Databricks and Snowflake reached a convergence point at the $5 billion annualized revenue mark in 2026. Databricks maintains a $5.4 billion run-rate with 65 percent year-over-year growth as of January 2026, while Snowflake reports roughly $5 billion in annualized revenue with 29 percent growth. The revenue gap reaches $400 million. Databricks grows faster, although both platforms scale within the same enterprise budgets.
The AI revenue divergence
The most significant distinction between the two platforms remains the scale of their AI-driven revenue. Databricks AI products surpassed $1 billion in revenue run-rate, whereas Snowflake AI revenue sits at $100 million. This $900 million canyon exists because Databricks built its architecture for AI and machine learning since its inception. Databricks relies on MLflow, Feature Store, Model Serving, and Vector Search to drive these figures.
Snowflake attempts to close this gap through the Cortex AI family. Snowflake Intelligence saw the fastest adoption ramp in company history, reaching 2,500 accounts within three months. Snowflake also sees significant adoption for Cortex AI, with AI workloads linked to approximately 50 percent of new bookings. You already know that the choice between Snowflake and Databricks depends on whether your data resides in a structured warehouse or a flexible lakehouse.
Databricks maintains an advantage in AI revenue because its tools like Agent Bricks and Mosaic AI integrate directly with its existing machine learning ecosystem. Databricks provides AI/BI Genie, which functions as a natural-language analytics counterpart to Snowflake Intelligence. Genie Deep Research creates research plans and analyzes multiple hypotheses using citations. While Snowflake expands its AI reach, the $1 billion versus $100 million metric suggests Databricks holds a structural lead in AI monetization.
MLflow adoption and agentic workflows
Databricks drives enterprise AI adoption through MLflow, which holds the title of the largest open source AI engineering platform for agents, LLMs, and ML models. MLflow sees over 30 million monthly downloads. Organizations use MLflow to debug, evaluate, monitor, and optimize production-quality AI applications. MLflow 3 on Databricks provides state-of-the-art observability, evaluation, and prompt management for agents and LLM applications.
MLflow 3 introduces Logged Models and Deployment Jobs to streamline the model lifecycle. Logged Models help teams track a model’s progress across different environments and runs. Deployment Jobs manage workflows including evaluation, approval, and deployment. These workflows rely on Unity Catalog for orchestration and save all events to an activity log.
Databricks uses MLflow to track training by logging parameters, metrics, artifacts, and code versions. The platform provides specific capabilities for agent development:
- MLflow Tracing records inputs, outputs, and metadata for each intermediate step of a request.
- Agent Evaluation measures and improves agent quality using MLflow evaluation.
- Prompt management allows users to version and iterate on prompt templates.
- Custom Agents allow developers to create agents that rely on MLflow to track code, performance metrics, and traces.
- Genie Code provides natural language access to traces, evaluation runs, scorers, and more.
Databricks integrates MLflow with Unity Catalog to provide centralized governance. This integration allows users to access models across workspaces, track model lineage, and discover models for reuse.
Snowflake Cortex and agentic expansion
Snowflake focuses its AI strategy on the Cortex AI family and the Snowflake Intelligence agentic experience. Snowflake Intelligence allows business users to converse with data using natural language, powered by LLMs from Anthropic and OpenAI. Snowflake Cortex Agents reached general availability on November 4, 2025. These agents run inside the Snowflake perimeter to maintain data security.
Snowflake also introduced the Cortex AI Gateway on July 28, 2026. This tool provides a centralized layer for governing agent interactions. It allows organizations to control which models, data, applications, MCP servers, and tools agents can access. The gateway provides centralized visibility into agent activity and helps control AI consumption costs by providing a unified view of spending.
Snowflake uses AI to drive platform consumption through two main products:
- CoCo: This tool provides AI-assisted data engineering and migration, with more than 9,100 accounts using it.
- CoWork: This product expands Snowflake into agentic workflows, with 5,800 accounts using it to interact with data.
Snowflake’s AI strategy targets the entire data lifecycle. The company recently acquired Observe to expand into observability and IT operations. This move allows Snowflake to ingest voluminous telemetry data like logs, metrics, and traces.
Architectural foundations and performance
The two companies follow different architectural philosophies. Snowflake utilizes a shared storage and elastic compute model. It separates storage, compute, and cloud services into three distinct layers. Compute runs as independent virtual warehouses that scale horizontally without data movement. Snowflake’s second-generation warehouses deliver 1.8x faster core analytics performance and a 5.5x improvement in DML operations.
Databricks utilizes the Lakehouse paradigm. It stores data in open formats like Delta Lake and Iceberg directly on cloud object storage. The compute engine uses Apache Spark and the proprietary Photon engine. Photon provides up to 12x performance improvement on TPC-DS big data workloads compared to vanilla Spark. Databricks also introduced Lakebase in 2026, which provides a serverless PostgreSQL offering for transactional workloads.
| Capability | Databricks | Snowflake |
|---|---|---|
| Primary Architecture | Lakehouse (Spark/Photon) | Cloud Data Warehouse |
| AI Revenue Run-rate | >$1 Billion | ~$100 Million |
| AI Product Adoption | High (MLflow/Mosaic) | Rapid (Snowflake Intelligence) |
| Transactional Support | Lakebase (via Neon) | Snowflake Postgres |
| Open Format Support | Native (Delta/Iceberg/Hudi) | Polaris Catalog (Iceberg) |
The convergence of these architectures means Databricks now offers high-performance SQL through Databricks SQL, while Snowflake supports open Iceberg tables through the Polaris Catalog. Databricks SQL reached an annual revenue run-rate of $600 million, growing more than 150 percent from a year ago.
Customer profiles and pricing models
Databricks and Snowflake attract different enterprise profiles. Databricks serves ML-led enterprises in pharma, advanced manufacturing, and large gaming, such as SciPlay, which reported a 5 percent uplift in user retention and a 75 percent reduction in game launch time. Databricks serves more than 20,000 organizations globally, with over 800 customers consuming more than $1 million annually and over 70 customers consuming more than $10 million.
Snowflake serves analytics-led enterprises, including Fortune 500 companies focusing on BI consolidation and financial services. Snowflake has 688 customers with trailing 12-month product revenue greater than $1 million and 766 Forbes Global 2000 customers. Snowflake’s customer base for high spenders also grows, with 56 customers exceeding $10 million in spend in Q4 FY26.
Spend data reveals differences in how these companies price their services.
| Pricing Tier | Databricks (Annual) | Snowflake (Annual) |
|---|---|---|
| SMB Average Spend | $59,607 | $185,082 |
| Enterprise Average Spend | $500,084 | $745,666 |
Databricks pricing for enterprise tiers remains negotiable based on headcount and usage volume. Snowflake’s pricing targets different needs, but enterprise customers spend significantly more on average. Snowflake’s net revenue retention shows a different trend than Databricks. While Databricks maintains a net revenue retention rate above 140 percent, Snowflake’s net revenue retention has declined from 171 percent at peak to 125 percent today.
Governance and open source posture
Both vendors compete on their commitment to open-source standards. Databricks open-sourced Unity Catalog, providing a unified governance layer for all data and AI assets. Unity Catalog allows users to access models across workspaces and track model lineage. Databricks also supports Delta Lake, Iceberg, Hudi, Parquet, CSV, and JSON.
Snowflake introduced the Polaris Catalog as its open-source Iceberg catalog. Polaris supports integration with Apache Doris, Apache Flink, Apache Spark, PyIceberg, StarRocks, and Trino. This move helps Snowflake mitigate lock-in concerns for procurement teams.
Databricks maintains a deeper public commitment to open source, but Snowflake’s Polaris Catalog targets the same Iceberg user base. The decision between Unity Catalog and Polaris depends on whether a team prefers a Lakehouse-native governance model or a warehouse-centric one.
Will Snowflake’s expansion into observability via the Observe acquisition create enough momentum to offset Databricks’ AI lead?
Workload determination for 2026
The platform choice depends on the dominant workload. Snowflake provides a simpler experience for structured analytical SQL and BI workloads. Its pricing model and time-to-first-query benefit teams with strong SQL skills. Databricks provides a better fit for ML model training, batch scoring, and large-scale unstructured data processing.
Databricks manages the machine learning lifecycle through MLflow, which integrates with Unity Catalog to coordinate evaluation and deployment workflows. Snowflake manages the AI lifecycle through Cortex, which brings generative AI directly into customer queries.
For organizations running both workloads, the architecture often involves using Iceberg as the underlying format. This allows Polaris or Unity Catalog to serve as the governance layer while both Snowflake and Databricks engines read from the same physical data. This configuration reduces the argument for vendor lock-in.
Companies should follow a structured plan to decide between these platforms.
- Days 1-30: Inventory workloads and project 2026 growth per workload type.
- Days 31-60: Pilot the candidate platform on the two highest-leverage workloads.
- Days 61-90: Review total cost of ownership, including compute, storage, and network.
If your organization requires advanced ML engineering and large-scale unstructured data processing, Databricks remains the structural choice. If your organization prioritizes governed SQL analytics and rapid deployment of agentic tools for business users, Snowflake provides the faster path to production.
