Snowflake vs. Databricks
Snowflake: governed, faster, and open. Databricks: not enterprise-ready, less performant and locks you in.
What is the difference between Snowflake and Databricks?
Enterprise AI should run on a foundation you choose and control, not one that can lock you in. Snowflake is built on open formats, an interoperable catalog, and interoperable semantic layer so switching engines, exporting your data, moving between BI tools or using a faster, cheaper compute is never a re-platforming project. That same openness and interoperability carries through the rest of the platform: a 99.99% SLA across every cloud and AI agents with their own verified identity and a complete audit trail logging both the agent and the authorizing human. Databricks asks customers to assemble the equivalent themselves: a proprietary catalog for governance, an uptime commitment that only covers Azure, agent audit trails that record the user or the agent but not both together, and a compute architecture that gets more expensive to leave the longer you depend on it.
These differences reflect fundamentally divergent core philosophies rather than isolated feature gaps. You are evaluating two distinct models: support for open standards versus vendor lock-in, service-level guarantees versus limited SLA coverage. When choosing a platform for the next five years, capability alone is not the deciding factor. The critical considerations are whether the solution avoids vendor lock-in, how rigorously governance is enforced, the overall cost structure, and who assumes ownership when failures occur.
In short
Snowflake gives you greater control over your data and AI. Databricks’ restrictions and capability gaps can limit that freedom.
Open in both directions, not just one. Snowflake reads and writes Iceberg tables managed in external catalogs; Databricks' catalog federation blocks writes to foreign tables it doesn't own. And once you're on Unity Catalog, there's no Databricks-supported way back while no other vendor supports Unity Catalog OSS, and Databricks alone controls the catalog's roadmap on both the commercial and open-source versions.4
Enterprise-ready by default, not bolted on. Snowflake combines native cross-cloud disaster recovery, and immutable backups; Databricks leaves these gaps for customers to fill with additional tooling and operational processes furthering the lock-in.
Faster analytics, cheaper transactions. Snowflake runs SQL analytics 2x faster than Databricks;5 Snowflake Postgres runs transactional workloads 4x faster than Databricks Lakebase at half the cost.6, 7
AI that finishes the job. Snowflake CoCo is built to solve critical problems and execute, not just answer – delivering governed, agentic workflows from start to finish, without leaving the platform your data lives in.
Is Databricks more open and interoperable than Snowflake?
No. Databricks selectively open sources some of its features and tightly controls its own catalog. It also supports certain open source projects, but restricts their capabilities — particularly write access to foreign Iceberg tables. Snowflake offers greater freedom to choose your engines and catalogs without moving your data.
As of Oct. 22, 2026
| Capability | Snowflake | Databricks |
|---|---|---|
| Open Storage | Apache Iceberg™ native – read/write by Spark, Trino, Flink, DuckDB without Snowflake compute | Delta + UniForm; full Iceberg support GA June 2025 but read only access for foreign Iceberg tables (not managed by Unity Catalog) |
| Open Catalog | Horizon is built on Apache Polaris™ – an ASF Top-Level Project, donated by Snowflake in 2024, with contributors from Google, Microsoft, AWS, Confluent, and Dremio. The open source project and the Snowflake-managed service run the same code.3 | Databricks owns the commercial version of Unity Catalog and, as the project’s dominant contributor, Databricks drives the roadmap and the contribution model for the OSS version.11 Unity Catalog OSS sits at Sandbox, the earliest of LF AI & Data's four hosting stages |
| Portable governance | Snowflake supports Apache Iceberg REST Catalog API specification natively. Snowflake Horizon catalog implements Apache Polaris under the covers. Because Snowflake speaks the standard Iceberg REST Catalog API, customers can point the same engines at a different Iceberg REST catalog — Polaris or a custom REST server — without changing how those engines read their metadata. Customers can even self-host Apache Polaris in their own infrastructure via Docker/Kubernetes. | Customers have to copy their data into a Unity Catalog-managed table because Unity Catalog does not write to foreign tables and can only read those tables, leading to data duplication and complexity.9, 10 Customers also have less flexibility because they cannot replace Unity Catalog with an independent Iceberg catalog such as Polaris or AWS Glue as the governing catalog, or use a different managed version of Unity Catalog OSS. |
| Credential Vending | Vends short-lived, scoped credentials on every external table access – no permanent storage keys required | Does not support credential vending for foreign tables; teams must configure long-lived storage credentials manually, widening the security blast radius of any compromised credential |
| Postgres Compatibility | 100% PostgreSQL-compatible with standard extensions (pgvector, PostGIS) – no proprietary middleware; standard Postgres logical replication, open and portable; pg_lake released as Apache 2.0 on GitHub | Lakebase runs on Databricks-owned Neon – extensions are limited to a Databricks-curated catalog |
Can Databricks support business-critical workloads?
Not out of the box. Databricks leaves cross-cloud disaster recovery, differential-privacy and aggregation controls, immutable backups, and threat detection for customers to assemble and operate – all of which Snowflake provides natively.
As of Oct. 22, 2026
| Capability | Snowflake | Databricks |
|---|---|---|
| Platform type | AI Data Cloud – governed data, model choice, agentic AI | Data + AI Lakehouse – built on Apache Spark |
| Uptime SLA | 99.99% for virtual warehouses1 | 99.95% on Azure only; no SLA published for AWS or GCP Lakehouse SQL workloads2 |
| Cross-cloud DR | Yes – three-step cross-cloud DR, data consistency | Cross-region DR within a single cloud; no native cross-cloud failover2 |
| Sensitive data protection | Foundational data security and governance: access control, Tri-Secret Secure encryption; malicious IP and leaked-credential detection, network security, advanced privacy policies (differential privacy, aggregation, projection) | No native differential privacy or aggregation policies; malicious IP and leaked-credential detection are customer configured on audit logs rather than built in |
| AI security and governance | Security extends from data layer to model and agent: per-agent verified identities with individually scoped permissions and a complete audit trail; prompt injection and jailbreak protection; WORM-compatible immutable backups; zero data retention at the model level | Provides role-based access, but agent audit trails do not record both the agent's identity and the authorizing human together. No native immutable or WORM-compatible backups |
Is Databricks faster than Snowflake?
No. Based on third-party benchmark tests, Snowflake delivers faster execution than Databricks across SQL analytics, ETL, interactive queries, transactional workloads and ML training, typically at lower cost. Results depend on workload, configuration, data volume and concurrency – compare execution time and total cost together using each test's published methodology.
As of Oct. 22, 2026
| Benchmark | Snowflake | Databricks |
|---|---|---|
| TPC-DS (SQL analytics) | 2× faster5 | Baseline |
| TPC-DI (ETL) | 30% faster, 9% less expensive6 | Baseline |
| TPC-H, 100 GB (Interactive Warehouse) | 10.7 seconds | 13.2 seconds |
| TPC-H, 1 TB (Interactive Warehouse) | 27.6 seconds | 71 seconds |
| TPC-C OLTP (Postgres) | 4× faster, half the cost6, 7 | Baseline (Lakebase) |
| PyTorch ML training | Up to 2.5× faster, up to 3× less expensive8 | Baseline |
| Distributed XGBoost | Up to 8× better cost per run8 | Baseline |
How does Snowflake compare to Databricks for building AI agents?
Snowflake provides a data-native control plane for enterprise AI, whereas Databricks' agent stack spans multiple components – Agent Bricks, Unity Gateway, and MLflow – that customers configure and integrate themselves. Snowflake integrates governance, data storage, model serving, semantic context, and interoperability into a unified ecosystem which often results in faster speed to outcome.
Cortex AI Gateway extends Snowflake’s control plane to every agent so you can monitor actions, govern connections and optimize AI spend. Where Databricks Unity Gateway and Snowflake Cortex AI Gateway actually differ is that Databricks governs AI traffic, Snowflake governs from where the data lives.
Snowflake provides a shared, governed enterprise context layer – using Cortex Sense and semantic models – to power Cortex Agents, CoWork, BI and deep analytics with consistent metrics and business context. Databricks Genie Ontology only applies to the Databricks Genie products and can only auto-extract from tables, queries, dashboards and utilizes 'Ontorank' authority score vs. a steward-approved authority model with versioning and RBAC built-in.
Snowflake delivers turnkey agentic workflows – including CoWork and CoCo – that execute natively within its governed data perimeter. Databricks' agent stack spans Agent Bricks, Unity Gateway, and MLflow — each capable, but requiring configuration and integration work that Snowflake handles by default.
Is Databricks cheaper than Snowflake?
Not necessarily. Snowflake's credit model includes compute and underlying infrastructure in a single unit, making workload cost straightforward to predict and control. Databricks bills differently depending on whether you use classic or serverless compute – classic separates DBUs from the cloud-infrastructure charge, adding a second line item to reconcile.
As of Oct. 22, 2026
| Pricing Component | Snowflake | Databricks Classic | Databricks Serverless |
|---|---|---|---|
| Compute Billing | Credits | Databricks Units (DBUs) | Service-specific DBUs |
| Underlying Compute Infrastructure | Included in compute credits | Additional cloud-provider charge | Included in service price |
Total cost depends on more than the sticker rate: SKU count, discount structure and how many separate line items land on one bill all affect what you pay. Snowflake prices compute, storage and AI features under one consistent model; Databricks' DBU rates multiply across workload types, and classic compute adds a second, separate cloud-infrastructure bill on top.
Can you use Snowflake and Databricks together?
Yes. Snowflake can connect to supported Iceberg and Delta tables in Unity Catalog without copying the data or requiring a migration first. Catalog-linked databases automate table discovery, reducing manual registration as teams add Snowflake analytics and AI to their existing data.
The interoperability is not equivalent in the other direction. Databricks’ catalog federation makes foreign Iceberg tables read-only, blocking INSERT, UPDATE, DELETE and MERGE. Snowflake supports read/write access to supported externally cataloged Iceberg tables: you can bring Snowflake to your data without first moving those tables into Horizon Catalog.
FAQs
Is Snowflake good for machine learning?
Yes. Snowflake ML supports building, training and deploying machine learning models on the same platform as the governed enterprise data. ML workflows in Snowflake are agentic, enabling teams to create production-ready ML pipelines using natural language from Snowflake CoCo. While Databricks centers their ML stack on Spark, Snowflake ML is built on Ray, which efficiently distributes execution. Our architectural choice to provide a managed version of Ray with built-in optimizations provides significant training and inference speedups at lower cost.
Does Snowflake govern AI agents from other vendors, or only its own?
Snowflake governs access to its data by third-party agents, not just Snowflake-built agents. When an external agent accesses data through Snowflake, applicable access controls, masking policies and auditing govern that access. Customers do not have to replace their agents with Snowflake’s own to retain governed data access, countering Databricks’ claim that Snowflake governs only its own agents.
Can I use any AI model with Snowflake?
Yes. Snowflake offers models from multiple providers and supports deploying compatible custom models. Teams can choose supported models based on quality, speed and cost rather than commit to one provider. In contrast, Databricks restricts model selection within key native AI features. Most notably, Databricks Genie does not allow users to select, change, or tune the underlying LLM powering the agent. While Snowflake enables broad model flexibility across AI agents, analytics, and custom LLM workflows, Databricks forces reliance on fixed, vendor-managed model choices within Genie. For customers that prefer open weight foundation models, Cortex Training offers the ability to easily customize open-weight LLMs on proprietary data that can then be deployed for scalable inference on the same managed GPU infrastructure.
How does Snowflake Sharing compare to Databricks' OpenSharing (formerly Delta Sharing)?
Snowflake Sharing lets supported external clients read shared Iceberg and Delta tables without a Snowflake account or duplicate data copies. Databricks OpenSharing shares Unity Catalog tables externally, but outbound sharing does not eliminate its external-data integration barriers.
Does Snowflake support Apache Spark?
Yes. Snowpark Connect for Apache Spark™ lets supported PySpark workloads run on Snowflake compute using familiar DataFrame APIs. Teams moving from Databricks can retain supported PySpark code rather than rewrite every pipeline, although unsupported APIs and Databricks-specific dependencies may require changes.
What do Snowflake and Databricks have in common?
Snowflake and Databricks are both cloud-native platforms built for analytics, data engineering, transactions, AI, applications, and collaboration on the same underlying cloud infrastructure (AWS, Azure, GCP). Both separate storage from compute, support open table formats – Snowflake with native Iceberg tables, Databricks with Iceberg alongside its default Delta Lake format – and offer data sharing and marketplace capabilities that let customers exchange data across organizations. Where they diverge is architecture: Snowflake unifies catalog, governance and AI into one managed platform, while Databricks assembles equivalent capabilities from a broader set of separate components.
Can Snowflake replace Databricks?
Yes. Snowflake can replace Databricks across analytics, data engineering, transactions, AI, applications and collaboration, subject to workload compatibility. Teams can start with supported data in Unity Catalog without copying it, then migrate workloads incrementally; Snowpark Connect preserves familiar PySpark APIs for supported operations. Databricks’ foreign-table access stops at reading; Snowflake supports read/write access across supported external Iceberg catalogs. That gives customers a path to consolidation without making a catalog migration the price of getting started.
What can Databricks do that Snowflake cannot?
Nothing that matters for running most production data and AI workloads. Most open formats and catalog standards Databricks supports, Snowflake supports too with governance, security and price-performance built in natively rather than added on afterward.
Is Snowflake a data warehouse or a lakehouse?
Do Snowflake-managed Iceberg tables in my own cloud storage get full Snowflake features?
Yes. Snowflake fully manages Iceberg tables in your own bucket, including automatic compaction, Time Travel and UNDROP within your data retention period. External engines such as Apache Spark™ and Trino can read and write the same tables through Horizon Catalog.
Does Snowflake support OLAP and OLTP?
Yes. Snowflake supports both analytical workloads (OLAP) and transactional workloads (OLTP). Snowflake Postgres provides a fully managed PostgreSQL database for transactional applications, while Hybrid Tables support low-latency reads and writes alongside analytical tables within Snowflake.
Does Snowflake have an app development platform?
Yes. Snowflake lets you build and ship secure enterprise apps across three key use cases. First, internal Python-based data apps, for example, data dashboards & visualizations, exploratory analytics, lightweight workflows, with Streamlit in Snowflake. Second, full-stack apps with custom UI; for example, advanced data exploration app, complex workflows app, and AI apps/agents, with Snowflake App Runtime. Third, external-facing, monetizable data products with the Native App framework, listed on the Snowflake Marketplace.
Which is easier to learn and staff: Snowflake or Databricks?
As organizations prioritize AI adoption, Snowflake’s ease of use provides a natural advantage: teams can focus on delivering value rather than assembling and maintaining the infrastructure behind it. Snowflake brings building, governing and operating AI into one managed environment. CoCo helps teams develop applications and pipelines, while CoWork helps users turn governed data into insights and actions. Declarative capabilities such as DCM Projects and native dbt simplify object management and transformation workflows, alongside integrated security, sharing, lineage, performance optimization, cost controls and disaster-recovery capabilities. Together, these capabilities mean fewer tools to connect and less infrastructure to manage. Databricks can suit teams with prioritizing extensive customization and also offers some managed capabilities. Snowflake’s strength is making that integrated foundation easier to adopt and operate. OM1 illustrates the value of simplification. Using Snowpark Container Services, the healthcare data company integrated an anonymizing language model into its dbt pipelines, replacing a complex workflow of notebooks, clusters and custom code with a single SQL call.
Why premier organizations choose Snowflake
65% cost savings
350% more efficient data delivery by switching from Databricks
“Snowflake's native support for Iceberg tables provides the performance, security and scalable compute needed”
54% cost savings
Replacing managed Spark with Snowflake
Migrating from Databricks to Snowflake
Assess compatibility
Inventory your notebooks, tables, catalog dependencies and Spark jobs. Snowflake AIM connects to your Databricks workspace, gathers that detail and scores how much of your code will move cleanly. You provide the code itself as files.
Connect before converting
Read supported Iceberg and Delta tables through Snowflake's Unity Catalog integration, without copying data. Convert formats or change catalog ownership only where your target architecture requires it.
Migrate incrementally
Databricks SQL converts automatically to Snowflake SQL. PySpark pipelines and notebooks convert separately, with Snowpark Connect keeping most PySpark code intact. Adapt whatever AIM flags as unsupported.
Validate before cutover
Compare results, price-performance and access controls. Confirm a rollback plan before retiring Databricks workloads.
