Move to the dbt platform: What you need to know

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Introduction

Moving from self-hosted dbt to the dbt platform streamlines analytics engineering workflows by allowing teams to develop, test, deploy, and explore data products using a single, fully managed software service. It's not just better tooling — it's about lowering total cost of ownership, powering AI with trusted data, and scaling with governed self-service.

Explore our 3-part-guide series on moving from self-hosted dbt to the dbt platform. The series is ideal for users aiming for streamlined workflows and enhanced analytics:

Guide	Information	Audience
Move to the dbt platform: What you need to know	Understand the considerations and methods needed in your move to the dbt platform.	Team leads Admins
Move to the dbt platform: Get started	Learn the steps needed to move to the dbt platform.	Developers Data engineers Data analysts
Move to the dbt platform: Optimization tips	Learn how to optimize your dbt platform experience with common scenarios and useful tips.	Everyone

Why move to the dbt platform?

If your team is using self-hosted dbt today, you could be reading this guide because:

• You've realized the burden of maintaining that deployment.
• The person who set it up has since left.
• You're interested in what the dbt platform could do to better manage the complexity of your dbt deployment, democratize access to more contributors, or improve security and governance practices.
• You need a governed data foundation for AI—shared definitions, lineage, and testing so analytics and AI give answers the business can trust.

Self-hosting hides its true cost in engineer hours and wasted compute. dbt platform eliminates that overhead with managed infrastructure and browser-based development so more people can contribute without you being the bottleneck.

State-aware orchestration is now dbt State

dbt State works with all engines and environments: dbt v1, the dbt platform, and dbt v2.

If you were using state-aware orchestration prior to June 1, 2026, you can continue using it. Once you start your free dbt State trial, it will be extended beyond the standard 30-day period. If the extension isn't applied to your account, contact your account team. To get started, refer to Migrate from state-aware orchestration.

The data layer is the AI layer—make sure it's tested, defined, and trusted end to end.

Moving to the dbt platform simplifies workflows by providing a fully managed environment that improves collaboration, security, and orchestration. With the dbt platform, you gain access to features like cross-team collaboration (dbt Mesh), version management, streamlined CI/CD, Catalog for comprehensive insights, and more — making it easier to manage complex dbt deployments and scale your data workflows efficiently.

It's ideal for teams looking to reduce the burden of maintaining their own infrastructure while enhancing governance and productivity.

 What are dbt and dbt v1?

• The dbt platform is the fastest and most reliable way to deploy dbt. It enables you to develop, test, deploy, and explore data products using a single, fully managed service. It also supports:
  • Development experiences tailored to multiple personas (Studio IDE or dbt platform CLI)
  • Out-of-the-box CI/CD workflows
  • The Semantic Layer for consistent metrics
  • Domain ownership of data with multi-project Mesh setups
  • Catalog for easier data discovery and understanding

Learn more about dbt platform features.

• dbt is an open-source tool that enables data teams to define and execute data transformations in a cloud data warehouse following analytics engineering best practices. While this can work well for 'single players' and small technical teams, all development happens on a command-line interface, and production deployments must be self-hosted and maintained. This requires significant, costly work that adds up over time to maintain and scale.

What you'll learn

Today thousands of companies, with data teams ranging in size from 2 to 2,000, rely on the dbt platform to accelerate data work, increase collaboration, and win the trust of the business. Understanding what you'll need to do in order to move between the dbt platform and your current self-hosted dbt deployment will help you strategize and plan for your move.

The guide outlines the following steps:

• Considerations: Learn about the most important things you need to think about when moving from self-hosted dbt to the dbt platform.
• Plan your move: Considerations you need to make, such as user roles and permissions, onboarding order, current workflows, and more.
• Move to the dbt platform: Review the steps to move your dbt project to the dbt platform, including setting up your account, data platform, and Git repository.
• Test and validate: Discover how to ensure model accuracy and performance post-move.
• Transition and training: Learn how to fully transition to the dbt platform and what training and support you may need.
• Summary: Summarizes key takeaways and what you've learned in this guide.
• What's next?: Introduces what to expect in the following guides.

Considerations

If your team is using self-hosted dbt today, you could be reading this guide because:

• You've realized the burden of maintaining that deployment.
• The person who set it up has since left.
• You're interested in what the dbt platform could do to better manage the complexity of your dbt deployment, democratize access to more contributors, or improve security and governance practices.

This guide shares the technical adjustments and team collaboration strategies you'll need to know to move your project from self-hosted dbt to the dbt platform. Each "build your own" deployment of dbt will look a little different, but after seeing hundreds of teams make the migration, there are many things in common.

The most important things you need to think about when moving from self-hosted dbt to the dbt platform:

• How is your team structured? Are there natural divisions of domain?
• Should you have one project or multiple? Which dbt resources do you want to standardize & keep central?
• Who should have permission to view, develop, and administer?
• How are you scheduling your dbt models to run in production?
• How are you currently managing Continuous integration/Continuous deployment (CI/CD) of logical changes (if at all)?
• How do your data developers prefer to work?
• How do you manage different data environments and the different behaviors in those environments?

The dbt platform provides standard mechanisms for tackling these considerations, all of which deliver long-term benefits to your organization:

• Cross-team collaboration
• Access control
• Orchestration
• Isolated data environments

If you have rolled out your own self-hosted dbt deployment, you have probably come up with different answers.

Plan your move

As you plan your move, consider your workflow and team layout to ensure a smooth transition. Here are some key considerations to keep in mind:

 Start small to minimize risk and maximize learning

You don't need to move every team and every developer's workflow all at once. Many customers with large dbt deployments start by moving one team and one project.

Once the benefits of a consolidated platform are clear, move the rest of your teams and workflows. While long-term 'hybrid' deployments can be challenging, it may make sense as a temporary on-ramp.

 User roles and responsibilities

Assess the users or personas involved in the pre-move, during the move, and post-move.

• Administrators: Plan for new access controls in the dbt platform, such as deciding what teams can manage themselves and what should be standardized. Determine who will be responsible for setting up and maintaining projects, data platform connections, and environments.
• Data developers (data analysts, data engineers, analytics engineers, business analysts): Determine onboarding order, workflow adaptation in the dbt platform, training on dbt platform CLI or Studio IDE usage, and role changes.
• Data consumers: Discover data insights by using Catalog to view your project's resources (such as models, tests, and metrics) and their lineage to gain a better understanding of its latest production state. StarterEnterprise

 Onboarding order

If you have multiple teams of dbt developers, think about how to start your onboarding sequence for the dbt platform:

• Start with downstream (like business-embedded teams) who may benefit from the Studio IDE as dev experience (less technical users) and sharing features (like auto-deferral and Catalog) to share with their stakeholders, moving to more technical teams later.
• Consider setting up a CI job in the dbt platform (even before development or production jobs) to streamline development workflows. This is especially beneficial if there's no existing CI process.

 Analyze current workflows, review processes, and team structures

Discover how the dbt platform can help simplify development, orchestration, and testing:

• Development: Develop dbt models, allowing you to build, test, run, and version control your dbt projects using the dbt platform CLI (command line interface or code editor) or Studio IDE (browser-based).
• Orchestration: Create custom schedules to run your production jobs. Schedule jobs by day of the week, time of day, or a recurring interval.
  • Set up a CI job to ensure developer effectiveness, and CD jobs to deploy changes as soon as they're merged.
  • Link deploy jobs together by triggering a job when another one is completed.
  • For the most flexibility, use the dbt platform API to trigger jobs. This makes sense when you want to integrate dbt execution with other data workflows.
• Continuous integration (CI): Use CI jobs to run your dbt projects in a temporary schema when new commits are pushed to open pull requests. This build-on-PR functionality is a great way to catch bugs before deploying to production.
  • For many teams, dbt platform CI represents a major improvement compared to their previous development workflows.
• How are you defining tests today?: While testing production data is important, it's not the most efficient way to catch logical errors introduced by developers You can use unit testing to allow you to validate your SQL modeling logic on a small set of static inputs before you materialize your full model in production.

 Understand access control

Transition to the dbt platform's access control mechanisms to ensure security and proper access management. dbt platform administrators can use the dbt platform's permission model to control user-level access in a dbt platform account:

• License-based access controls: Users are configured with account-wide license types. These licenses control the things a user can do within the application: view project metadata, develop changes within those projects, or administer access to those projects.
• Role-based Access Control (RBAC): Users are assigned to groups with specific permissions on specific projects or all projects in the account. A user may be a member of multiple groups, and those groups may have permissions on multiple projects. EnterpriseEnterprise +

 Manage environments

If you require isolation between production and pre-production data environments due to sensitive data, the dbt platform can support Development, Staging, and Production data environments.

This provides developers with the benefits of an enhanced workflow while ensuring isolation between Staging and Production data, and locking down permissions on Prod.

Move to dbt

This guide is your roadmap to help you think about migration strategies and what moving from self-hosted dbt to the dbt platform could look like.

After reviewing the considerations and planning your move, you may want to start moving your dbt project to the dbt platform:

• Check out the detailed Move to the dbt platform: Get started guide for useful tasks and insights for a smooth transition from self-hosted dbt to the dbt platform.

For a more detailed comparison of self-hosted dbt and the dbt platform, check out How the dbt platform compares with self-hosted dbt.

Test and validate

After setting the foundations of the dbt platform, it's important to validate your migration to ensure seamless functionality and data integrity:

• Review your dbt project: Ensure your project compiles correctly and that you can run commands. Make sure your models are accurate and monitor performance post-move.
• Start cutover: You can start the cutover to the dbt platform by creating a dbt platform job with commands that only run a small subset of the DAG. Validate the tables are being populated in the proper database/schemas as expected. Then continue to expand the scope of the job to include more sections of the DAG as you gain confidence in the results.
• Precision testing: Use unit testing to allow you to validate your SQL modeling logic on a small set of static inputs before you materialize your full model in production.
• Access and permissions: Review and adjust access controls and permissions within the dbt platform to maintain security protocols and safeguard your data.

Transition and training

Once you've confirmed that dbt platform orchestration and CI/CD are working as expected, you should pause your current orchestration tool and stop or update your current CI/CD process. This is not relevant if you're still using an external orchestrator (such as Airflow), and you've swapped out local dbt execution for dbt platform execution (through the API).

Familiarize your team with the dbt platform's features and optimize development and deployment processes. Some key features to consider include:

• Release tracks: Choose a release track for automatic dbt version upgrades, at the cadence appropriate for your team — removing the hassle of manual updates and the risk of version discrepancies. You can also get early access to new functionality, ahead of self-hosted dbt.
• Development tools: Use the dbt platform CLI or Studio IDE to build, test, run, and version control your dbt projects.
• Documentation and Source freshness: Automate storage of documentation and track source freshness in the dbt platform, which streamlines project maintenance.
• Notifications and logs: Receive immediate notifications for job failures, with direct links to the job details. Access comprehensive logs for all job runs to help with troubleshooting.
• CI/CD: Use the dbt platform's CI/CD feature to run your dbt projects in a temporary schema whenever new commits are pushed to open pull requests. This helps with catching bugs before deploying to production.

Beyond your move

Now that you've chosen the dbt platform as your platform, you've unlocked the power of streamlining collaboration, enhancing workflow efficiency, and leveraging powerful features for analytics engineering teams. Here are some additional features you can use to unlock the full potential of the dbt platform:

• Audit logs: Use audit logs to review actions performed by people in your organization. Audit logs contain audited user and system events in real time. You can even export all the activity (beyond the 90 days you can view in the dbt platform). EnterpriseEnterprise +
• dbt platform APIs: Use the dbt platform's robust APIs to create, read, update, and delete (CRUD) projects/jobs/environments project. The dbt platform Administrative API and Terraform provider facilitate programmatic access and configuration storage. While the Discovery API offers extensive metadata querying capabilities, such as job data, model configurations, usage, and overall project health. StarterEnterprise
• Catalog: Use Catalog to view your project's resources (such as models, tests, and metrics) and their lineage to gain a better understanding of its latest production state. (Once you have a successful job in a Production environment). StarterEnterprise
• dbt Semantic Layer: The dbt Semantic Layer allows you to define universal metrics on top of your models that can then be queried in your business intelligence (BI) tool. This means no more inconsistent metrics — there's now a centralized way to define these metrics and create visibility in every component of the data flow. StarterEnterprise
• dbt Mesh: Use dbt Mesh to share data models across organizations, enabling data teams to collaborate on shared data models and leverage the work of other teams. EnterpriseEnterprise +

Additional help

• dbt Learn courses: Access our free Learn dbt video courses for on-demand training.
• dbt Community: Join the dbt Community to connect with other dbt users, ask questions, and share best practices.
• dbt Support team: Our dbt Support team is always available to help you troubleshoot your dbt issues. Create a support ticket in the dbt platform and we'll be happy to help!
• Account management Enterprise accounts have an account management team available to help troubleshoot solutions and account management assistance. Book a demo to learn more. EnterpriseEnterprise +

Summary

This guide should now have given you some insight and equipped you with a framework for moving from self-hosted dbt to the dbt platform. This guide has covered the following key areas:

• Considerations: Understanding the foundational steps required for a successful migration, including evaluating your current setup and identifying key considerations unique to your team's structure and workflow needs.

• Plan you move: Highlighting the importance of workflow redesign, role-specific responsibilities, and the adoption of new processes to harness the dbt platform's collaborative and efficient environment.

• Move to the dbt platform: Linking to the guide that outlines technical steps required to transition your dbt project to the dbt platform, including setting up your account, data platform, and Git repository.

• Test and validate: Emphasizing technical transitions, including testing and validating your dbt projects within the dbt platform ecosystem to ensure data integrity and performance.

• Transition and training: Share useful transition, training, and onboarding information for your team. Fully leverage the dbt platform's capabilities, from development tools (dbt platform CLI and Studio IDE) to advanced features such as Catalog, the Semantic Layer, and Mesh.

What's next?

Congratulations on finishing this guide, we hope it's given you insight into the considerations you need to take to best plan your move to the dbt platform.

For the next steps, you can continue exploring our 3-part-guide series on moving from self-hosted dbt to the dbt platform:

Guide	Information	Audience
Move to the dbt platform: What you need to know	Understand the considerations and methods needed in your move to the dbt platform.	Team leads Admins
Move to the dbt platform: Get started	Learn the steps needed to move to the dbt platform.	Developers Data engineers Data analysts
Move to the dbt platform: Optimization tips	Learn how to optimize your dbt platform experience with common scenarios and useful tips.	Everyone

Why move to the dbt platform?

If your team is using self-hosted dbt today, you could be reading this guide because:

• You've realized the burden of maintaining that deployment.
• The person who set it up has since left.
• You're interested in what the dbt platform could do to better manage the complexity of your dbt deployment, democratize access to more contributors, or improve security and governance practices.
• You need a governed data foundation for AI—shared definitions, lineage, and testing so analytics and AI give answers the business can trust.

Self-hosting hides its true cost in engineer hours and wasted compute. dbt platform eliminates that overhead with managed infrastructure and browser-based development so more people can contribute without you being the bottleneck.

State-aware orchestration is now dbt State

dbt State works with all engines and environments: dbt v1, the dbt platform, and dbt v2.

If you were using state-aware orchestration prior to June 1, 2026, you can continue using it. Once you start your free dbt State trial, it will be extended beyond the standard 30-day period. If the extension isn't applied to your account, contact your account team. To get started, refer to Migrate from state-aware orchestration.

The data layer is the AI layer—make sure it's tested, defined, and trusted end to end.

Moving to the dbt platform simplifies workflows by providing a fully managed environment that improves collaboration, security, and orchestration. With the dbt platform, you gain access to features like cross-team collaboration (dbt Mesh), version management, streamlined CI/CD, Catalog for comprehensive insights, and more — making it easier to manage complex dbt deployments and scale your data workflows efficiently.

It's ideal for teams looking to reduce the burden of maintaining their own infrastructure while enhancing governance and productivity.

Related content

• Learn dbt courses
• Book expert-led demos and insights
• Work with the dbt Labs' Professional Services team to support your data organization and migration.
• How the dbt platform compares with self-hosted dbt for a detailed comparison of self-hosted dbt and the dbt platform.
• Subscribe to the dbt platform RSS alerts

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