Databricks 対 Snowflake

Databricks is a unified, open platform for data, analytics, and AI agents; Snowflake makes you assemble those capabilities on a proprietary foundation. Databricks runs on open standards, so the same governed data serves analytics, BI, and AI agents. Snowflake layers the same capabilities onto a foundation that stays proprietary where it counts, and governs only the agents Snowflake itself ships.

The lakehouse argument is over. Open table formats won, and Snowflake's adoption of Apache Iceberg™ concedes it. The question that decides your next five years is no longer "warehouse or lakehouse." It is what you can build on top, and how open the foundation underneath really is.

In short:

• Open foundation, not lock-in. Unity Catalog is a fully open Apache Iceberg™ catalog that lets any engine read your data in place, with no copies.

• Lower, more predictable cost at scale. The price-performance advantage widens as concurrency and data volume grow. In 2025 ETL benchmarking after Snowflake's Gen2 launch, Databricks ran 2.8x faster at about 3.4x better price-performance.

• Proven AI/ML, recognized by analysts. A Leader in the 2025 Gartner Magic Quadrant for Data Science and Machine Learning, top of the Lakehouse use case in the 2025 Gartner Magic Quadrant for Cloud DBMS, a Leader in the 2024 Forrester Wave for Data Lakehouses, and a Leader in the 2025 IDC MarketScapes for Unified AI Governance and Data Platform Software.

• Built for what comes next. Genie turns natural language into governed insight, and Lakebase plus Unity AI Gateway make Databricks the platform agents are built and governed on, not just queried from.

Across decision-making dimensions, Databricks leads on openness, cost at scale, AI/ML maturity, OLTP capabilities, and agent governance. The table below summarizes each, with every claim linked to a public source.

Databricks keeps your data in fully open Apache Iceberg™ that any engine can read in place; Snowflake's openness is narrower, because its native format tables can be queried only by Snowflake's own engine. Both vendors support Iceberg. The difference is how far that openness actually reaches.

Unity Catalog is a fully open, production-ready Apache Iceberg™ catalog, with Managed Iceberg, Iceberg v3, and Foreign Iceberg generally available. Any engine that speaks Iceberg (Spark, Trino, Flink, Snowflake, DuckDB, pandas) reads your governed data in place, with no copies. It federates the catalogs you already run, including AWS Glue, Google Cloud, Snowflake Horizon, Palantir, Salesforce, and Workday, so it becomes a single pane of glass over your entire data estate.

Openness on Databricks is end to end:

• Connectivity. Federated pushdown reaches key external sources, including MySQL, Redshift, and SQL Server, so you can query and govern data wherever it lives.
• Data access. You choose the engine and the open format. Your data is not gated behind a proprietary engine.
• Asset sharing. Delta Sharing distributes data and AI assets across regions, clouds, and platforms, including to Snowflake, Trino, Flink, and Apache Spark™, with no copies and no proprietary client.

Snowflake's openness is narrower than the messaging suggests. Its native, non-Iceberg tables can be queried only by Snowflake's own engine. 

Yes. On small BI queries the two platforms are close, but in 2025 TPC-DI ETL benchmarking after Snowflake's Gen2 launch, Databricks SQL Serverless ran roughly 2.8x faster at about 3.4x better price-performance, and the advantage widens as concurrency and data volume grow.

• In Databricks engineering benchmarking run after Snowflake launched its Generation 2 warehouses, Databricks SQL Serverless completed the TPC-DI ETL benchmark roughly 2.8x faster than Snowflake Gen2, at about 3.4x better price-performance.
• Unity Catalog managed tables accelerate queries up to 20x and cut cost by more than 50%, with Predictive Optimization adapting data layout to workload patterns automatically. ETL is typically half or more of total data spend.
• Migration outcomes track the same pattern: GetYourGuide cut operational costs about 20% moving from Snowflake to Databricks for its unified data warehouse.

Snowflake Gen 2 whilst faster increases cost by up to 35% for I/O bound workloads. Snowflake has introduced considerable complexity forcing users to decide between warehouse generations for each and every workload.

Databricks. It is a Leader in the 2025 Gartner Magic Quadrant for Data Science and Machine Learning, positioned highest in Ability to Execute and furthest in Completeness of Vision, with thousands of enterprises running AI/ML in production on one architecture.

• Gartner Magic Quadrant for Data Science and Machine Learning Platforms, 2025. Databricks is a Leader for the fourth consecutive year, positioned highest in Ability to Execute and furthest in Completeness of Vision. Snowflake entered the report for the first time in 2025 with its MLOps capabilities slow to arrive.
• Gartner Magic Quadrant for Cloud DBMS, 2025. Databricks is a Leader for the fifth consecutive year, recognized for Completeness of Vision and velocity of innovation, and scored top of the Lakehouse use case in Gartner's Critical Capabilities. With Databricks ahead on both execution and completeness of vision.
• Forrester Wave: Data Lakehouses, Q2 2024 (the most recent edition). Databricks is a Leader with the highest scores in both Current Offering and Strategy.
• IDC MarketScape: Worldwide Unified AI Governance Platforms 2025-2026 (December 2025). Databricks is a Leader with the highest Strategies score of all vendors, for governing data, ML, generative AI, and agents through Unity Catalog in one framework.
• IDC MarketScape: Worldwide Data Platform Software 2025 (July 2025). Databricks is a Leader across storage, governance, data engineering, analytics, and AI workloads on one platform.

The architectural reason is straightforward. Databricks was built for data science, ML, and generative AI on one unified platform. On Snowflake, these capabilities were added to the warehouse over time, much of it through acquisition, which is the pattern below.

Databricks repeatedly defines a data-platform category, and Snowflake assembles a version of it later, usually through acquisition and usually bolted to its SQL warehouse. This "follower's roadmap" pattern is built on a closed foundation, and it shows up across four categories.

• Distributed processing. Databricks was founded by the original creators of Apache Spark™. Snowflake's answer, Snowpark, pushes work into Snowflake's own engine rather than a true distributed Apache Spark™ runtime. Its Snowpark Connect compatibility layer still does not support core Apache Spark™ APIs including the RDD API, MLlib (pyspark.ml), and Structured Streaming.
• Declarative pipelines. Databricks built declarative ETL as Delta Live Tables and in June 2025 donated it to Apache Spark™ as Apache Spark™ Declarative Pipelines, an open standard any Apache Spark™ platform can run. Snowflake's equivalent, Dynamic Tables, is a narrower, proprietary alternative.
• Ingestion. Rather than build streaming-grade ingestion natively, Snowflake acquired Datavolo in late 2024 and launched Openflow on top of Apache NiFi, a project that dates to 2006.
• Serverless Postgres for agents. Databricks acquired Neon on May 14, 2025: a serverless Postgres where more than 80% of databases are created by AI agents rather than people, and a fresh instance starts in under 500 milliseconds with instant branching. Days later, on June 2, Snowflake acquired Crunchy Data. Snowflake Postgres is basically an extension to Iceberg data; nothing more, it  lacks the instant-branching, dev-and-test model that agents rely on.

The pattern matters because the foundation underneath these additions stays closed. Snowflake's native data requires its own engine to query, sharing is largely confined to the Snowflake ecosystem, and agent governance covers only Snowflake's own agents. In the age of agentic disruption, a closed platform is a standing risk. An open foundation is what lets you take advantage of the latest and greatest development, and it is the strategic bet that Databricks has made from the start.

Databricks is the platform where agents are built, iterated, and governed on, not just queried from: Lakebase gives agents serverless Postgres with instant branching, and Unity AI Gateway governs internal and external agents, while Snowflake governs only its own agents. Querying data with an agent is the easy part. Building, iterating, and governing agents in production is where platforms separate.

• Lakebase, built on Neon, is serverless Postgres designed for agents. A fresh instance starts in under 500 milliseconds, scales to zero, and supports instant branching, so an agent or developer can spin up an isolated copy for every test. It autosyncs between Delta and Postgres and into vector search, so operational and analytical data stay in step. Snowflake's Postgres, built on the Crunchy Data acquisition, targets enterprise Postgres on Kubernetes rather than the instant-branching, dev-and-test model agents iterate on.
• Databricks Apps provides a simple Node and Python framework with OAuth and native resource integration, no API keys to manage. Snowflake app development spans Streamlit, which runs under a restrictive Content Security Policy and runtime constraints, and Snowpark Container Services, which requires provisioning compute pools, image repositories, and roles.
• Unity AI Gateway governs and observes internal and external MCPs, LLM inference calls, and third-party coding agents. Snowflake governs and observes only its own agents and MCPs, so anything outside its perimeter falls outside its controls.

Open model choice. Databricks lets you serve Claude, Llama, GPT-OSS, Gemini, and your own fine-tunes behind a single gateway.

Is Databricks enterprise-ready? Yes. Databricks provides documented multi-region disaster recovery, a platform uptime SLA of 99.9% or higher (99.95% on Azure), and unified governance through Unity Catalog across every engine and cloud. It is a Leader in the 2025 Gartner MQ for DSML and Cloud DBMS, and  2024 Forrester Wave for Data Lakehouses.

Does Databricks have disaster recovery? Yes. Databricks documents active-passive, multi-region disaster recovery, and its control plane is resilient to zone failures, recovering automatically within roughly 15 minutes.

Is Unity Catalog open source and based on open standards? Unity Catalog is a fully open Apache Iceberg™ catalog with open REST APIs, so any Iceberg-compatible engine (Spark, Trino, Flink, Snowflake, DuckDB, pandas) reads your data without copies. It also federates external catalogs including Glue, Snowflake Horizon, Palantir, Salesforce, and Workday.

Is my data locked into Databricks? No. Your data lives in open Iceberg  or Delta in your own storage, readable by any engine. On Snowflake, customers are forced to choose between Snowflake proprietary, native format and Iceberg. Customers need to consider performance implications and unsupported features.

Is Databricks more expensive than Snowflake? No. On small BI queries the two are close, but at large-scale ETL and as concurrency and data volume grow, Databricks pulls ahead on both speed and cost. In 2025 benchmarking against Snowflake's latest-generation warehouses, Databricks ran roughly 2.8x faster at about 3.4x better price-performance. Snowflake Gen 2 whilst faster increases cost by up to 35% for I/O bound workloads.

Is Snowflake good for AI and machine learning? Snowflake added AI/ML to its warehouse and entered the Gartner DSML Magic Quadrant for the first time in 2025. Snowflake MLOps and AI availability limitations. Databricks has run production AI/ML for thousands of enterprises on one platform and is the Leader in that quadrant.

How does Databricks handle AI agents differently from Snowflake? Databricks governs internal and external agents and MCPs through Unity AI Gateway and lets agents build and iterate on Lakebase, serverless Postgres with scale-to-zero and instant branching. Snowflake governs only its own agents, and its Postgres offering targets standard deployments rather than the instant-branching model agents iterate on.

Can I use my own AI models? Yes. Databricks supports open model choice (Claude, Llama, GPT-OSS, Gemini, and fine-tunes) behind one gateway, instead of a single-vendor model bet.