Snowflake vs Databricks vs BigQuery

For pure SQL warehousing on structured data, Snowflake is typically 15 to 25 percent cheaper than Databricks SQL Warehouse and 5 to 15 percent more expensive than BigQuery on equivalent workloads. For lakehouse workloads mixing ETL, ML, and SQL, Databricks is typically 20 to 35 percent cheaper than the equivalent Snowflake plus SageMaker or Snowflake plus Vertex AI stack. For Google-native analytics on click-stream and ad data, BigQuery is the only economically rational choice. The platform choice is a workload choice, not a price choice. This guide compares the three platforms on the eight criteria that decide post-deployment outcomes.

The three platforms in one paragraph each

Snowflake is a SQL-first cloud data warehouse with strong governance, Marketplace, and Data Sharing capabilities. Snowflake separates compute from storage, scales compute via warehouses, and charges per credit consumed. Snowflake runs on AWS, Azure, and GCP and is the de-facto cross-cloud option.

Databricks is a lakehouse platform with SQL, ETL, ML, and AI workloads on the same compute layer. Databricks is strongest on data engineering, ML, and AI workloads. Databricks separates compute from storage and charges per DBU consumed. Databricks runs on AWS, Azure, and GCP.

BigQuery is Google Cloud's serverless data warehouse. BigQuery has no compute clusters to manage and charges per query bytes processed (on-demand) or per slot consumed (capacity reservation). BigQuery is GCP-only and is the strongest fit for Google-native workloads (Google Ads data, Google Analytics 4, YouTube data, Chrome telemetry).

Pricing comparison at scale

The price comparison below uses a 100-TB lakehouse running a typical analytics mix: 40 percent ETL, 30 percent SQL BI, 20 percent ML feature engineering, 10 percent ad-hoc data science. Numbers are list before negotiated discount.

The three-year TCO column reflects a 25 percent commit discount on Snowflake, a 28 percent commit discount on Databricks, and a 30 percent commit discount on BigQuery, applied to the relevant mid-range of each row. Realised discount in any individual deal varies materially.

Workload fit by platform

Data sharing and Marketplace

Snowflake Marketplace is the most mature data exchange. Over 2,500 listings, native data sharing without copy, and Native Apps that install third-party software into the customer's Snowflake account. Snowflake Data Sharing remains the easiest cross-organisation data exchange pattern.

Databricks Marketplace and Delta Sharing have closed much of the gap. Delta Sharing is an open protocol, which means Databricks data can be shared with non-Databricks consumers. The Marketplace has 1,000-plus listings but is younger and less populated than Snowflake's.

BigQuery Analytics Hub provides data sharing within Google Cloud. Cross-cloud data sharing is weaker. For organisations whose data partners use AWS or Azure, BigQuery Analytics Hub is a friction point.

AI and ML capability

Databricks Mosaic AI is the most integrated AI stack of the three. Foundation Model APIs, fine-tuning, RAG with Vector Search, Mosaic Pretraining, and Model Serving with provisioned throughput, all on the same platform with shared governance via Unity Catalog. For greenfield enterprise AI builds, Databricks is the strongest fit.

Snowflake Cortex is the easiest GenAI for SQL teams. SQL functions invoke LLMs and Document AI inline. Cortex Analyst answers natural-language questions about Snowflake data. Cortex Search delivers RAG without external infrastructure. For brownfield Snowflake estates, Cortex is the lowest-friction path to GenAI.

BigQuery integrates with Vertex AI for generative AI. The integration is improving but is less integrated than Databricks Mosaic or Snowflake Cortex. For Google-native shops with Vertex AI investment already in place, BigQuery is competitive.

Exit cost and portability

Exit cost differs materially across the three platforms.

Snowflake exit: data is stored in Snowflake-managed micro-partitions on object storage. Exit requires unloading to flat files (Parquet or CSV) at $0.02 to $0.09 per GB egress. For a 100-TB warehouse, egress alone is $2,000 to $9,000. Compute migration and SQL refactor are the larger cost: typically 6 to 18 months and $400,000 to $1.5 million for an enterprise estate.

Databricks exit: data already lives in customer-owned object storage (S3, ADLS, GCS) in open formats (Parquet, Delta). Exit means abandoning the Databricks compute, governance, and tooling layer but the data is portable. Cost: 3 to 12 months and $200,000 to $800,000, mostly governance and tooling re-implementation.

BigQuery exit: data is stored in BigQuery's proprietary columnar format. Exit requires export to Cloud Storage at $0.10 per GB plus standard egress fees. For a 100-TB warehouse, export alone is $10,000-plus and egress out of Google Cloud is $0.08 to $0.12 per GB. Tooling migration is comparable to Snowflake at 6 to 18 months and $400,000 to $1.5 million.

The decision framework

Apply the framework below. Answer the five questions in order. The first question with a clear answer drives the platform choice.

Question 1: Do you have a contractual or regulatory requirement for multi-cloud or cloud-agnostic deployment? If yes, choose Snowflake. Databricks is multi-cloud but commercial portability is weaker. BigQuery is single-cloud.

Question 2: Is the dominant workload Google Ads, GA4, YouTube, or Chrome telemetry data? If yes, choose BigQuery. The data is already in BigQuery via Google's first-party integrations.

Question 3: Is the dominant workload ML training, GenAI on internal data, or data engineering on streaming sources? If yes, choose Databricks.

Question 4: Is the dominant workload SQL BI on structured data, with data sharing across organisational boundaries as a key use case? If yes, choose Snowflake.

Question 5: Mixed workload with no single dominant pattern? Default to Databricks for greenfield (best AI roadmap) or stay with the incumbent if already deployed.

For platform-specific pricing detail, see our Snowflake pricing pillar and Databricks pricing pillar. For the underlying cloud commit interaction, see AWS EDP negotiation, Azure MACC versus CTP, and Google Cloud Enterprise Agreement. For AI workload economics across platforms, see our enterprise LLM cost comparison and AI vendor selection framework. For broader cost controls, see our cloud cost optimization guide. To engage on platform selection, see our cloud contract negotiation service.

Governance and observability

Snowflake governance centres on the Information Schema, the Access History, and the Tagging system. Object-level tagging enables policy-driven masking and row access. Access History records every query against every object with retention up to 365 days on Business Critical edition. Snowflake's governance is mature, predictable, and well-integrated with third-party catalogue tools like Atlan and Collibra.

Databricks governance centres on Unity Catalog. Unity Catalog covers tables, files, ML models, AI tools, and now AI agents. Lineage is captured automatically across SQL, Python, and ML pipelines. Audit logs flow to the cloud-native audit store. For organisations standardising on a single lakehouse, Unity Catalog is the most complete governance plane available today.

BigQuery governance flows through Dataplex, IAM, and BigQuery row-level and column-level security. Dataplex unifies metadata, lineage, and policy across BigQuery, Cloud Storage, and Pub/Sub. The integration is tight inside Google Cloud and weaker for any data outside it.

Time to deployment

For greenfield enterprise deployments, time to first production workload runs as follows. Snowflake: 4 to 8 weeks for SQL BI, 8 to 14 weeks with full governance and Marketplace integration. Databricks: 6 to 12 weeks for SQL plus ETL, 12 to 20 weeks with Unity Catalog and ML workloads. BigQuery: 3 to 6 weeks for SQL on already-resident Google Cloud data, 10 to 16 weeks for cross-source enterprise analytics.

For brownfield migrations from legacy on-premise warehouses (Teradata, Netezza, Vertica, Exadata) or legacy Hadoop estates, timelines extend by 6 to 18 months and migration cost typically runs 30 to 60 percent of three-year platform TCO. The migration cost rarely belongs on the platform-cost slide alone; it belongs on the case-for-change slide.

Contract terms compared