Databricks vs Snowflake
An independent, sourced comparison. Every row links to the vendor's own docs and carries a verified date.
By brickster.ai · updated Jun 22, 2026 · feature data verified Jun 21, 2026
The short answer
Pick Databricks if your center of gravity is data engineering, machine learning, or AI agents on open formats and you're comfortable managing compute. Pick Snowflake if you mostly run SQL and BI and want fewer knobs to turn. The core difference: Databricks is an open lakehouse you run in your own cloud account, Snowflake is a managed SaaS warehouse with a proprietary core that's opening up to Apache Iceberg.
Databricks and Snowflake both let you store data, run SQL, build pipelines, and train models on one platform, but they got there from opposite directions. Databricks started as a managed Apache Spark service and grew into the Data Intelligence Platform, a lakehouse built on open table formats (Delta Lake and, now, managed Apache Iceberg). It runs compute in your own AWS, Azure, or GCP account and bills by the DBU. It tends to win where the work is data engineering, machine learning, and AI.
Snowflake started as a cloud data warehouse and is now the AI Data Cloud. Its core is a proprietary vectorized SQL engine over proprietary micro-partition storage (FDN), delivered as pure SaaS with almost no infrastructure to manage. It bills by the credit. Snowflake is opening that closed core to Iceberg through native Iceberg tables and the Polaris-based Open Catalog. The practical choice is usually open and flexible with more knobs versus simple and lower-ops.
Choose Databricks if
- Your team writes Spark, Python, or Scala and does heavy ETL, not just SQL, and you want Lakeflow Declarative Pipelines, Auto Loader, and Structured Streaming with Real-Time Mode in one place.
- Machine learning and generative AI are central. You want Mosaic AI (Model Serving, Vector Search, Agent Framework with a managed MCP server), MLflow, a feature store, AutoML, and GPU compute natively.
- You want data in open formats you control. Delta Lake and managed Iceberg (read and write, GA) with Unity Catalog as an open-source Iceberg REST catalog, so other engines can read your tables.
- You're fine running compute in your own cloud account and tuning clusters, instance types, and Photon to trade cost against speed.
- You want one platform spanning engineering, BI (AI/BI Dashboards plus Genie for natural-language SQL), and AI, rather than bolting ML onto a warehouse.
Choose Snowflake if
- Most of your workload is SQL analytics and BI, and you want near-zero infrastructure management. Warehouses auto-suspend and auto-resume, and you size them with a t-shirt label instead of picking instances.
- You value predictability and low operational overhead more than fine-grained control. A SQL team can be productive in a day without learning Spark.
- You want SQL-first AI without standing up ML infrastructure. Cortex Analyst (natural-language SQL), Cortex Search, the native VECTOR type, and Cortex Agents with a managed MCP server are built in.
- Data sharing and a large marketplace matter. Secure Data Sharing, native Clean Rooms, and a Marketplace with 3,400-plus listings are mature and widely used.
- You want strong, simple governance out of the box with Horizon Catalog, tag-based masking, row access policies, and automatic lineage and classification.
Databricks vs Snowflake, measure by measure
Every cell links to the vendor's own product, pricing, or docs page and shows when it was last verified. It quotes them, it doesn't score a winner.
| Measure | Databricks Lakehouse (Spark + Photon) | Snowflake Cloud data warehouse |
|---|---|---|
| Architecture & openness | ||
| ArchitecturePlatform shape | Data Intelligence Platform (lakehouse) source · verified 2026-06-21 | Cloud data warehouse / AI Data Cloud source · verified 2026-06-21 |
| Compute engineUnderlying query engine | Apache Spark + Photon source · verified 2026-06-21 | Proprietary vectorized SQL engine source · verified 2026-06-21 |
| Storage / compute separationIndependent scaling | Decoupled storage and compute source · verified 2026-06-21 | Fully decoupled storage and compute source · verified 2026-06-21 |
| Native table formatDelta / Iceberg / proprietary | Delta Lake (and managed Iceberg) source · verified 2026-06-21 | Proprietary micro-partitions (FDN) source · verified 2026-06-21 |
| Apache IcebergRead + write support | Native managed Iceberg, read+write GA source · verified 2026-06-21 | Native Iceberg, read+write source · verified 2026-06-21 |
| Delta LakeRead / write Delta tables | Native Delta read/write source · verified 2026-06-21 | Read via external/Iceberg, no native write source · verified 2026-06-21 |
| Open / REST catalogIceberg REST / open catalog | Unity Catalog Iceberg REST catalog source · verified 2026-06-21 | Open Catalog (Apache Polaris, Iceberg REST) source · verified 2026-06-21 |
| Open-source coreEngine / format open source | Spark, Delta, Unity Catalog open source source · verified 2026-06-21 | Engine closed; Polaris/Arctic open source · verified 2026-06-21 |
| Multi-cloudAWS / Azure / GCP | AWS, Azure, GCP source · verified 2026-06-21 | AWS, Azure, GCP source · verified 2026-06-21 |
| Deployment modelSaaS vs your cloud account | Runs in your cloud account source · verified 2026-06-21 | SaaS-only managed service source · verified 2026-06-21 |
| Cost & pricing | ||
| Billing unit | Per-DBU source · verified 2026-06-21 | Per-credit consumption source · verified 2026-06-21 |
| Billing granularityPer-second / minute / hour | Per-second source · verified 2026-06-21 | Per-second, 60-second minimum source · verified 2026-06-21 |
| Scale-to-zero serverlessAuto-suspend | Serverless SQL/compute, auto-suspend source · verified 2026-06-21 | Auto-suspend warehouses + serverless compute source · verified 2026-06-21 |
| Separate infra billCompute billed apart from VM / storage | Classic: separate VM bill; serverless bundled source · verified 2026-06-21 | Bundled; no separate cloud VM bill source · verified 2026-06-21 |
| Storage pricing$ / TB-month | No Databricks storage charge; cloud bills it source · verified 2026-06-21 | ~$23/TB-month on-demand (AWS US East) source · verified 2026-06-21 |
| Free tier / trial | Free Edition + 14-day trial source · verified 2026-06-21 | 30-day free trial with credits source · verified 2026-06-21 |
| Committed-use discounts | Committed-use contracts source · verified 2026-06-21 | Pre-paid capacity discounts source · verified 2026-06-21 |
| Cost observabilityUsage / cost monitoring | System tables, usage dashboards, budgets source · verified 2026-06-21 | Cost views, budgets, resource monitors source · verified 2026-06-21 |
| Pricing transparencyPublished vs custom-quote | List DBU prices published source · verified 2026-06-21 | List credit/storage prices published source · verified 2026-06-21 |
| SQL & query | ||
| ANSI SQL coverageWindow, recursive CTE | ANSI SQL incl. window, recursive CTE source · verified 2026-06-21 | Broad ANSI SQL, window + recursive CTE source · verified 2026-06-21 |
| Semi-structured dataJSON / VARIANT | Native VARIANT and JSON support source · verified 2026-06-21 | Native VARIANT/JSON/Avro/Parquet source · verified 2026-06-21 |
| GeospatialGeo types + functions | Spatial SQL GA, GEOMETRY/GEOGRAPHY, H3 source · verified 2026-06-21 | GEOGRAPHY + GEOMETRY types and functions source · verified 2026-06-21 |
| User-defined functionsSQL / Python / Java | SQL, Python, Scala, Java UDFs source · verified 2026-06-21 | SQL, Python, Java, Scala UDFs source · verified 2026-06-21 |
| Materialized views | Native materialized views source · verified 2026-06-21 | Native materialized views source · verified 2026-06-21 |
| Query result caching | Query result caching source · verified 2026-06-21 | Persisted query result cache source · verified 2026-06-21 |
| Query federationQuery external sources in place | Lakehouse Federation source · verified 2026-06-21 | External tables; limited live federation source · verified 2026-06-21 |
| Data engineering | ||
| Batch ETL / ELT toolingNative pipeline tooling | Lakeflow Declarative Pipelines, Jobs source · verified 2026-06-21 | Snowpark, Streams, Tasks, Openflow source · verified 2026-06-21 |
| Streaming ingestion | Structured Streaming, Real-Time Mode source · verified 2026-06-21 | Snowpipe Streaming, up to 10 GB/s source · verified 2026-06-21 |
| Change data capture | CDC via APPLY CHANGES, Lakeflow Connect source · verified 2026-06-21 | Streams; Openflow CDC connectors source · verified 2026-06-21 |
| Auto file ingestionAuto Loader / Snowpipe class | Auto Loader source · verified 2026-06-21 | Snowpipe auto-ingest source · verified 2026-06-21 |
| Native orchestrationJobs / scheduler | Lakeflow Jobs source · verified 2026-06-21 | Native Tasks scheduler / DAGs source · verified 2026-06-21 |
| dbt support | First-class dbt adapter and task source · verified 2026-06-21 | First-class dbt adapter source · verified 2026-06-21 |
| Declarative pipelinesDLT / Lakeflow-style | Lakeflow Declarative Pipelines source · verified 2026-06-21 | Dynamic Tables declarative pipelines source · verified 2026-06-21 |
| ML & AI | ||
| Model trainingNative, on-platform | Native training on Spark/GPU clusters source · verified 2026-06-21 | Snowflake ML native training (CPU/GPU) source · verified 2026-06-21 |
| Feature store | Native feature store in Unity Catalog source · verified 2026-06-21 | Native Snowflake Feature Store source · verified 2026-06-21 |
| Experiment trackingMLflow or equivalent | Managed MLflow source · verified 2026-06-21 | Managed MLflow integration source · verified 2026-06-21 |
| Model servingHost / inference | Mosaic AI Model Serving source · verified 2026-06-21 | Model Serving on Container Services source · verified 2026-06-21 |
| AutoML | Partial automation, no full AutoML product source · verified 2026-06-21 | |
| Vector searchEmbeddings index | Mosaic AI Vector Search source · verified 2026-06-21 | Native VECTOR type + Cortex Search source · verified 2026-06-21 |
| Foundation-model gatewayGoverned multi-model access | Mosaic AI Gateway (multi-model) source · verified 2026-06-21 | Cortex AI hosts multiple LLMs source · verified 2026-06-21 |
| Text-to-SQLNL-to-SQL assistant | AI/BI Genie source · verified 2026-06-21 | Cortex Analyst NL-to-SQL source · verified 2026-06-21 |
| Agents / MCPAgent framework + MCP server | Mosaic AI Agent Framework, managed MCP source · verified 2026-06-21 | Cortex Agents + managed MCP server source · verified 2026-06-21 |
| GPU compute | GPU instances for ML source · verified 2026-06-21 | GPU compute pools (Container Services) source · verified 2026-06-21 |
| BI & consumption | ||
| Native dashboards / BI | AI/BI Dashboards source · verified 2026-06-21 | Streamlit apps + dashboards in Snowsight source · verified 2026-06-21 |
| Semantic / metrics layer | Unity Catalog Metric Views source · verified 2026-06-21 | Semantic views source · verified 2026-06-21 |
| Notebooks | Native notebooks source · verified 2026-06-21 | Native Snowflake Notebooks source · verified 2026-06-21 |
| Natural-language BIAsk-your-data | AI/BI Genie natural-language source · verified 2026-06-21 | Cortex Analyst ask-your-data source · verified 2026-06-21 |
| BI tool integrationsTableau / Power BI / Looker | Tableau, Power BI, Looker connectors source · verified 2026-06-21 | Tableau, Power BI, Looker connectors source · verified 2026-06-21 |
| Governance & security | ||
| Unified governance catalogOne catalog across data + AI | Unity Catalog across data and AI source · verified 2026-06-21 | Horizon Catalog across data + AI source · verified 2026-06-21 |
| Fine-grained RBAC | Fine-grained RBAC in Unity Catalog source · verified 2026-06-21 | Role-based access control source · verified 2026-06-21 |
| Attribute-based access controlTag-based policies | ABAC with governed tags, GA source · verified 2026-06-21 | Tag-based masking/policies source · verified 2026-06-21 |
| Column masking | Dynamic column masks source · verified 2026-06-21 | Dynamic data masking source · verified 2026-06-21 |
| Row-level security | Row filters source · verified 2026-06-21 | Row access policies source · verified 2026-06-21 |
| Data lineageAutomatic | Automatic lineage in Unity Catalog source · verified 2026-06-21 | Automatic data lineage (Horizon) source · verified 2026-06-21 |
| Data classificationAuto PII discovery | Automated data classification GA source · verified 2026-06-21 | Automatic sensitive-data classification source · verified 2026-06-21 |
| Audit logging | Audit logs / system tables source · verified 2026-06-21 | Access History / audit logs source · verified 2026-06-21 |
| Customer-managed keysCMK / BYOK | Customer-managed keys source · verified 2026-06-21 | Tri-Secret Secure customer-managed keys source · verified 2026-06-21 |
| Private networkingPrivateLink / VPC | PrivateLink, VNet/VPC injection source · verified 2026-06-21 | PrivateLink / Private Link networking source · verified 2026-06-21 |
| Sharing & collaboration | ||
| Data sharingCross-account / cross-cloud | Delta Sharing (cross-cloud) source · verified 2026-06-21 | Secure cross-cloud data sharing source · verified 2026-06-21 |
| Clean rooms | Clean Rooms GA source · verified 2026-06-21 | Native Data Clean Rooms source · verified 2026-06-21 |
| Marketplace | Databricks Marketplace source · verified 2026-06-21 | Snowflake Marketplace (3,400+ listings) source · verified 2026-06-21 |
| Operations & reliability | ||
| Public status APIMachine-readable uptime | Status page with RSS/email subscribe source · verified 2026-06-21 | Statuspage with JSON status API source · verified 2026-06-21 |
| Published SLA | Published uptime SLA (99.95% serverless) source · verified 2026-06-21 | 99.9% SLA (99.99% target) source · verified 2026-06-21 |
| Auto-scaling | Cluster autoscaling source · verified 2026-06-21 | Multi-cluster elastic auto-scaling source · verified 2026-06-21 |
| Multi-region / DR | DR guidance; not automatic replication source · verified 2026-06-21 | Cross-region replication and failover source · verified 2026-06-21 |
| Workload isolationIsolate ETL vs BI | Separate warehouses/clusters per workload source · verified 2026-06-21 | Separate virtual warehouses per workload source · verified 2026-06-21 |
| Ecosystem & support | ||
| Partner connectors | Lakeflow Connect 100+ sources source · verified 2026-06-21 | Openflow + broad partner connectors source · verified 2026-06-21 |
| Compliance certificationsSOC 2 / HIPAA / FedRAMP / ISO | SOC 2, HIPAA, PCI-DSS, FedRAMP, ISO source · verified 2026-06-21 | SOC 2, HIPAA, PCI, FedRAMP, ISO source · verified 2026-06-21 |
| Global regions | Dozens of regions across AWS/Azure/GCP source · verified 2026-06-21 | Global regions across AWS/Azure/GCP source · verified 2026-06-21 |
| Support tiers | Tiered support plans source · verified 2026-06-21 | Standard, Premier, Business Critical source · verified 2026-06-21 |
Architecture and openness
Databricks is a lakehouse. Compute (Apache Spark plus the Photon engine) runs in your own cloud account against open table formats in your object storage: Delta Lake natively, plus managed Apache Iceberg with read and write now GA. Unity Catalog is open-sourced and exposes an Iceberg REST catalog, so engines like Trino or Flink can read your tables. Snowflake is SaaS only. Its core is a proprietary vectorized SQL engine over proprietary micro-partition storage (FDN), so you don't see or manage the infrastructure. Snowflake is opening up through native Iceberg tables (read and write) and Open Catalog, its managed Apache Polaris Iceberg REST catalog. The practical trade-off: Databricks gives you open formats and direct control of compute, Snowflake gives you a closed but simpler managed core that now interoperates with Iceberg. If avoiding storage lock-in is a hard requirement, Databricks starts open and Snowflake is catching up.
Pricing and cost model
The two bill on different units, so list prices don't compare directly. Databricks charges per DBU (Databricks Unit) by the second, and the rate depends on the workload: Lakeflow Jobs compute is the cheapest, all-purpose interactive compute costs roughly 3 to 4 times more per DBU, and SQL has classic, pro, and serverless rates. On non-serverless compute you also pay your cloud provider separately for the VMs. Snowflake charges per credit by the second with a 60-second minimum, and the credit rate depends on edition (Standard, Enterprise, Business Critical, VPS) and region. Compute infrastructure is bundled into the credit, and storage is billed separately per terabyte. Snowflake warehouses auto-suspend when idle, which limits waste. Real cost depends on workload shape, so model your own usage rather than trusting headline rates.
Data engineering and streaming
Databricks leans toward code-first engineering. You get Lakeflow (Declarative Pipelines, Connect, and Jobs), Auto Loader for incremental file ingestion, and Structured Streaming with a low-latency Real-Time Mode, all on Spark with Python, Scala, or SQL. This suits complex transformations and large-scale or true streaming work. Snowflake leans SQL-first and lower-ops. Dynamic Tables give declarative, incrementally refreshed transformations, Streams plus Tasks handle change capture and scheduling, Snowpipe Streaming handles low-latency ingestion, and Openflow (built on Apache NiFi) handles connector-based movement. Snowpark runs Python, Java, and Scala inside Snowflake for non-SQL logic. The honest split: Databricks is stronger for heavy, custom, or genuinely streaming pipelines and gives you more control, while Snowflake gets simple-to-medium pipelines running faster with less to operate. Teams that live in SQL usually find Snowflake quicker to start.
Machine learning and AI
This is Databricks' clearest edge. Mosaic AI covers Model Serving, Vector Search, a Gateway, and an Agent Framework with a managed MCP server, alongside MLflow, a feature store, AutoML, and GPU compute. The full path from data to training to serving to agents lives on one platform, which is why ML-heavy teams gravitate here. Snowflake has closed much of the gap for common cases. Cortex AI hosts multiple LLMs, Cortex Analyst does natural-language SQL, Cortex Search handles retrieval, there's a native VECTOR type, and Cortex Agents ship with a managed MCP server. Snowflake ML adds training, a feature store, Model Serving on Container Services, native experiment tracking (ML Experiments), and GPU compute pools. For SQL-centric AI and retrieval, Snowflake is often enough. For custom model training, deep MLOps, or building agents, Databricks generally goes further.
Governance and security
Both offer mature governance, and the gap here is small. Databricks centers on Unity Catalog: attribute-based access control (ABAC) is GA, plus column masks, row filters, end-to-end lineage, and automated classification, all spanning data and AI assets. Because Unity Catalog is open-sourced with an Iceberg REST endpoint, governance can reach beyond Databricks compute. Snowflake centers on Horizon Catalog: tag-based masking, row access policies, automatic lineage, and automatic classification, managed through Snowsight. Snowflake's edition model also matters here, since Business Critical adds controls like customer-managed keys and stricter compliance for regulated industries, which factors into your credit rate. Both support fine-grained access, masking, and lineage well enough for most enterprises. The deciding factor is usually which catalog your wider stack standardizes on and whether you need governance to span engines through an open Iceberg catalog.
BI and consumption
Snowflake assumes BI tools like Tableau, Power BI, or Looker connect over SQL, and adds its own layer: Snowsight for querying and dashboards, Streamlit for building data apps inside Snowflake, semantic views, and native notebooks. The SQL-warehouse heritage makes it a natural backend for existing BI stacks. Databricks offers AI/BI Dashboards and Genie, a natural-language-to-SQL experience tied to Unity Catalog metadata, plus Databricks Apps for building interactive apps, and it connects to the same external BI tools. Both ship a conversational, natural-language query layer (Genie on Databricks, Cortex Analyst on Snowflake) and both have spatial and geospatial SQL. If your organization is already standardized on a BI tool, both work as the warehouse behind it. Snowflake is the more conventional, drop-in SQL endpoint, while Databricks pulls BI closer to its catalog and AI features.
Databricks vs Snowflake pricing
Databricks bills per DBU by the second, with the rate set by workload (Lakeflow Jobs, all-purpose, SQL classic/pro/serverless) and tier, and on non-serverless compute you also pay your cloud provider for the VMs. Snowflake bills per credit by the second with a 60-second minimum, with the rate set by edition and region, and infrastructure bundled in. Different units and bundling rules make headline rates hard to compare, so model your own workload.
Databricks
Databricks lists pricing per DBU with per-second granularity and no up-front cost (databricks.com/product/pricing). The page routes exact figures through a calculator and product pages rather than a static table. Based on widely cited mid-2026 FinOps breakdowns of those rates (CloudZero and Flexera), Premium-tier AWS rates run roughly: Lakeflow Jobs compute about $0.15/DBU, all-purpose interactive compute about $0.55/DBU, SQL classic about $0.22/DBU, SQL pro about $0.55/DBU, and SQL serverless about $0.70/DBU (serverless bundles the VM cost, non-serverless does not). Enterprise tier runs higher than Premium (all-purpose about $0.65/DBU). Standard tier has been retired on AWS and GCP and is being phased out on Azure through 2026. Free Edition and a trial with up to $400 in usage are available.
Snowflake
From Snowflake's own Service Consumption Table effective June 10, 2026, on-demand credit prices in AWS US East (N. Virginia) are $2.00 (Standard), $3.00 (Enterprise), $4.00 (Business Critical), and $6.00 (VPS) per credit, billed per second with a 60-second minimum. AI Credits are $2.00 on-demand. On-demand storage is $23.00/TB/month in most US AWS and Azure regions, dropping toward about $13.80/TB at the highest capacity tier. Cloud Services compute is free up to 10% of daily warehouse credits. The trial provides $400 in credits for 30 days. Credit rates rise in non-US regions and fall with pre-paid capacity commitments.
Sources: Databricks pricing, Snowflake Consumption Table, Snowflake pricing, CloudZero (Databricks pricing), Flexera (Databricks pricing guide).
Frequently asked questions
Is Databricks cheaper than Snowflake?
Neither is reliably cheaper. They bill on different units (Databricks per DBU plus your own cloud VM cost on non-serverless compute, Snowflake per credit with infrastructure bundled), so cost depends on your workload. Databricks often wins on large engineering and ML jobs you can tune. Snowflake's auto-suspend warehouses reduce idle waste for spiky SQL. Model your own usage.
What is the main difference between Databricks and Snowflake?
Databricks is an open lakehouse: Spark and Photon compute running in your own cloud account over open formats (Delta Lake and Iceberg), with strong ML and AI. Snowflake is a managed SaaS warehouse with a proprietary SQL engine and storage, simpler to operate, now opening to Iceberg. Open and flexible versus simple and lower-ops.
Is Databricks or Snowflake better for machine learning?
Databricks is generally stronger for machine learning. Mosaic AI (Model Serving, Vector Search, Agent Framework with a managed MCP server), MLflow, a feature store, AutoML, and GPU compute cover the full train-to-serve-to-agent path. Snowflake ML and Cortex AI have closed the gap for SQL-centric AI and retrieval, but Databricks goes further on custom training and MLOps.
Databricks or Snowflake for data engineering?
Databricks suits code-first and streaming-heavy engineering: Lakeflow, Auto Loader, and Structured Streaming on Spark with Python or Scala. Snowflake suits SQL-first, lower-ops pipelines: Dynamic Tables, Streams plus Tasks, Snowpipe Streaming, and Openflow. Choose Databricks for complex or true streaming work, Snowflake to get simpler pipelines running faster.
Can Snowflake do everything Databricks does?
Mostly, but not identically. Snowflake now covers ML, vectors, agents, and Iceberg, areas once unique to Databricks. It remains SaaS-only with a proprietary core, so you get less control over compute and open formats. Databricks runs in your cloud account on open formats with deeper ML and streaming. Functionality overlaps, the architecture and control differ.
Which is easier to use?
Snowflake is usually easier to start for SQL and BI teams. Warehouses auto-suspend and auto-resume, you size compute with a t-shirt label, and there's almost no infrastructure to manage. Databricks offers more control (cluster, instance, and Photon settings) and a code-first model, which is more capable but has a steeper learning curve, especially for non-Spark users.
Do they both support Apache Iceberg?
Yes. Databricks supports managed Iceberg tables with read and write GA, and Unity Catalog is open-sourced with an Iceberg REST catalog. Snowflake supports native Iceberg tables (read and write) and Open Catalog, its managed Apache Polaris Iceberg REST catalog. Both let external engines interoperate over Iceberg, though Databricks also has native Delta Lake and Snowflake retains its proprietary FDN format.
How this comparison works
- Every cell in the table links to the vendor's own documentation and shows when it was last verified. We quote them, we don't run our own benchmarks.
- The feature data is a slow-moving snapshot, re-checked periodically. The open-source momentum and refresh date update daily via our pipeline.
- brickster.ai is independent and not affiliated with Databricks, Snowflake, or any vendor. If something looks wrong, tell us.