Buying snapshot
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| Decision point | What dbt Labs states |
|---|---|
License | The repository source code is licensed under the Apache License 2.0, a no-charge, royalty-free copyright license. |
Installation | dbt v2.0 ships as a single self-contained binary with no Python runtime or dependency management. It and its drivers support macOS and Linux on x86-64 and ARM, and Windows on x86-64; Windows on ARM is not yet supported. |
Ways to run it | Self-host dbt, or use a hosted dbt platform plan. The Developer plan is for individual developers getting started without managing infrastructure, and it upgrades to the latest dbt release automatically. |
Data handling | dbt Labs says dbt never stores your data. |
Support | Community help in the dbt Community Slack and Discourse; open an issue to report a bug or request a feature. The plan comparison lists a 24x5 support level for Developer and Starter and priority support for Enterprise and Enterprise+; the Enterprise card says priority support with SLAs. Enterprise and Enterprise+ list onboarding and professional services available to purchase. |
Our take
Choose dbt when people who write SQL own your warehouse transformations and you want those changes versioned, tested and reviewed like application code. Self-host it if your team will run the environment itself; choose a hosted plan if you would rather not manage infrastructure.
Evaluate dbt (data build tool)
Popular comparisons
See all 12 dbt (data build tool) comparisons
Where it stands out
Software practices for SQL work. Models build on one another, and dbt manages and shows those relationships and tests your transformations. dbt Labs describes pipelines as modular, tested and version-controlled, with local validation that catches issues before they reach the warehouse.
A stricter, more inspectable v2.0. The Rust rewrite enforces a tightly defined language specification at parse time and writes Parquet artifacts you can query, while still producing JSON artifacts such as manifest.json for backwards compatibility. dbt Labs says v2.0 parses, compiles and runs projects in a fraction of v1's time.
Shared metric definitions. The dbt Semantic Layer defines consistent metrics and delivers them to dashboards or LLMs. Defining and querying metrics is listed from the Starter plan, and Enterprise adds governed metrics with caching.
Product screenshot
Trade-offs to weigh
Two licenses to check. The source code is Apache 2.0, but dbt Labs also releases dbt as a distribution of the repository with dbt-specific customizations under a separate dbt product license. Confirm which build you install and read its terms.
A major version change is under way. The main branch now holds dbt v2.0, a ground-up rewrite in Rust, while v1 Python development continues on the 1.latest branch. Decide which version your project targets before you standardize.
Security and governance controls sit in custom-priced plans. SSO, RBAC and SCIM, column-level lineage and dbt Mesh are listed from Enterprise. PrivateLink, IP restrictions and Hybrid Projects, which run dbt in your own environment, are listed only for Enterprise+.
Pricing and plan conditions
Add-ons are billed separately, with no seats and no minimums, and can run against self-hosted dbt or a platform plan. dbt State bills each daily unique reuse of a model, test or snapshot as a Daily Active Target Table (DATT): $0.094 per DATT pay-as-you-go, with a 30-day free trial and free monthly usage available, and custom pricing for upfront commits. dbt Wizard uses per-token pricing that differs by AI model, or you can bring your own key and pay your AI provider directly. Developer and Starter get a free $100 Wizard credit for a 30-day trial, then pay as they go. Enterprise includes $100 a month and Enterprise+ $200 a month in Wizard credits per account, with extra usage billed as a consumption add-on.
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| Plan | Published price | Seats, projects and monthly limits |
|---|---|---|
Developer | Free | One developer seat, 1 project, 3,000 successful models built per month. |
Starter | $100 per user/month | Five developer seats, 1 project, 15,000 successful models built and 5,000 queried metrics per month. |
Enterprise | Custom pricing | Custom developer seat count, 30 projects, 100,000 successful models built and 20,000 queried metrics per month. |
Enterprise+ | Custom pricing | Custom developer seat count, unlimited projects (Custom in the plan comparison table), 100,000 successful models built and 20,000 queried metrics per month. |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
1.0k GitHub commits 90d14.1k GitHub stars0 vulnerabilities across 1 packageOpenSSF score 6.0/10
See all signals from 7 sources
PyPI · dbt-core@1.12.6
Alternatives to dbt (data build tool)
The reviewed substitutes for dbt (data build tool) 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.
- Coalesce
- 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.
- 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.
- SQLMesh
- 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 Cloud
- 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.
- Apache Airflow
- Mostly deployed together — Airflow schedules, dbt models — but a team whose only orchestration need is running dbt models can use dbt Cloud's scheduler instead of Airflow, or Airflow instead of paying for it. dbt's own published analysis makes that case.Applies to: Orchestrating dbt runs, where the choice is dbt Cloud's built-in scheduler against a general-purpose orchestrator that also coordinates ingestion and ML pipelines.
- Bruin
- dbt (data build tool) and Bruin both define SQL transformations as version-controlled, tested code that builds tables in the warehouse. dbt covers transformation only, while Bruin adds ingestion, Python assets and scheduling in the same project.Applies to: Building tested SQL models in a cloud warehouse.
- Matillion
- dbt (data build tool) and Matillion both transform data inside cloud warehouses such as Snowflake, BigQuery and Redshift. dbt is an open-source SQL framework for analytics engineers who work in code with version control and tests, while Matillion is a commercial platform with a visual job designer that also loads the data, which suits teams that prefer building pipelines on a canvas.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.
- Prefect
- Python-native workflow orchestration with managed cloud control planeApplies to: Whether a transformation framework needs an orchestrator, or replaces one.
- Fivetran
- The two sit at different layers of one system and the documented deployments run them together, so the reader's question is which job each one does rather than which to buy.Applies to: Whether these two do the same job, or different jobs in one pipeline.
- Databricks
- A transformation framework models data inside whichever platform holds it, and the platform provides the compute and storage it runs on. Platforms also offer native transformation, so the reader's question is whether a separate framework is needed rather than which to buy.Applies to: Whether warehouse transformation needs its own framework, or the platform's native tooling is enough.
- Dagster
- Asset-centric data orchestrator with built-in lineage, observability, and dbt integrationApplies to: Whether a transformation framework needs an orchestrator, or replaces one.
- Snowflake
- A transformation framework models data inside whichever platform holds it, and the platform provides the compute and storage it runs on. Platforms also offer native transformation, so the reader's question is whether a separate framework is needed rather than which to buy.Applies to: Whether warehouse transformation needs its own framework, or the platform's native tooling is enough.
Head to head
See detailed alternatives analysis
dbt (data build tool) alternatives should be evaluated by product role, architecture, pricing, public adoption signals, and operational trade-offs—not category proximity alone. dbt (data build tool) is a SQL-based transformation framework for building modular warehouse models with testing, version control, and CI/CD practices. Its open-source repository uses the Apache-2.0 license, has 13,789 stars, and released v1.12.3 on August 21, 2026. The best alternative depends on whether the immediate gap is data ingestion, workflow orchestration, or managed dbt operations rather than SQL transformation itself.
Top Alternatives Overview
Airbyte
Airbyte is an open-source ELT platform focused on moving data from operational sources into warehouses, lakes, databases, and vector stores. Its key differentiator from dbt (data build tool) is its Data Replication Engine: Airbyte handles batch and CDC replication, while dbt transforms data after it is available in the destination platform. Airbyte offers 600+ connectors, a connector development kit, and deployment choices spanning self-hosted and cloud environments; its Agent Engine also supports direct connector operations for AI-agent and real-time-system use cases. Teams gain source connectivity and ingestion flexibility, but they do not gain dbt’s warehouse-modeling workflow, SQL testing approach, or semantic layer. Choose this if ingestion coverage, CDC replication, or self-hosted connector control is the primary problem—not if replacing SQL transformation is the goal.
Apache Airflow
Apache Airflow is an Apache License 2.0 open-source workflow orchestration platform for authoring, scheduling, and monitoring pipelines through Python-based DAGs. Compared with dbt (data build tool), Airflow operates at the workflow-control layer: it can coordinate tasks across Google Cloud Platform, Amazon Web Services, Microsoft Azure, and other third-party systems rather than concentrating on warehouse-resident transformation models. Its web application provides operational visibility into pipeline status and logs, while standard Python supports dynamic task generation and scheduling logic. Airflow gives teams broad orchestration control, but it requires Python workflow engineering and does not provide dbt’s SQL modeling conventions, built-in transformation tests, or semantic metric definition. Choose this if complex, multi-system workflow scheduling is more important than a dedicated analytics-engineering transformation framework.
dbt Cloud
dbt Cloud is the managed control-plane option for teams that want dbt workflows without operating the surrounding platform components themselves. It adds managed workflow automation, collaboration capabilities, and cloud-hosted operational features to dbt’s transformation approach; the public documentation also identifies orchestration, observability, and a semantic layer as core capabilities. dbt Cloud includes an accessible visual path through dbt Canvas, while the VS Code extension brings Fusion-engine capabilities such as live error detection, fast parsing, and lineage insights into VS Code or Cursor. The trade-off is commercial cost: user feedback specifically identifies per-seat pricing as a concern as developer counts grow. Choose this if your team wants to retain dbt’s SQL and governance model while reducing the operational burden of scheduling, documentation hosting, and managed development workflows.
Architecture and Approach Comparison
dbt (data build tool) is warehouse-centric ELT: analytics engineers define business logic in modular SQL models and run transformations in the cloud data warehouse. Its approach emphasizes version control, testing, CI/CD, documentation, and governed metrics; the newer Fusion engine and VS Code extension aim to improve local developer feedback and parsing performance. Airbyte sits earlier in the pipeline, replicating data through batch and CDC connectors into warehouses, lakes, databases, and vector stores. It is therefore complementary to dbt when a team needs both ingestion and warehouse transformation.
Apache Airflow is architecturally different again. Its Python DAGs coordinate and monitor discrete workflow tasks, making it suitable where scheduling and dependencies span cloud services and third-party systems. dbt Core users commonly need separate orchestration and CI/CD setup, which is a meaningful operational trade-off; Airflow addresses orchestration, but not dbt’s transformation discipline. dbt Cloud combines managed transformation workflows with orchestration, observability, and semantic-layer capabilities. We recommend dbt or dbt Cloud for SQL-centered warehouse modeling, Airbyte for source replication, and Airflow for cross-system workflow control.
Pricing Comparison
The three tools charge in very different ways. dbt (data build tool) is free for one Developer seat with 3,000 successful models a month; Starter is $100 per user per month for up to five seats, and Enterprise and Enterprise+ are custom priced. dbt State is billed at $0.094 per daily active target table (DATT), with 30 days free for eligible new organizations. Airbyte provides a free open-source self-hosted plan with unlimited connectors and 600+ connectors, while its Cloud Standard plan starts at $20/month with 5 credits included. Apache Airflow is free and open source under Apache License 2.0.
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| Tool | Pricing model | Known price details |
|---|---|---|
| dbt (data build tool) | Free Developer seat; per-seat paid plans | Developer free (1 seat); Starter $100/user/month (up to 5 seats); Enterprise and Enterprise+ contact sales; dbt State $0.094 per DATT |
| Airbyte | Freemium and open source | Self-hosted Free plan; Cloud Standard from $20/month; Pro and Enterprise Flex contact sales |
| Apache Airflow | Open source | Free under Apache License 2.0 |
| dbt Cloud | Freemium | dbt Core is free; Developer free (1 seat); Starter $100/user/month (up to 5 seats); Enterprise custom |
Pricing should not be treated as a simple product-cost comparison. Airbyte’s free self-hosted option changes the economics of connector-heavy ingestion, while Airflow’s software license does not remove the engineering responsibility of operating workflows. dbt Cloud can reduce platform operations, but per-seat costs are a documented concern for larger developer groups.
When to Consider Switching
Consider moving from dbt (data build tool) to Airbyte when data ingestion—not transformation—is blocking delivery. dbt is primarily focused on warehouse-centric ELT and is not designed to replace a platform that supplies batch and CDC replication from hundreds of sources. Airbyte is the stronger fit when connector breadth, self-hosted deployment, or replication into vector stores is required.
Consider Apache Airflow when the central need is programmatic orchestration across multiple systems. dbt Core requires separate orchestration and CI/CD setup, and Airflow provides Python DAGs, scheduling, monitoring, logs, and plug-and-play operators for major cloud platforms. This is not a reason to discard dbt’s model layer automatically; it is a reason to choose an orchestration-first architecture.
Consider dbt Cloud when the team values dbt’s SQL workflow but wants managed scheduling, documentation hosting, collaboration, and platform operations. Conversely, teams should be cautious about dbt Cloud where developer-seat growth makes pricing difficult to justify. dbt also requires SQL skills and is not ideal for teams seeking a purely visual tool, although dbt Canvas provides a drag-and-drop governed development interface.
Migration Considerations
Moving away from dbt (data build tool) starts with separating what dbt currently does: SQL models, tests, documentation, lineage context, version-control workflows, CI/CD, and any orchestration handled outside dbt Core. SQL compatibility is the first technical constraint because dbt models run inside cloud warehouses; moving to Airbyte does not translate or replace those transformations because Airbyte’s role is replication. A transition to Apache Airflow requires translating execution dependencies into Python DAGs and deciding where warehouse SQL, validation, and deployment conventions will live.
A move to dbt Cloud is operationally simpler in one sense because the underlying dbt transformation approach remains central, but teams should still assess managed workflow configuration, user access, pricing exposure, and how they will use orchestration and observability features. For any path, inventory data formats and destinations, source-to-target dependencies, warehouse-specific SQL, tests, documentation requirements, and CI/CD ownership. Complexity rises with the number of models, external task dependencies, custom SQL patterns, and governance expectations.