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It's a split decision.
Choose Dremio for an Iceberg-native query platform with a self-managed option, Apache Polaris catalog and MCP access for AI agents. Choose Databricks if you want data engineering, warehousing, analytics and AI in one metered platform, with AWS, Azure and GCP support. Pricing is usage-based in both cases, but DCUs and DBUs are not directly comparable. Model your expected workloads and validate table portability, access controls and migration paths before committing.
01Side by side
| Dremio | Databricks | |
|---|---|---|
| Pricing model | $0.20 per DCU, pay-as-you-go | Per DBU or CU; rates vary by workload |
| Free tier | Free plan listed; 30-day trial, $400 credit | $0 Community Edition; 14-day trial |
| Self-hosting | Enterprise self-managed on Kubernetes or cloud | Self-hosting not described; supports AWS, Azure, GCP |
| Developer entry points | Dremio CLI supports Claude Code and Codex | IDE integrations and partner ecosystem |
| Integrations | MCP, BI tools, federated data sources | Cloud providers, IDEs, partner ecosystem |
| Deployment model | Managed cloud or self-managed software | Platform on AWS, Azure and GCP |
| Platforms | Web | Web |
| Limits and scaling | Autonomous Reflections; serverless scaling on demand | Billed by the second; usage-based compute |
Dremio Choose Dremio if
- Choose Dremio if Iceberg-native querying is central to your architecture.
- Choose Dremio if you need a self-managed Kubernetes deployment.
- Choose Dremio if agents need MCP access to governed enterprise data.
Databricks Choose Databricks if
- Choose Databricks if you need data engineering, warehousing and AI together.
- Choose Databricks if your workloads use AWS, Azure or GCP.
- Choose Databricks if a zero-cost single-node learning tier is useful.
02Pricing at real usage
Dremio Cloud lists pay-as-you-go pricing at $0.20 per DCU, plus a 30-day trial with $400 credit. Dremio says annual contracts with committed pricing are available, but it does not publish those rates. Enterprise is consumption-based, with pricing available through sales. The free plan is listed, but its terms are not specified.
Databricks Community Edition costs $0 per month and includes a single-node cluster with limited micro-cluster and storage functionality. Premium pay-as-you-go bills by the second: Data Engineering starts at $0.15/DBU and Data Warehousing at $0.22/DBU. Genie includes 150 free monthly DBUs, then starts at $0.07/DBU. A 14-day trial is available; cloud infrastructure costs are separate. Both platforms’ bills grow with compute use, and their billing units are not directly comparable. Databricks also lists custom annual or multi-year commitments.
03API, SDKs and integration
Dremio’s clearest agent interface is MCP: the platform connects agents including Claude, ChatGPT and Gemini to enterprise data. Its CLI gives Claude Code and Codex direct lakehouse access. Dremio also connects to BI tools and federates queries across data sources, while Apache Polaris supports read and write across Iceberg REST engines.
Databricks groups data engineering, warehousing, AI, analytics and application development in one platform. Lakeflow handles batch and streaming ETL pipelines; Genie supports natural-language questions over enterprise data. Databricks also lists IDE integrations, cloud-provider integrations for AWS, Azure and GCP, and a partner ecosystem. The vendors’ product pages do not name language-specific SDKs or API protocols for either platform, so confirm the supported client libraries and authentication flows for your stack. For agent work, compare Dremio’s MCP path with the Databricks tools and integrations required by your application.
04Open source, self-hosting and lock-in
Neither vendor publishes a licence for its complete platform. Dremio’s use of Apache Polaris and Apache Iceberg does not make the whole Dremio product open source. Databricks highlights open-source technologies, but that does not establish an open-source licence for its hosted platform.
Dremio Enterprise can run self-managed on Kubernetes or AWS, Azure and GCP; Dremio Cloud is managed. Databricks supports AWS, Azure and GCP, but its product information does not describe a self-hosted deployment. Both platforms build around open lake data, while Dremio explicitly supports Iceberg natively and read/write across Iceberg REST engines. That can inform portability, but it is not a substitute for testing catalog, permissions and table access during migration. Before adopting either platform, define how you will export data, metadata and governance settings, then validate the path with your actual formats and destination systems.
Questions people ask
Is Dremio cheaper than Databricks?
There is no direct price comparison from the listed rates: Dremio charges $0.20 per DCU, while Databricks charges by DBU or CU, with rates varying by workload. Databricks lists Data Engineering from $0.15/DBU and Data Warehousing from $0.22/DBU. Compare expected workload usage, not just unit prices.
Does either platform have a free tier?
Both list free options. Dremio lists a free plan and a 30-day trial with $400 credit, though free-plan terms are not specified. Databricks Community Edition is $0 per month for a single-node cluster with limited micro-cluster and storage functionality. Databricks also offers a 14-day trial.
Can you self-host Databricks?
Databricks lists AWS, Azure and GCP support, but its product information does not describe a self-hosted deployment. Dremio Enterprise explicitly supports self-management on Kubernetes or AWS, Azure and GCP. If deployment control is a hard requirement, confirm Databricks options with the vendor before evaluating it against Dremio.
Which platform is better for AI agents?
Dremio has a direct MCP integration for connecting agents including Claude, ChatGPT and Gemini, plus CLI access for Claude Code and Codex. Databricks offers agent-building capabilities and Genie for natural-language analytics. Choose based on whether you need agent connectivity to lakehouse data or a broader platform for building and running data and AI workloads.