Databricks is a specialized Apache Spark-based data and AI platform focused on unified analytics and machine learning workflows, while Google Cloud Platform (GCP) is a comprehensive cloud infrastructure provider offering 200+ services across compute, storage, networking, and analytics. Databricks runs on top of cloud providers including GCP, whereas GCP is the underlying infrastructure.
Databricks vs Google Cloud Platform — Side-by-side comparison on A Versus B
Choose Databricks if you need a dedicated, Spark-optimized platform for data engineering and AI workflows with multi-cloud flexibility and sophisticated data governance. Choose Google Cloud Platform if you need a comprehensive, enterprise-grade infrastructure foundation with broad service coverage, lower starting costs for diverse workloads, and integration with Google's AI services like Vertex AI and BigQuery.
Compare Databricks vs Google Cloud Platform (GCP)
AI and data lakehouse platform built on Apache Spark for unified analytics and ML workflows
Comprehensive cloud platform offering 200+ services for compute, storage, databases, AI, analytics, and enterprise solutions
Databricks vs Google Cloud Platform — Comparison Data
Structured comparison data for Databricks vs Google Cloud Platform with 221 attributes.
SQL Query Performance (sample 1TB table)
BigQuery/Equivalent Query Speed (1TB dataset)
Data Governance (Unity Catalog equivalent)
Minimum Learning Curve (months for competency)
Starting Monthly Cost (1 TB storage + compute)
Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)
Required Team Skills (FTE equivalents for operations)
Query Latency (median, standard ETL workload)
Built-in Collaboration Tools (notebooks, dashboards, repos)
Starting Monthly Cost (10GB active data)
SQL Query Performance (TPC-DS Benchmark)
Maximum Concurrent Queries Per Warehouse
Annual Cost (100TB/year, 5-node baseline)
Collaborative Notebooks with Version Control
Serverless Function Cost (1M executions/month)
Typical Production Compute Cost (2vCPU, 4GB RAM)
Databricks vs Google Cloud Platform — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Databricks vs Google Cloud Platform.
Can Databricks run on Google Cloud Platform?
Yes. Databricks is a SaaS platform that runs on top of cloud infrastructure providers including GCP, AWS, and Azure. You deploy Databricks workspaces on GCP infrastructure, and Databricks manages the Spark cluster orchestration. This gives you Databricks' optimization and features while using GCP's underlying compute and storage.
Which platform is better for large-scale data analytics?
For Spark-native workloads: Databricks wins due to Photon engine (10x faster) and optimized Delta Lake. For general analytics: BigQuery on GCP wins for SQL speed (8-12 sec vs 15-30 sec on Databricks) and lower cost at scale. For mixed workloads (analytics + web apps + APIs): GCP's 200+ services provide better integration.
How do pricing models compare for a small startup?
GCP is significantly cheaper for startups: small workloads cost $50-200/month with pay-per-use pricing and no minimum. Databricks has a $600+/month minimum DBU commitment. However, if your startup uses Spark extensively, Databricks' performance gains may justify higher costs compared to GCP's Dataproc.
Does Google Cloud offer Apache Spark support?
Yes, through Google Cloud Dataproc, which provides managed Hadoop and Spark clusters. However, Dataproc is not as optimized as Databricks; you manage cluster lifecycle, and Spark queries run at native speed without Photon acceleration. GCP's strength is BigQuery for SQL analytics, not Spark optimization.
Which platform better supports machine learning workflows?
Both excel at ML but differently. Databricks integrates MLflow for end-to-end ML lifecycle (experiment tracking, model registry, serving) natively. GCP's Vertex AI provides AutoML, custom training, and generative AI models. Choose Databricks for Spark-based feature engineering + MLflow, or GCP for broader AI services (vision, NLP, large language models via Vertex AI).