Decision comparison
Databricks vs Starburst
Databricks and Starburst address fundamentally different data challenges despite both operating in the lakehouse space. Databricks delivers a unified platform where data engineering, SQL analytics, and machine learning converge under one roof. Its strength lies in the breadth of workloads it handles — from ETL pipelines with Delta Live Tables to model serving with Mosaic AI. Starburst takes a different approach by excelling at federated query access: it lets you query data where it already lives across dozens of sources without requiring data movement. If your organization is consolidating around a single lakehouse and needs integrated ML capabilities, Databricks is the stronger choice. If your data lives across multiple systems and you need a SQL-first analytics layer that federates queries without centralization, Starburst offers a more targeted and cost-effective solution with its free tier and credit-based pricing.
Data Warehouses
Last Updated:
Quick Comparison
| Decision factor | Databricks | Starburst |
|---|---|---|
| Architecture | Lakehouse architecture unifying data lakes and warehouses on cloud object storage with Delta Lake | Federated query engine built on Trino that queries data in place across lakes, warehouses, and databases |
| Query Engine | Apache Spark-based processing with Photon engine for SQL workloads and Delta Engine optimizations | Enhanced Trino-based ANSI SQL engine with 50+ connectors and vendor-reported quick query performance |
| Pricing Model | Standard $289/mo (5TB), Premium $1,499/mo (50TB). Free trial available. | Free tier (up to 3 clusters, standard cluster execution mode), Pro tier starting at $0.50/credit (flexible cluster execution modes, streaming ingest), Enterprise tier starting at $0.75/credit (advanced autoscaling, fine-grained access controls) |
| Data Federation | Primarily designed for centralized lakehouse; supports external data via Unity Catalog and partner connectors | Core strength with 50+ connectors enabling queries across distributed data sources without data movement |
| ML/AI Capabilities | Full ML lifecycle with managed MLflow, experiment tracking, model serving, Mosaic AI, and AutoML | Focused on serving governed data to AI applications; no native ML training or model serving pipeline |
| Best For | Unified data engineering, analytics, and AI/ML teams needing deep Spark integration and lakehouse architecture | Analytics teams needing federated SQL access across multiple data sources with strong governance |
Databricks
- Architecture:
- Lakehouse architecture unifying data lakes and warehouses on cloud object storage with Delta Lake
- Query Engine:
- Apache Spark-based processing with Photon engine for SQL workloads and Delta Engine optimizations
- Pricing Model:
- Standard $289/mo (5TB), Premium $1,499/mo (50TB). Free trial available.
- Data Federation:
- Primarily designed for centralized lakehouse; supports external data via Unity Catalog and partner connectors
- ML/AI Capabilities:
- Full ML lifecycle with managed MLflow, experiment tracking, model serving, Mosaic AI, and AutoML
- Best For:
- Unified data engineering, analytics, and AI/ML teams needing deep Spark integration and lakehouse architecture
Starburst
- Architecture:
- Federated query engine built on Trino that queries data in place across lakes, warehouses, and databases
- Query Engine:
- Enhanced Trino-based ANSI SQL engine with 50+ connectors and vendor-reported quick query performance
- Pricing Model:
- Free tier (up to 3 clusters, standard cluster execution mode), Pro tier starting at $0.50/credit (flexible cluster execution modes, streaming ingest), Enterprise tier starting at $0.75/credit (advanced autoscaling, fine-grained access controls)
- Data Federation:
- Core strength with 50+ connectors enabling queries across distributed data sources without data movement
- ML/AI Capabilities:
- Focused on serving governed data to AI applications; no native ML training or model serving pipeline
- Best For:
- Analytics teams needing federated SQL access across multiple data sources with strong governance
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Databricks | Starburst |
|---|---|---|
| GitHub stars(Developer adoption) | 385 | Not available |
| npm weekly downloads(Developer adoption) | 573.0k | Not available |
| Product Hunt comments(Community interest) | 5 | Not available |
| Product Hunt rating(Community interest) | 5.0/5 | Not available |
| Product Hunt reviews(Community interest) | 5 | Not available |
| Product Hunt votes(Community interest) | 91 | Not available |
| PyPI weekly downloads(Developer adoption) | 21.6M | Not available |
| Stack Overflow questions(Community interest) | 8.4k | 28 |
| GitHub commits, 90d(Developer adoption) | 699 | Not available |
| Hacker News mentions, 90d(Community interest) | 69 | Not available |
| Docker Hub pulls(Product adoption) | Not available | 375.5k |
| PyPI weekly downloads(Ecosystem adoption) | Not available | 4.6M |
As of August 31, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Databricks
August 31, 2026Package vulnerabilities
npm · @databricks/sql@2.0.0 · PyPI · databricks-sdk@0.133.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Starburst
August 31, 2026Package vulnerabilities
PyPI · trino@0.339.0
0 vulnerabilities
across 1 package
Repository security score
Not available
Interface Preview
Starburst
Feature Comparison
| Feature | Databricks | Starburst |
|---|---|---|
| Data Processing & Query Engine | ||
| SQL Query Support | Full SQL support via Databricks SQL with Photon and Delta Engine optimizations for warehouse-style workloads | ANSI SQL engine built on enhanced Trino with vendor-reported quick performance across federated sources |
| Multi-language Support | SQL, Python, Scala, and R via collaborative notebooks with full Spark integration | SQL-only query interface; Python and other languages supported through external integrations |
| Batch Processing | Managed Apache Spark for large-scale ETL with Delta Live Tables for declarative pipeline orchestration | Primarily an interactive query engine; batch workloads handled via scheduled queries |
| Data Architecture & Storage | ||
| Lakehouse Support | Native lakehouse architecture with Delta Lake providing ACID transactions, schema evolution, and time travel | Open data lakehouse with native support for Apache Iceberg, Delta Lake, Apache Hudi, and Hive table formats |
| Data Federation | Unity Catalog enables cross-source governance; primarily designed for centralized lakehouse storage | Core design principle with 50+ connectors enabling federated queries across lakes, warehouses, and databases |
| Open Table Format Support | Delta Lake as primary format with emerging support for Apache Iceberg through UniForm | Native support for Iceberg, Delta Lake, Hudi, and Hive formats with no vendor lock-in |
| AI & Machine Learning | ||
| ML Training & Experimentation | Managed MLflow for experiment tracking, AutoML, and integrated model development workflows | No native ML training; serves as a governed data layer for external ML tools and frameworks |
| Model Serving & Deployment | Built-in model serving with Mosaic AI, real-time endpoints, and GPU-accelerated inference | No native model serving; focuses on delivering governed data to AI applications and agents |
| AI Agent Support | Mosaic AI for building compound AI systems and agent frameworks on the lakehouse | Data platform for AI agents with built-in governance and context for trusted data access |
| Governance & Security | ||
| Access Control | Unity Catalog with role-based access control, data lineage, and centralized governance across workspaces | Fine-grained access controls with ABAC and SCIM at Enterprise tier; built-in governance and context layer |
| Data Lineage | End-to-end lineage tracking through Unity Catalog across tables, columns, and notebooks | Query-level lineage tracking across federated data sources with governance context |
| Compliance Features | SOC 2, HIPAA, FedRAMP certifications; workspace-level isolation and encryption | AWS PrivateLink support at Enterprise tier; role-based and attribute-based access for regulatory compliance |
| Deployment & Operations | ||
| Cloud Deployment | Multi-cloud deployment across AWS, Azure, and GCP with managed control plane | Starburst Galaxy (managed cloud) plus self-managed options for on-premises and hybrid environments |
| Cluster Management | Auto-scaling clusters with spot instance support for 60-70% cost savings on AWS | Advanced autoscaling at Enterprise tier; flexible cluster execution modes at Pro tier and above |
| Collaboration Tools | Shared notebooks, repos, dashboards, and workspace-level collaboration with role-based access | SQL-focused workbench for collaborative querying; integrates with BI tools for downstream collaboration |
Data Processing & Query Engine
SQL Query Support
DatabricksFull SQL support via Databricks SQL with Photon and Delta Engine optimizations for warehouse-style workloads
StarburstANSI SQL engine built on enhanced Trino with vendor-reported quick performance across federated sources
Multi-language Support
DatabricksSQL, Python, Scala, and R via collaborative notebooks with full Spark integration
StarburstSQL-only query interface; Python and other languages supported through external integrations
Batch Processing
DatabricksManaged Apache Spark for large-scale ETL with Delta Live Tables for declarative pipeline orchestration
StarburstPrimarily an interactive query engine; batch workloads handled via scheduled queries
Data Architecture & Storage
Lakehouse Support
DatabricksNative lakehouse architecture with Delta Lake providing ACID transactions, schema evolution, and time travel
StarburstOpen data lakehouse with native support for Apache Iceberg, Delta Lake, Apache Hudi, and Hive table formats
Data Federation
DatabricksUnity Catalog enables cross-source governance; primarily designed for centralized lakehouse storage
StarburstCore design principle with 50+ connectors enabling federated queries across lakes, warehouses, and databases
Open Table Format Support
DatabricksDelta Lake as primary format with emerging support for Apache Iceberg through UniForm
StarburstNative support for Iceberg, Delta Lake, Hudi, and Hive formats with no vendor lock-in
AI & Machine Learning
ML Training & Experimentation
DatabricksManaged MLflow for experiment tracking, AutoML, and integrated model development workflows
StarburstNo native ML training; serves as a governed data layer for external ML tools and frameworks
Model Serving & Deployment
DatabricksBuilt-in model serving with Mosaic AI, real-time endpoints, and GPU-accelerated inference
StarburstNo native model serving; focuses on delivering governed data to AI applications and agents
AI Agent Support
DatabricksMosaic AI for building compound AI systems and agent frameworks on the lakehouse
StarburstData platform for AI agents with built-in governance and context for trusted data access
Governance & Security
Access Control
DatabricksUnity Catalog with role-based access control, data lineage, and centralized governance across workspaces
StarburstFine-grained access controls with ABAC and SCIM at Enterprise tier; built-in governance and context layer
Data Lineage
DatabricksEnd-to-end lineage tracking through Unity Catalog across tables, columns, and notebooks
StarburstQuery-level lineage tracking across federated data sources with governance context
Compliance Features
DatabricksSOC 2, HIPAA, FedRAMP certifications; workspace-level isolation and encryption
StarburstAWS PrivateLink support at Enterprise tier; role-based and attribute-based access for regulatory compliance
Deployment & Operations
Cloud Deployment
DatabricksMulti-cloud deployment across AWS, Azure, and GCP with managed control plane
StarburstStarburst Galaxy (managed cloud) plus self-managed options for on-premises and hybrid environments
Cluster Management
DatabricksAuto-scaling clusters with spot instance support for 60-70% cost savings on AWS
StarburstAdvanced autoscaling at Enterprise tier; flexible cluster execution modes at Pro tier and above
Collaboration Tools
DatabricksShared notebooks, repos, dashboards, and workspace-level collaboration with role-based access
StarburstSQL-focused workbench for collaborative querying; integrates with BI tools for downstream collaboration
Decision Summary
Databricks and Starburst address fundamentally different data challenges despite both operating in the lakehouse space. Databricks delivers a unified platform where data engineering, SQL analytics, and machine learning converge under one roof. Its strength lies in the breadth of workloads it handles — from ETL pipelines with Delta Live Tables to model serving with Mosaic AI. Starburst takes a different approach by excelling at federated query access: it lets you query data where it already lives across dozens of sources without requiring data movement. If your organization is consolidating around a single lakehouse and needs integrated ML capabilities, Databricks is the stronger choice. If your data lives across multiple systems and you need a SQL-first analytics layer that federates queries without centralization, Starburst offers a more targeted and cost-effective solution with its free tier and credit-based pricing.
Best-fit scenarios
Choose Databricks if:
Choose Databricks when you need a unified platform that covers data engineering, analytics, and AI/ML under a single architecture. It is the right fit for teams running complex Spark-based ETL pipelines, training and deploying machine learning models, and collaborating across data science and engineering roles. The lakehouse architecture with Delta Lake provides strong data reliability through ACID transactions, schema evolution, and time travel. Organizations already invested in Apache Spark or those needing managed MLflow and GPU-accelerated model serving will find Databricks delivers the most integrated experience.
Choose Starburst if:
Choose Starburst when your priority is querying data across multiple distributed systems without consolidating everything into a single warehouse. Its Trino-based federated engine connects to 50+ data sources, making it ideal for organizations with data spread across lakes, warehouses, and operational databases. The free tier with up to 3 clusters makes it accessible for smaller teams, while the credit-based pricing at a current vendor-published rate (Pro) and a current vendor-published rate (Enterprise) keeps costs predictable. Starburst is particularly strong for analytics teams that need a governed SQL layer on top of existing infrastructure without heavy data migration.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
Can Databricks and Starburst be used together?
Yes. Many organizations use Starburst as a federated query layer on top of a Databricks lakehouse. Starburst can connect to Delta Lake tables stored in Databricks while also querying data from other sources like Snowflake, PostgreSQL, or S3. This combination lets teams maintain a centralized lakehouse in Databricks for ML and engineering workloads while using Starburst to provide unified SQL access across the entire data estate.
Which platform offers better pricing for small teams?
Starburst is more accessible for smaller teams with its free tier that includes up to 3 clusters at no cost. The Pro tier starts at a current vendor-published rate per credit with usage-based billing. Databricks uses consumption-based DBU pricing with no free tier for production workloads — Jobs compute starts at roughly a vendor-specific amount per DBU, but All-Purpose compute costs at a higher usage rate, and cloud infrastructure fees from AWS, Azure, or GCP are billed on top. For teams focused purely on SQL analytics, Starburst generally has a a different commercial starting point.
How do Databricks and Starburst differ on open table format support?
Both platforms support open lakehouse formats but with different emphases. Databricks uses Delta Lake as its native format and has added Iceberg compatibility through UniForm. Starburst provides native support for Apache Iceberg, Delta Lake, Apache Hudi, and Apache Hive without favoring any single format. This makes Starburst a stronger choice for organizations that want format flexibility and need to avoid vendor lock-in to a specific table format.
Which platform is better for machine learning workloads?
Databricks is the clear leader for ML workloads. It offers managed MLflow for experiment tracking, AutoML for automated model development, model serving endpoints for real-time inference, and Mosaic AI for building compound AI systems. Starburst does not provide native ML training or model serving capabilities — it focuses on delivering governed data to external ML tools and AI agents. Teams that need an end-to-end ML lifecycle integrated with their data platform should choose Databricks.
What deployment options does each platform support?
Databricks supports multi-cloud deployment across AWS, Azure, and GCP with a managed control plane. Starburst offers Starburst Galaxy as a managed cloud service plus self-managed deployment options for on-premises and hybrid environments. Starburst's self-managed option gives more flexibility for organizations with strict data residency or air-gapped requirements, while Databricks provides a fully managed experience across the three major cloud providers.