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dbt Labs
Transformation tool and platform for analytics engineering, used to build tested data models in the warehouse. The company behind dbt.
Analytics engineering teams that model data in a warehouse and need tests, docs, and a shared project.
Teams that need a human labeling queue, or groups with no warehouse and no plan to get one.
Verdict
dbt is a great fit for analytics and data engineering teams that need modeled, tested warehouse tables other systems can trust. Core open source is free; Cloud pricing is in a familiar mid band for analytics tooling once the team is on shared projects. The community is deep and loyal. dbt is still the standard way to turn warehouse SQL into trusted models for AI and analytics alike.
Score Breakdown
How dbt Labs scores in the categories that matter to its buyers.
Pricing
dbt Cloud sells developer seats plus monthly allowances for successful models built and queried metrics. Starter is self-serve; Enterprise tiers are quoted. Prices below are USD from dbt Labs’ public rate card as of September 2026.
| Plan | Seats | Models / month | Price | What stands out |
|---|---|---|---|---|
| Developer | 1 | 3,000 | $0 | One project; browser IDE; no API access |
| Starter | Up to 5 | 15,000 | $100 / seat / mo | 5,000 queried metrics / mo; API access; one project |
| Enterprise | Custom | 100,000 | Sales quote | Up to 30 projects; Mesh, Canvas, advanced Copilot |
| Enterprise+ | Custom | 100,000 | Sales quote | Unlimited projects; PrivateLink; IP restrictions; hybrid |
The Field at a Glance
Where dbt Labs ranks among Data & labeling vendors we reviewed, by Overall Score and relative typical engagement cost.
dbt Labs scores 8.3, level with Pinecone (8.3) and ahead of Labelbox (7.9), Scale AI (7.4), Encord (7.3), and Gretel (7.1). Its relative typical engagement cost is among the lower options on this chart.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| Warehouse modeling | Strong | This is the job most teams hire dbt for. |
| Tested inputs for later model work | Strong | Tests on upstream tables give later model work cleaner inputs. |
| Human annotation | Poor | A labeling vendor covers this, not dbt. |
| No warehouse | Poor | dbt transforms data inside a warehouse, so it needs one. |
Review Excerpts
Excerpts from public reviews and forum discussions of dbt Labs. Paid reviews and pay-for-play sites were excluded.
“We used dbt to transform source data into data tables, push these data tables into our data warehouse, establish sources of truth for data, track data lineage / dependencies / downstream impacts, and as a source of truth for business logic (metric definitions, what data can be used for what, and so forth).”
“The prerequisite is that you have a supported database/data warehouse and have already found a way to ingest your raw data. Then dbt is very well suited to manage your transformation logic if the people using it are familiar with SQL. If you want to benefit from bringing engineering practices to data, dbt is a great fit. It can bring CI/CD practices, version control, automated testing, documentation generation, etc. It is not so well suited if the people managing the transformation logic do not like to code (in SQL) but prefer graphical user interfaces.”
“dbt is available in a free on-prem version which is what really accelerated our model building. The on-prem version allows us to work on the models in native SQL but is enriched with many useful features like automatically incrementing the timestamps. We are mainly using dbt to make good use of the transformation functionality of sql over the pyspark boilerplate atop of it.”
“The documentation could be a bit better, since a lot of the documentation is focussing on the dbt cloud plattform instead of the core version. As a core (on-prem) user you need to abstract the features that are available to you and take the information from the documentation.”
Company Facts
- Founded
- 2016 (CB Insights)
- Headquarters
- Philadelphia, Pennsylvania, United States (CB Insights)
- Total funding raised
- $415.9M (CB Insights)
- Key investors
- Fivetran, Andreessen Horowitz, Amplify Partners, Sequoia Capital, Altimeter Capital (CB Insights)
- Customers
- Sunrun, Conde Nast, JetBlue, Food Folk, McDonald's Nordics and Nasdaq (CB Insights)
- Website
- getdbt.com
Key Leadership
Tristan Handy
Co-founder and President
Methodology
This page is an independent evaluation of dbt Labs for buyers comparing options in data prep & pipelines. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. dbt Labs did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria fall under it in two groups.
- Buyer outcomes (for data prep & pipelines)
- Transformation models
- Tests and docs
- Project fit for analytics engineering
- Downstream AI data use
- Company & commercial
- Innovation & product leadership
- Project management & communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the use-case matrix above.
Scope
dbt Labs is graded here as data prep & pipelines. Criteria scores can move as more review volume and product checks are added.