A note on terminology: dbt Labs now refers to dbt Core as self-hosted dbt and dbt Cloud as the dbt platform. As many organisations still know and search for these products as dbt Core and dbt Cloud, we’ll use those familiar terms throughout this article.
Choosing between dbt Core and dbt Cloud isn’t simply a question of comparing features.
dbt Core gives organisations significant control over how dbt is deployed and operated. For teams with established infrastructure, platform engineering capability and specific architectural requirements, that control can be valuable.
dbt Cloud takes a different approach, providing a managed environment with capabilities for developing, orchestrating, deploying and managing dbt projects. For some organisations, this can reduce the operational responsibilities surrounding dbt and provide a more standardised environment for teams to work in.
Neither approach is inherently right for every organisation.
What we should be asking is: which operating model best fits your requirements, capabilities and priorities?
When dbt Core Makes Sense
dbt Core can be an effective choice when an organisation has good reasons to retain control over its environment.
You may prefer dbt Core if you have established infrastructure and DevOps capabilities, specific deployment or architectural requirements, or an existing environment that operates efficiently.
It can also make sense when your team values the flexibility to build and manage the surrounding tooling itself and has the capacity to maintain it.
If your dbt Core environment is working well and continues to meet the organisation’s requirements, growth alone isn’t necessarily a reason to move.
When dbt Cloud Makes Sense
For other organisations, the question may be less about retaining control and more about where they want to direct their resources.
Operating dbt Core can involve managing infrastructure, orchestration, CI/CD, environments, access, monitoring, upgrades and other capabilities around the dbt project.
As adoption grows, organisations may consider dbt Cloud if they want to reduce some of this operational responsibility, provide a more standardised environment for contributors or consolidate more of their dbt development and deployment processes within one platform.
Those potential benefits still need to be weighed against the cost, effort and implications of changing an existing environment.
7 Considerations When Comparing dbt Core and dbt Cloud
1.
Control
Start by considering how much control your organisation needs over its infrastructure and operating environment.
dbt Core provides considerable flexibility over how and where dbt is run. This may be important if you have specific architectural, infrastructure, security or deployment requirements.
If your organisation doesn’t require that level of control, consider whether managing it provides enough value to justify the effort involved.
2.
Internal Capability
Having the technical capability to operate dbt Core is different from deciding that your organisation should continue owning that capability.
Teams with established platform engineering and DevOps functions may already have the skills, processes and infrastructure required to operate dbt Core effectively.
Other organisations may prefer dbt Cloud if reducing the amount of specialist operational knowledge required around dbt better aligns with their operating model.
3.
Engineering Capacity
Consider where you want your engineers spending their time.
Running dbt Core involves responsibilities beyond developing models. Infrastructure, deployment workflows, orchestration, upgrades, monitoring and troubleshooting all require attention.
That isn’t necessarily inefficient. For some teams, these responsibilities fit naturally within their role.
For others, reducing this operational workload may allow engineering capacity to be directed towards modelling, data quality, analytics and new data products.
The question should focus on whether that effort represents the best use of your team’s capacity.
4.
Scale & Collaboration
As more people contribute to dbt, the requirements surrounding development can change.
Consider how effectively your current environment supports multiple contributors, development environments, testing, deployment and collaboration.
If your dbt Core processes continue to scale effectively, there may be little reason to change them.
If each stage of growth requires additional tooling, infrastructure or processes, it may be worth comparing that operating model with what dbt Cloud provides.
5.
Governance and Organisational Requirements
Security, access, auditability and governance matter regardless of which approach you choose.
With dbt Core, organisations can design these controls around their own infrastructure and existing technology environment.
dbt Cloud provides platform capabilities that may allow some of these requirements to be managed within the dbt environment itself.
What you should be asking is: which approach best fits your organisation’s particular security, compliance and governance requirements.
6.
Total Operating Cost and Effort
Licence cost alone doesn’t provide a complete comparison.
For dbt Core, consider the infrastructure, supporting tools and internal resources required to operate and maintain the environment.
For dbt Cloud, consider subscription costs alongside migration, implementation, integration and ongoing administration.
A fair comparison should look at the total operating model, including both direct expenditure and internal effort.
Deciding Between dbt Core vs dbt Cloud
| Consideration | dbt Core may suit you when… | dbt Cloud may suit you when… |
| Control | Infrastructure and deployment control is important | You don’t need to manage as much of the operating environment yourself |
| Internal capability | You have established skills and processes to operate dbt Core | You want to reduce operational responsibilities surrounding dbt |
| Engineering capacity | Managing the environment is an appropriate use of your team’s resources | You want to direct more capacity towards data development |
| Scale | Your existing environment and processes continue to scale effectively | Growth is increasing the effort required to manage your environment |
| Governance | Your existing controls and tooling meet your requirements | You want to consolidate more capabilities within the dbt environment |
| Commercial | Your infrastructure and internal operating costs remain appropriate | The managed model provides sufficient value to justify its cost |
Assess Before Making A Decision
If your dbt Core environment is reliable, appropriately governed and cost-effective, and your team has the capacity and capability to operate it, continuing with dbt Core may be the appropriate decision.
There is little value in migrating simply because another option exists.
Equally, if maintaining the environment is consuming increasing resources, limiting collaboration or no longer aligns with how your organisation wants its data team to operate, there may be a business case for considering dbt Cloud.
The decision should be driven by what you need to improve.
Leaning towards a migration to dbt Cloud?
Before deciding whether to move from dbt Core to dbt Cloud, establish what you are trying to achieve.
Review your current architecture, operating effort, dependencies, workflows and costs. Identify what is working well and where genuine constraints exist. Then consider whether dbt Cloud addresses those constraints sufficiently to justify a change.
You may conclude that dbt Core remains the right fit.
You may identify improvements that can be made without migrating.
Or you may determine that moving to dbt Cloud provides a better operating model for where your organisation is heading.
Helping Organisations Get More From dbt
Data Army is a dbt Visionary Consulting & Services Partner, the highest tier of partnership within the dbt Partner Program. As a dbt-certified organisation, we bring deep expertise in analytics engineering, implementing and optimising dbt for our clients, and helping organisations advance their modern data stack with scalable, trusted data practices.
We also use dbt extensively within Data Army’s own data environment, so our team has first-hand experience of the platform and the same practical considerations our clients encounter when implementing, managing and scaling dbt.
Considering the Move to dbt Cloud?
See how we’ve helped others make the move, or learn how to manage your own migration to dbt Cloud.
See a dbt Cloud migration in action
See how Data Army helped Australian digital home loan platform and financial technology company, Lendi, migrate to dbt Cloud, creating a trusted data foundation for its AI-native future.
How to migrate to dbt Cloud
Our practical guide steps through how to move to dbt Cloud while managing dependencies, reducing disruption and taking the opportunity to build a more scalable, easier-to-manage environment.
*dbt Labs now refers to what many teams know as dbt Core as self-hosted dbt, while dbt Cloud is now the dbt platform.
You can read more about the distinction between self-hosted dbt and the dbt platform here.