Snowflake Cortex AI architecture vs Databricks

The architectural divide

Snowflake builds its AI intelligence directly into the data platform. This differs from Databricks, which assembles AI capabilities around its lakehouse architecture. Snowflake Arctic, a 480-billion parameter Mixture-of-Experts model, uses 128 fine-grained experts and top-2 gating to select 17 billion active parameters for inference. The company released the weights under an Apache 2.0 license. Because Snowflake integrates these models into Cortex AI, agents inherit the security, masking, lineage, and access controls already present in the platform. Databricks uses MLflow to manage agents, but these agents remain guests that require engineers to manually build security and logging around them. Snowflake committed $6 billion to AWS infrastructure to meet the demand for AI on governed data. The Arctic model uses a hybrid architecture that combines a 10 billion dense transformer model with a residual Mixture of Experts component. This design allows for overlapping communication and computation to hide communication overhead during training. The training process for Arctic spanned three stages and used approximately 3.5 trillion tokens. Databricks provides unified governance through Unity Catalog, which manages table and column-level access control across workspaces.

Semantic control and agentic workflows

The battle for dominance centers on the semantic layer. Snowflake uses Semantic Views and CoCo to build a governed intelligence layer directly on top of enterprise data. This system discovers business concepts and relationships from source metadata. One customer used Snowflake CoCo to rebuild a data model in three weeks, which reduced a four-month project by 81%. Databricks Genie requires teams to manually add business context, descriptions, query instructions, and example queries to its domain-based spaces, and if business logic is messy, the answers can drift off course. Databricks recommends keeping each Genie space to about 30 tables to avoid noisy answers. If you are managing a team of 50 engineers, you should consider whether you want to spend their time building infrastructure or using it. Can an agent reason effectively without this manual curation? Snowflake Cortex Analyst achieves 90% SQL accuracy when paired with a mature semantic model. Cortex functions like AI_CLASSIFY provide zero-shot classification with JSON outputs, while AI_EXTRACT uses the inbuilt Arctic Extract model to turn paragraphs into structured fields. AI_TRANSCRIBE unlocks insights from audio files in Snowflake stages by returning full transcripts and segment timestamps. Snowflake users call models from OpenAI, Anthropic, Mistral, and DeepSeek as first-party functions through Cortex AI, which ensures that the intelligence stays within the governed environment of the platform.

Economics and performance

Pricing and performance models create a steep divide. Snowflake uses a credit-based system where larger warehouses consume more credits per hour. Databricks uses Databricks Units (DBUs) that vary by workload type, such as Jobs Compute, All-Purpose Compute, SQL Compute, or Model Serving, which makes cost prediction harder for many administrators. In 2025 benchmarking, Databricks ran 2.8x faster for ETL than Snowflake at 3.4x better price/performance. Snowflake Gen2 warehouses increased costs by up to 35% for I/O bound workloads. Snowflake storage costs approximately $23 to $40 per TB per month. Databricks is the better choice for heavy ML model training because of its native integration with MLflow and Python-based workflows, whereas Snowflake is the better choice for SQL-heavy analytics where users want simplicity.