Snowflake is a pure cloud data warehouse optimized for SQL analytics with consumption-based pricing, while Databricks is a unified lakehouse platform combining data warehousing and AI/ML workloads with Apache Spark at its core. Databricks excels for organizations needing integrated machine learning, while Snowflake dominates traditional analytics and BI use cases.
Snowflake vs Databricks — Side-by-side comparison on A Versus B
Enterprise cloud data warehouse with native multi-cloud support and broad BI ecosystem integration.
AI and data lakehouse platform built on Apache Spark for unified analytics and ML workflows
Core Architecture. ML/AI Capabilities. Query Engine. Starting Price (monthly). SQL Analytics Performance. Data Format Flexibility. Time-to-Insight for Analytics
Choose Snowflake if your primary focus is SQL-based analytics, business intelligence, and cost-predictability with straightforward data warehousing needs. Choose Databricks if you need a unified platform for analytics AND machine learning, work with diverse data formats, or require Apache Spark's distributed processing capabilities for complex transformations.
Snowflake: 1-3 days | Databricks: 3-7 days
Snowflake: Structured (Parquet, CSV, JSON) | Databricks: All formats (Delta, Parquet, Images, Videos, Audio)
Snowflake: Snowflake Marketplace (proprietary) | Databricks: Delta Sharing (open standard)
Snowflake: SQL primarily | Databricks: SQL, Python, Scala, R, Java
Snowflake: $2-4 per credit | Databricks: N/A
Snowflake: 3 (AWS, Azure, GCP) | Databricks: N/A
Snowflake: 20-30 minutes | Databricks: N/A
Snowflake: Limited by warehouse size | Databricks: N/A
Snowflake: 1,000+ datasets | Databricks: N/A
Snowflake: Limited by warehouse size, manual tuning | Databricks: N/A
Snowflake: AWS, Azure, GCP | Databricks: N/A
Snowflake: 2-4 seconds | Databricks: N/A
Snowflake: Native, cross-account/cross-cloud | Databricks: N/A
Snowflake: Complete separation | Databricks: N/A
Snowflake: Available (instant, free) | Databricks: N/A
Snowflake: 100,000 events/sec | Databricks: N/A
Snowflake: Consumption-based (compute + storage) | Databricks: N/A
Snowflake: 3/10 (very easy) | Databricks: N/A
Snowflake: 30-120 seconds | Databricks: N/A
Snowflake: 0.3-0.5 weeks (1-2 days) | Databricks: N/A
Snowflake: $120,000-180,000 | Databricks: N/A
Snowflake: Up to 50+ PB (cloud limits) | Databricks: N/A
Snowflake: SQL, Python, Java, JavaScript, Scala | Databricks: N/A
Snowflake: 1-2 years (SQL knowledge) | Databricks: N/A
Snowflake: Yes (sub-second latency) | Databricks: N/A
Snowflake: 8 (Parquet, CSV, JSON, ORC, AVRO, XML, PDF, Images) | Databricks: N/A
Snowflake: Cloud-only (SaaS) | Databricks: Cloud-only (3 regions)
Snowflake: 1,000-10,000ms | Databricks: N/A
Snowflake: Batch-based (bulk loading) | Databricks: N/A
Snowflake: 95% (full ANSI) | Databricks: N/A
Snowflake: ~10,000 enterprises | Databricks: N/A
Snowflake: $10,000-1,000,000 (credits-based) | Databricks: N/A
Snowflake: 3-15 (depends on data size) | Databricks: N/A
Snowflake: Unlimited (petabyte-scale) | Databricks: N/A
Snowflake: 3/10 (managed cloud service) | Databricks: N/A
Snowflake: 2-5 seconds | Databricks: 8-15 seconds
Snowflake: Limited (external integration required) | Databricks: Native (MLflow, AutoML, Feature Store)
Snowflake: Structured (optimized for tables/CSV/JSON) | Databricks: Delta, Parquet, ORC, CSV, JSON (open-source native)
Snowflake: Unlimited (multi-cluster shared warehouse) | Databricks: Unlimited (serverless SQL analytics)
Snowflake: 5-10 minutes | Databricks: 15-30 minutes
Snowflake: 32% of cloud data warehouse market | Databricks: 18% of lakehouse market
Snowflake: 2.8x baseline (external ML tools required) | Databricks: 1.0x baseline (integrated ML platform)
Snowflake: 0.1 weeks (24 hours) | Databricks: N/A
Snowflake: $4.00-$6.00 (Medium warehouse) | Databricks: N/A
Snowflake: $23 (on-demand) | Databricks: N/A
Snowflake: ~14 seconds | Databricks: N/A
Snowflake: ~1 (auto-scaling, no downtime) | Databricks: N/A
Snowflake: 4-8 (clustering hints optional) | Databricks: N/A
Starting Monthly Cost (10GB active data)
SQL Query Performance (TPC-DS Benchmark)
Snowflake: Cortex AI (basic) | Databricks: MLflow + Spark ML
Snowflake: High (proprietary format) | Databricks: Low (open Delta/Iceberg formats)
Maximum Concurrent Queries Per Warehouse
Snowflake: 8-128 (warehouse-dependent) | Databricks: Unlimited (Spark clusters)
Annual TCO (100TB storage, average usage)
Snowflake: 10-15 hours | Databricks: 1-2 weeks
Snowflake: Independent scaling | Databricks: N/A
Snowflake: None (on-demand only) | Databricks: N/A
Snowflake: ANSI SQL compliant | Databricks: N/A
Snowflake: 5 (account creation) | Databricks: N/A
Snowflake: 1 (virtual warehouse) | Databricks: N/A
Query Performance (10TB TPC-DS benchmark)
Snowflake: Fully independent (separate pricing) | Databricks: N/A
Snowflake: ANSI SQL with advanced optimizations | Databricks: N/A
Snowflake: 90 days automatic | Databricks: N/A
Snowflake: 0.5 engineers | Databricks: N/A
Snowflake: 2,800 stars | Databricks: 8,200 stars (databricks/databricks-cli)
Snowflake: Industry leading (100% baseline) | Databricks: 2-5x speedup with Photon
Snowflake: 5-10 minutes (serverless) | Databricks: 30-45 minutes (cluster setup)
Snowflake: 4/10 (limited native, requires integrations) | Databricks: 9/10 (native MLflow, AutoML, Feature Store)
Snowflake: $2.00-$4.00 per credit | Databricks: $0.40-$1.20 per DBU
Snowflake: 32% enterprise market share (2025) | Databricks: 18% enterprise market share (2025)
Snowflake: Limited (structured tables primary) | Databricks: Excellent (images, videos, text, PDFs)
Snowflake: Native (Secure Shares, production-ready) | Databricks: Partial (Delta Sharing, emerging)
Snowflake: 20-35 minutes (SQL based) | Databricks: 8-15 minutes (Spark optimized)
Snowflake: $280,000-$360,000 | Databricks: N/A
Snowflake: 3-5 seconds | Databricks: N/A
Snowflake: 500-1000+ elastic | Databricks: N/A
Snowflake: 3-5:1 average | Databricks: N/A
Snowflake: Yes (zero-copy) | Databricks: N/A
Snowflake: Per-second (1 credit minimum per query) | Databricks: N/A
Snowflake: $4,000-8,000 | Databricks: N/A
Snowflake: $2-4 per credit (1 credit = compute + storage) | Databricks: N/A
Snowflake: 100% (must ingest into Snowflake) | Databricks: N/A
Snowflake: Slower without ingestion; 5-10x slower on unoptimized data lake queries | Databricks: N/A
Snowflake: 30+ (Tableau, Power BI, Looker, Qlik, Sisense) | Databricks: N/A
Snowflake: 90 days retention | Databricks: N/A
Snowflake: No (requires data copy into Snowflake) | Databricks: N/A
Snowflake: N/A | Databricks: AWS, Azure, GCP
Snowflake: N/A | Databricks: $0.30 (1 DBU compute)
Snowflake: N/A | Databricks: 12+ model families via AutoML
Snowflake: N/A | Databricks: Unlimited simultaneous editing
Snowflake: N/A | Databricks: 15+ (S3, RDS, Kinesis)
Snowflake: N/A | Databricks: Native Delta Lake engine
Snowflake: N/A | Databricks: Up to 70% savings
SQL Query Performance (sample 1TB table)
Snowflake: N/A | Databricks: 8-15 (native optimizations)
Snowflake: N/A | Databricks: $2,000-$15,000/month
Snowflake: N/A | Databricks: Minimal (<5 hours/month)
Snowflake: N/A | Databricks: Unlimited with real-time sync
Snowflake: N/A | Databricks: 6+ (SSO, RBAC, audit logging, IP controls, encryption, workspace isolation)
Snowflake: N/A | Databricks: All Spark formats + native Delta Lake optimization
Snowflake: N/A | Databricks: 8,000+ questions
Snowflake: N/A | Databricks: ANSI SQL with Spark extensions
Snowflake: N/A | Databricks: 15+ native connectors
Snowflake: N/A | Databricks: MLflow, Feature Store, AutoML included
Snowflake: N/A | Databricks: Yes, into Delta Lake
Snowflake: N/A | Databricks: 1-5 seconds (on cached data)
Snowflake: N/A | Databricks: 10x (Photon engine)
Snowflake: N/A | Databricks: 25+ integrated
BigQuery/Equivalent Query Speed (1TB dataset)
Snowflake: N/A | Databricks: 15-30 sec (via Databricks SQL)
Snowflake: N/A | Databricks: MLflow + Databricks Intelligence Engine (built-in)
Snowflake: N/A | Databricks: Native Delta Lake with ACID, time travel, schema evolution
Snowflake: N/A | Databricks: Native MLflow Feature Store included
Data Governance (Unity Catalog equivalent)
Snowflake: N/A | Databricks: Unity Catalog with lineage, tags, access control
Snowflake: N/A | Databricks: 5-15 seconds
Snowflake: N/A | Databricks: Structured, semi-structured, unstructured
Snowflake: N/A | Databricks: 3 (AWS, Azure, GCP)
Snowflake: N/A | Databricks: Manual cluster setup and scaling
Snowflake: N/A | Databricks: MLflow, Spark MLlib, TensorFlow, PyTorch
Minimum Learning Curve (months for competency)
Starting Monthly Cost (1 TB storage + compute)
Snowflake: N/A | Databricks: $400-800 (variable compute)
Snowflake: N/A | Databricks: 10-100x faster (Photon engine)
Snowflake: N/A | Databricks: ~15 core data/AI services
Snowflake: N/A | Databricks: $0.50-$2.50 (depends on cloud provider)
Snowflake: N/A | Databricks: MLflow, Feature Store, Model Registry
Snowflake: N/A | Databricks: Limited (Power BI via connector only)
Snowflake: N/A | Databricks: Unity Catalog, lineage, access control, Delta Lake
Snowflake: N/A | Databricks: 3-6 months (focused data/AI projects)
Snowflake: N/A | Databricks: 10-100x faster
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)
Required Team Skills (FTE equivalents for operations)
Snowflake: N/A | Databricks: 0.25 (minimal management)
Snowflake: N/A | Databricks: Full ANSI SQL (100%)
Query Latency (median, standard ETL workload)
Snowflake: N/A | Databricks: 3.5-8 seconds
Built-in Collaboration Tools (notebooks, dashboards, repos)
Snowflake: N/A | Databricks: Notebooks, Dashboards, SQL Editor, Repos, MLflow
Snowflake: N/A | Databricks: 285 seconds
Snowflake: N/A | Databricks: 3-5x faster (Photon engine)
Snowflake: N/A | Databricks: 8 frameworks (via MLflow ecosystem)
Snowflake: N/A | Databricks: Column, row, and table-level with tags
Snowflake: N/A | Databricks: 60+ (via partner clouds)
Snowflake: N/A | Databricks: 50+ (data/AI focused)
Snowflake: N/A | Databricks: $3,500-$5,000
Snowflake: N/A | Databricks: Native (Delta Lake)
Snowflake: N/A | Databricks: 0.25 weeks (15 minutes)
Snowflake: N/A | Databricks: 50-100x faster (Spark + Photon)
Snowflake: N/A | Databricks: 0.5-3 seconds (Photon)
Annual Cost (100TB/year, 5-node baseline)
Snowflake: N/A | Databricks: $120,000-$180,000
Snowflake: N/A | Databricks: Yes (Unity Catalog native)
Snowflake: N/A | Databricks: Automatic (5-30 min provisioning)
Collaborative Notebooks with Version Control
Snowflake: N/A | Databricks: Yes (built-in with Git integration)
Snowflake: N/A | Databricks: No (cloud-only)
Snowflake: N/A | Databricks: 8-12 seconds
Snowflake: N/A | Databricks: 30-60 minutes (cluster creation)
Snowflake: N/A | Databricks: $2,400-3,600 (on-demand, m5.large cluster)
Snowflake: N/A | Databricks: $0.03-0.05/GB ($30-50/TB)
Snowflake: N/A | Databricks: SQL, Python, Scala, R, Java (full support)
Snowflake: N/A | Databricks: Native MLflow, Feature Store, AutoML, Model Registry
Snowflake: N/A | Databricks: 24 hours (configurable)
Snowflake: N/A | Databricks: $600 (for 2 DBUs minimum)
Snowflake: N/A | Databricks: 15 integrated services
Snowflake: N/A | Databricks: 10-100x faster with Photon engine
Snowflake: N/A | Databricks: Not primary strength
Snowflake: N/A | Databricks: 3 clouds (AWS, Azure, GCP)
Snowflake: N/A | Databricks: 10+ (via Mosaic AI and MLflow)
Snowflake: N/A | Databricks: 10 GB compute hours free
Snowflake: N/A | Databricks: Data discovery, lineage, access control, PII detection
How to choose between Snowflake and Databricks
Snowflake vs Databricks — Structured Comparison Data
Machine-readable comparison dataset for Snowflake vs Databricks. Contains 216 attribute comparisons, 5 FAQ pairs.
Snowflake vs Databricks — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Snowflake vs Databricks.
Which platform is cheaper for SQL analytics workloads?
Snowflake is typically cheaper for pure SQL analytics and BI use cases, with starting costs around $120-240/month. Databricks starts at $500+/month but becomes cost-competitive when combining analytics with ML/AI workloads, as you avoid paying for multiple separate platforms.
Can I use Snowflake for machine learning?
Snowflake has limited native ML capabilities. You can use Snowflake ML (Python UDFs, basic model training) for simple tasks, but for production ML pipelines, you'll typically integrate external tools like SageMaker, Vertex AI, or Databricks ML, adding complexity and cost.
What is the lakehouse architecture and why does Databricks use it?
A lakehouse combines data lake flexibility (any data format, structure) with warehouse performance (SQL querying, ACID transactions). Databricks uses Delta Lake to achieve this, allowing raw data ingestion without ETL overhead while maintaining data quality—ideal for organizations handling diverse data types.
Which platform has better SQL query performance?
Snowflake has optimized SQL query performance (typically 2-5 seconds for standard queries on 1TB datasets) due to its columnar architecture and query optimizer. Databricks SQL is competitive but slightly slower (8-15 seconds typical) because it uses distributed Spark processing, which has more overhead for pure SQL workloads.
Is Databricks harder to learn than Snowflake?
Yes, Databricks has a steeper learning curve. Snowflake is designed for traditional SQL users and requires minimal distributed computing knowledge. Databricks requires understanding of Apache Spark, distributed processing, and cluster management, making it better for data engineers and ML practitioners but more complex for BI analysts.
How to Choose Between Snowflake and Databricks
Core Architecture: Snowflake has Cloud data warehouse (columnar storage), while Databricks has Lakehouse (Delta Lake on object storage).
ML/AI Capabilities: Snowflake has Limited (requires external tools), while Databricks has Native (Databricks ML, MLflow built-in).
Query Engine: Snowflake has Proprietary SQL engine, while Databricks has Apache Spark (distributed processing).
Starting Price (monthly): Snowflake has $2-4 per credit (~$120-240 minimum), while Databricks has $0.30-0.55 per DBU (~$500+ minimum).
SQL Analytics Performance: Snowflake has Superior (optimized for BI/analytics), while Databricks has Competitive (good for SQL, better for mixed workloads).
Data Format Flexibility: Snowflake has Structured data (requires ingestion), while Databricks has Any format (raw data, structured, unstructured).
Time-to-Insight for Analytics: Snowflake has Faster (pre-optimized queries, ~2-5 seconds), while Databricks has Moderate (Spark optimization, ~5-15 seconds typical).