3-6 months (focused data/AI projects)
—
Time to Production (median)(weeks)
1-3 weeks
—
Time to First Query (production)(days)
1-3 days
—
Cluster Management Required(hours/month)
Minimal (<5 hours/month)
—
Infrastructure Management Required
Manual cluster setup and scaling
—
Required Team Skills (FTE equivalents for operations)(FTE)
0.25 (minimal management)
—
Operational Complexity (1-10 scale)(score)
3/10 (managed cloud service)
—
Scaling Adjustment Time(minutes)
~1 (auto-scaling, no downtime)
—
Show 1 more attribute
Configuration Tuning Required(hours (estimated))
4-8 (clustering hints optional)
—
Built-in Security Features
6+ (SSO, RBAC, audit logging, IP controls, encryption, workspace isolation)
—
Supported Data Formats(formats)
All Spark formats + native Delta Lake optimization
—
SQL Standard Compliance(percent)
95% (full ANSI)
—
Community Size(members/stars)
8,000+ questions
—
Community Size (GitHub Stars)(stars)
8,200 stars (databricks/databricks-cli)
—
SQL Standard Compliance Level(null)
ANSI SQL with Spark extensions
—
Supported Data Connectors(count)
15+ native connectors
—
Available Integrations(count)
600+
—
Enterprise SLA Uptime(percent)
99.9%
—
Uptime SLA Guarantee(%)
99.99%
—
Native ML/AI Features(null)
MLflow, Feature Store, AutoML included
—
ML Frameworks Supported(count)
8 frameworks (via MLflow ecosystem)
—
Data Consolidation Required(null)
Yes, into Delta Lake
—
Deployment Options
Cloud-only (3 regions)
SaaS only (AWS/Azure/GCP)
Compute-Storage Decoupling
Complete separation
—
Available Services(services)
25+ integrated
—
Organizations Using Platform(count (thousands))
30,000+
—
Enterprise Customers (2025)(count)
~10,000 enterprises
—
Native ML Pipeline Integration(rating)
MLflow + Databricks Intelligence Engine (built-in)
—
Data Lakehouse ACID Support(capability)
Native Delta Lake with ACID, time travel, schema evolution
—
Data Governance Features(key capabilities)
Unity Catalog, lineage, access control, Delta Lake
—
Enterprise Customers(millions)
10,000+
—
Annual Customer Growth Rate (2025)(percent)
22% YoY
—
ML Feature Store(null)
Native MLflow Feature Store included
—
Native ML Framework Support
MLflow, Spark MLlib, TensorFlow, PyTorch
—
Native ML Ops Tools(tools included)
MLflow, Feature Store, Model Registry
—
Data Governance (Unity Catalog equivalent)(null)
Unity Catalog with lineage, tags, access control
—
Setup Complexity (1=Simple, 10=Complex)(scale)
7/10
—
Learning Curve (1-10 Scale)(scale)
3/10 (very easy)
—
Fortune 500 Adoption(%)
40%
—
Supported Data Types
Structured, semi-structured, unstructured
—
Minimum Learning Curve (months for competency)(months)
2-3 months
—
Data Warehouse Setup Time(minutes)
15-30 minutes
5-10 minutes
Setup Complexity (1-10 scale)(scale)
7
4
Setup Time(minutes)
15 minutes
—
Time to Production(minutes)
0.5
—
Total Service Offerings(services)
~15 core data/AI services
—
Enterprise Service Count(services)
50+ (data/AI focused)
—
Microsoft Ecosystem Integration(native integrations)
Limited (Power BI via connector only)
—
Initial Setup Time to Production(weeks)
1-2 weeks
—
Setup Time to First Query(minutes)
20-30 minutes
—
Cloud Providers Supported(count)
3 (AWS, Azure, GCP)
—
Data Governance Granularity(access level)
Column, row, and table-level with tags
—
ACID Transaction Support(boolean)
Native (Delta Lake)
—
Concurrent Users Support(users)
Unlimited (serverless SQL analytics)
Unlimited (multi-cluster shared warehouse)
Maximum Scalability(petabytes)
Up to 50+ PB (cloud limits)
—
Maximum Concurrent Users(users)
Unlimited (elastic scaling)
—
Maximum Daily Data Volume(terabytes)
Unlimited (petabyte-scale)
—
ML Model Training Cost Efficiency(relative cost index)
1.0x baseline (integrated ML platform)
2.8x baseline (external ML tools required)
Data Format Lock-in Risk
Low (open Delta/Iceberg formats)
High (proprietary format)
Data Marketplace Size(number of datasets)
1,000+ datasets
—
Compression Ratio(ratio)
4:1 to 8:1
—
Licensing Model
Consumption-based (compute + storage)
—
Supported Query Languages(count)
SQL, Python, Java, JavaScript, Scala
—
Required Technical Expertise Level(years experience needed)
1-2 years (SQL knowledge)
—
Real-time Analytics Capability
Yes (sub-second latency)
—
Maximum Single Query Data Scanned(petabytes)
20+
—
Pros & Cons
10 pros·4 cons across both
S
Databricks
+5-2
Pros
- Native MLflow integration for end-to-end ML workflows without external tools
- Open data formats (Delta Lake, Apache Iceberg) prevent vendor lock-in
- Unified interface for batch processing, streaming, and real-time analytics
- Superior handling of unstructured data (images, text, video) via Spark
- Apache Spark ecosystem provides massive horizontal scalability
Cons
- Query performance on complex SQL operations ~50% slower than Snowflake (45s vs 28s TPC-DS)
- Steeper learning curve for SQL-only analysts unfamiliar with PySpark/Scala
S
Snowflake
+5-2
Pros
- Industry-leading SQL query performance (28s TPC-DS vs competitors' 45+s) due to optimized indexing
- Frictionless BI tool ecosystem (90%+ of BI platforms certified native connectors)
- Zero-copy cloning allows instant full database copies for testing
- Automatic scaling handles variable workloads without manual intervention
- Superior time-series data optimization with built-in ARRAY/OBJECT support
Cons
- Proprietary data format creates vendor lock-in; data retrieval adds 10-15% overhead
- Limited native ML/AI capabilities; requires expensive third-party integrations or Snowflake's weak Cortex AI
Frequently Asked Questions
5 questions
Snowflake is typically 25-30% cheaper at small scale ($480-600/month vs Databricks $650-800/month). However, Databricks' open data format saves expensive data egress fees if you later migrate, offsetting some cost difference over 2-3 years.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
As an affiliate, we may earn a commission from qualifying purchases at no extra cost to you. Learn more about our affiliate disclosure
Wikipedia
- W
Databricks on Wikipedia (opens in new tab)
Unified lakehouse platform combining data warehousing with Apache Spark-based processing and native ML capabilities.
- W
Snowflake on Wikipedia (opens in new tab)
Cloud-native data warehouse with separated compute and storage, optimized for SQL queries and BI analytics.
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