Buying snapshot
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| Decision point | What dbt Labs states |
|---|---|
Vendor | dbt Labs announces on its site that Fivetran and dbt Labs have merged. |
Where you develop | The plan comparison lists a browser-based studio IDE, the dbt command line interface (CLI) and a VS Code extension. dbt Labs publishes dbt Cloud CLI binaries from its dbt-cli GitHub repository. |
Data platforms | dbt Labs says dbt "runs on every major data platform" and works "on your machine or in dbt platform"; its product page says you can "Connect to any data platform." |
dbt version upgrades | Developer: the "Latest" release track. Starter: the "Latest" and "Compatible" tracks. Enterprise: all release tracks. Enterprise+: all release tracks, including rollbacks. |
Support | Developer and Starter list a 24x5 support level. Enterprise lists priority support with SLAs. On Enterprise and Enterprise+, onboarding and professional services are available to purchase. |
Our take
dbt Cloud fits a team that already models its data in dbt and wants scheduling, CI checks and a shared metric layer hosted for it. If one project and Starter's included monthly builds cover your work, the published Starter rate makes the cost easy to plan; if not, treat Enterprise as the realistic starting point.
Evaluate dbt Cloud
Comparisons
Where it stands out
Scheduling and CI on the free plan. The Developer plan already includes the browser-based IDE, job scheduling and monitoring, and CI checks on GitHub and GitLab. One developer can run a real production schedule before anyone pays.
Shared metric definitions without Enterprise. Teams that want one definition of revenue or churn can get the dbt Semantic Layer on Starter, so they do not need an Enterprise contract for it.
Builds only what changed. The dbt State add-on, in dbt Labs' words, "builds only what changed and skips the rest by reusing, cloning or auto-deferring." It can be bought on its own and run against self-hosted dbt, or added to a dbt platform plan.
Product screenshot
Trade-offs to weigh
Build volume, not only seats, sets the plan. Each plan includes a set number of successful models built per month. Running a large project many times a day uses that amount faster than adding people does, so compare your monthly build count with each plan's included amount before choosing.
Several domain teams means Enterprise. If you plan to split work across teams with dbt Mesh and multi-project lineage, budget for Enterprise; the lower plans keep you to one project.
Security requirements put the price behind a sales call. If your company requires SSO, SCIM or private networking, the plans that include them have custom pricing, so you learn the cost only after talking to sales.
Part of the bill moves with usage. After their trials and included credits, dbt State and dbt Wizard are billed by consumption, so the monthly cost changes with how much you run.
Pricing and plan conditions
Starter is the only paid plan with a published rate, charged per user per month, so a Starter bill grows with the number of users. The add-ons sit outside seat pricing: dbt Labs describes them as "No seats, no minimums," and they can be added to a dbt platform plan or bought on their own for self-hosted dbt. For dbt Wizard you can instead bring your own key (BYOK) and pay your AI provider directly.
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| Plan | Published price | What it adds |
|---|---|---|
Developer | Free | One developer seat; 3,000 successful models built per month; 1 project. Browser-based IDE, MFA, job scheduling and monitoring, CI checks on GitHub and GitLab, automatic upgrades to the latest dbt release. |
Starter | $100 per user/month | Five developer seats; 15,000 successful models built per month; 5,000 queried metrics per month; 1 project. All features in Developer, plus lineage with dbt Catalog, the dbt Semantic Layer and API access. |
Enterprise | Custom pricing | Custom developer seat count; 100,000 successful models built per month; 20,000 queried metrics per month; 30 projects. All features in Starter, plus column-level lineage, governed metrics with caching, multi-project architecture (dbt Mesh), SSO, RBAC and SCIM, and priority support with SLAs. |
Enterprise+ | Custom Pricing | Custom developer seat count; 100,000 successful models built per month; 20,000 queried metrics per month; unlimited projects. All features in Enterprise, plus PrivateLink, IP restrictions and Hybrid Projects. |
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| Plan | dbt Wizard | dbt State |
|---|---|---|
Developer | Free $100 Wizard credit for a 30-day free trial, then pay-as-you-go | 30-day free trial, then pay-as-you-go |
Starter | Free $100 Wizard credit for a 30-day free trial, then pay-as-you-go | 30-day free trial, then pay-as-you-go |
Enterprise | $100/mo in Wizard credits per account; additional usage billed as a consumption add-on | 30-day free trial; additional usage billed as a consumption add-on |
Enterprise+ | $200/mo in Wizard credits per account; additional usage billed as a consumption add-on | 30-day free trial; additional usage billed as a consumption add-on |
Pay-as-you-go rate | Per-token pricing that differs by AI model | $0.094 / DATT (each daily unique reuse of a model, test or snapshot); custom pricing for upfront commits through sales |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
0 GitHub commits 90d12 GitHub stars0 vulnerabilities across 1 package
See all signals from 5 sources
PyPI · dbt-core@1.12.6
Alternatives to dbt Cloud
The reviewed substitutes for dbt Cloud among the transformation frameworks, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Dataform
- Two transformation frameworks modelling data inside the warehouse with version control, tests and documentation. They are compared directly, differ on SQL authoring style and state handling, and a team standardises on one.Applies to: Choosing the framework that will model and test data inside the warehouse.
- dbt (data build tool)
- dbt (data build tool) and dbt Cloud build SQL models in the warehouse from the same dbt project code. The open-source framework runs from the command line on infrastructure the team sets up, while dbt Cloud is the managed service from dbt Labs that adds a web editor, job scheduling and hosted documentation.Applies to: Choosing between two products of the same kind for one job.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Cube
- Cube and dbt Cloud can both host the shared metric definitions that BI tools and AI agents query. Cube is built around an open-source semantic layer with caching and APIs for applications, while dbt Cloud is mainly a transformation platform whose semantic layer is defined next to a team's dbt models.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.
Related technologies
Normally used together rather than chosen between, so these are not alternatives.
- Apache Airflow
- A transformation framework models data inside the warehouse; an orchestrator schedules work and coordinates it across tools. dbt Core ships no scheduler and is run from a DAG, which is why the published guidance says to use both. The reader's question is which layer does what.Applies to: Whether a transformation framework needs an orchestrator, or replaces one.
Head to head
See detailed alternatives analysis
dbt Cloud and Prefect address different parts of a data workflow. dbt’s platform centers on analytics development: teams write SQL models, use version control and CI/CD, schedule workflows, test data, inspect lineage, and define consistent metrics through dbt Semantic Layer for dashboards or LLMs. Prefect is a workflow orchestration platform for data, ML, and agents that uses ordinary Python functions decorated as flows and tasks.
The commercial models also differ. dbt lists a free Developer plan with one Developer seat, one project, and 3,000 successful models built per month. Its Starter plan is listed at $100 per user per month and includes five Developer seats, one project, 15,000 successful models built per month, and 5,000 queried metrics per month. Prefect Cloud offers a free Hobby tier, while its paid plans separate a $100-per-month Starter plan from a $100-per-user-per-month Team plan. The useful choice is therefore driven by whether the immediate need is SQL-centered transformation and governed metrics, or durable orchestration of Python workflows across infrastructure.
Top Alternatives Overview
Prefect is the directly documented alternative in this comparison. Prefect describes its platform as durable orchestration for data, ML, and agents. A workflow can be written as a Python function with the @flow decorator; Prefect adds state, logging, retries, scheduling, caching, and recovery. Its product page also describes task-level tracking and the ability to execute flows on user infrastructure, scale out, and retry failures.
Prefect offers managed and hybrid execution options. Prefect Serverless runs flows on managed compute. For teams that need execution to remain in their own environment, Prefect describes a hybrid model in which lightweight workers run code inside the team’s environment while the control plane supplies scheduling and visibility. Its pricing page describes custom work pools as infrastructure the customer configures beyond the managed default.
Prefect Cloud’s free Hobby tier includes two users, up to five deployments, 500 minutes of Prefect Serverless, workflow scheduling, logging and alerting, and seven days of run retention. Starter is listed at $100 per month and includes three users, up to 20 deployments, 75 hours of Prefect Serverless, bring-your-own compute, webhooks, and a 1,250-requests-per-minute API limit. Team is listed at $100 per user per month for four to eight users, up to 100 deployments, 225 hours of Prefect Serverless, service accounts, and 24-hour audit-log retention.
Enterprise has custom pricing and includes SAML/OIDC SSO, RBAC plus ACLs, multiple workspaces, SCIM directory sync, IP allowlisting, PrivateLink, and unlimited deployments. Buyers considering Prefect should confirm the plan’s billing term, included Serverless allowance, deployment limit, work-pool requirements, and any custom Enterprise terms.
Architecture and Approach Comparison
dbt presents its platform as a data control plane for analytics workflows. Its transformation experience is based on writing SQL data models, with version control and CI/CD used to keep pipelines moving. The platform also lists orchestration, observability, Catalog, Semantic Layer, and Mesh among its workflow experiences. dbt Catalog provides lineage and metadata context; dbt’s pricing page distinguishes lineage with dbt Catalog from column-level lineage on Enterprise. dbt Semantic Layer defines and queries metrics on Starter, and dbt’s product page says the Semantic Layer can deliver consistent metrics to dashboards or LLMs.
For operations, dbt lists Git version control, testing, job scheduling, source-freshness reporting, job monitoring and alerts, and advanced CI. Its paid-plan structure adds access controls and governance at higher tiers: Enterprise lists SSO, role-based access control, and SCIM provisioning, while Enterprise+ lists PrivateLink, IP Restrictions, and Hybrid Projects that run dbt in the customer’s environment. dbt also lists Browser-based IDE, CLI, and VSCode extension as build experiences.
Prefect approaches the workflow layer through Python. The product page says ordinary Python functions can become durable, observable workflows using decorators. It describes flows that can branch, loop, and create tasks at runtime, with durable execution handling retries, state, and crash recovery. Prefect’s documented deployment options include fully managed Prefect Cloud, managed Prefect Serverless compute, and execution in a customer VPC through hybrid workers. Its control plane provides scheduling and visibility, while workers execute code where the data lives.
This makes the products complementary in documented scope: dbt supplies SQL transformation, analytics metadata, lineage, and governed metrics, while Prefect supplies Python-based orchestration for workflows that can include data, ML, and agent workloads. Whether a team adopts one, the other, or both depends on its own integration and operating requirements; the published pages do not establish a one-size-fits-all architecture.
Pricing Comparison
Both vendors publish entry-level plans, but the published units and included allowances are not identical.
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| Product | Published plan | Published price and term | Included published limits |
|---|---|---|---|
| dbt | Developer | Free | One Developer seat, one project, and 3,000 successful models built per month |
| dbt | Starter | $100 per user per month | Five Developer seats, one project, 15,000 successful models built per month, and 5,000 queried metrics per month |
| dbt | Enterprise | Custom pricing | Custom Developer-seat count, 30 projects, 100,000 successful models built per month, and 20,000 queried metrics per month |
| dbt | Enterprise+ | Custom pricing | Custom Developer-seat count, unlimited projects, 100,000 successful models built per month, and 20,000 queried metrics per month |
| Prefect Cloud | Hobby | Free forever | Two users, five deployments, 500 minutes of Prefect Serverless, and seven-day run retention |
| Prefect Cloud | Starter | $100 per month | Three users, 20 deployments, 75 hours of Prefect Serverless, and three custom work pools |
| Prefect Cloud | Team | $100 per user per month | Four to eight users, 100 deployments, 225 hours of Prefect Serverless, and unlimited custom work pools |
| Prefect Cloud | Enterprise | Custom | Five or more users, unlimited deployments, custom Serverless allowance, and annual billing |
The dbt Starter amount is a per-user monthly rate; it should not be treated as a total monthly price for a five-seat team. dbt’s Enterprise and Enterprise+ pages name custom pricing but do not publish a currency-denominated price or contract term. They do list different project limits—30 for Enterprise and unlimited for Enterprise+—plus higher-tier capabilities such as column-level lineage, governed metrics with caching, and Hybrid Projects on Enterprise+.
Prefect’s Starter rate is a monthly plan price, whereas Team is a per-user monthly rate. Prefect lists Enterprise as custom and identifies annual billing in its plan comparison, but it does not publish an Enterprise amount. A buyer should confirm the intended user count, deployments, Serverless-compute allowance, custom-work-pool needs, billing term, and the custom Enterprise price before comparing an organization-wide cost.
When to Consider Switching
Consider Prefect when the workflow requirement is centered on Python orchestration rather than only SQL transformation. Prefect describes a model where Python functions become flows and tasks with state, logging, automatic retries, scheduling, caching, and recovery. It also says workflows can branch, loop, and create tasks at runtime. Those documented characteristics are relevant when a team wants to coordinate data, ML, or agent workflows alongside its existing code.
Deployment requirements are another decision point. Prefect Serverless offers managed compute, while Prefect’s hybrid model allows workers to execute code inside the customer environment or VPC. Prefect says the control plane remains responsible for scheduling and visibility in that model. Teams that need external HTTP triggers can evaluate webhooks on Starter; those needing automation identities can evaluate service accounts on Team; and teams that require SAML/OIDC, SCIM, IP allowlisting, or PrivateLink can evaluate Enterprise.
Stay with or prioritize dbt when SQL transformation, analytics metadata, and consistent metric definitions are central. dbt lists SQL-model development, testing, orchestration, Catalog lineage, and dbt Semantic Layer. The Semantic Layer is specifically described as defining consistent metrics for dashboards or LLMs. Starter includes dbt Catalog lineage, Semantic Layer metrics, API access, and five Developer seats; Enterprise adds column-level lineage, governed metrics with caching, and multi-project architecture.
Cost evaluation should use the stated units. dbt Starter is $100 per user per month, while Prefect Team is also $100 per user per month. Prefect additionally publishes a free Hobby tier and a $100-per-month Starter plan. The published plan pages do not establish a universal savings result, so teams should map users, projects or deployments, model-build and metric allowances, Serverless usage, and security requirements to the applicable plan.
Migration Considerations
A move from dbt Cloud to Prefect changes the documented workflow surface rather than simply exchanging equivalent plans. dbt lists a transformation environment with SQL models, Browser-based IDE, CLI, VSCode extension, Git version control, testing, job scheduling, source-freshness reporting, job monitoring and alerts, and advanced CI. It also lists Catalog and Semantic Layer capabilities. Teams should inventory which of those experiences are in active use, including dbt Catalog lineage, metrics defined through dbt Semantic Layer, API access, release-track requirements, and higher-tier access controls.
Prefect’s documented workflow construct is a Python function decorated with @flow. Its pages describe automatic retries, state, logging, scheduling, caching, recovery, and task tracking. A migration design can therefore evaluate how existing scheduled work will be represented as Python flows, how it will execute through Prefect Serverless or custom work pools, and whether it must instead run through hybrid workers inside the customer environment. The pricing page defines a deployment as a server-side definition of a flow that can be scheduled, triggered, and run remotely, which makes deployment counts a practical sizing input.
Operational controls require separate review. Prefect lists webhooks on Starter, service accounts and 24-hour audit-log retention on Team, and SAML/OIDC, RBAC plus ACLs, SCIM, IP allowlisting, PrivateLink, multiple workspaces, and unlimited deployments on Enterprise. dbt lists MFA across its plans, API access on Starter, SSO/RBAC/SCIM on Enterprise, and PrivateLink, IP Restrictions, and Hybrid Projects on Enterprise+. Equivalent outcomes should be validated feature by feature rather than assumed from a plan name.
The published pages do not provide a migration duration, a dbt-to-Prefect conversion procedure, or a statement that dbt models run unchanged under Prefect. Plan a scoped technical assessment before changing production workflows: identify the Python flow boundary, execution environment, schedules and triggers, observability requirements, security controls, plan limits, and the custom terms needed for either vendor’s enterprise offering.