Updating dbt Cloud pricing to support long-term community growth
Cast your mind back to January of 2020. Pre-pandemic. Pre- involuntary global homeschooling. Pre- a lot of things that you may wish to leave far in your rear view mirror.
For us, it was pre-dbt Labs! We were still a small company of about 20 humans called Fishtown Analytics. We earned our living by doing analytics consulting in the modern data stack and had a small handful of engineers working on building dbt, which earned just north of $0 / year.
In January of 2020, we launched the dbt Cloud IDE. Prior to that, dbt Cloud had exclusively been focused on executing dbt jobs, but we had heard repeatedly that learning the command line was too big of a leap for many organizations who wanted to deploy dbt to tens or hundreds of data practitioners. Because dbt has always been about empowerment, we decided to lean into this, to make the development experience more turnkey. No pip install dbt or git push origin main but all of the same power of the analytics engineering workflow.
With the launch of the IDE, we updated our pricing. We charged fifty whole dollars a month for a user! No platform fee, no metering on job execution, just a per-user charge. Drew and Connor and I came to that decision with literally zero analytical rigor—we just wanted to unlock the analytics engineering workflow to as many humans as possible. A couple of Stripe configs later and it was done. The total dbt Cloud user base at the time was maybe a couple hundred companies.
And that is where things have stood for just shy of three years. So much of dbt’s growth—from 1,000 to 17,000 active deployments and 4,000 to 50,000 community members!—has happened while the pricing for our most widely-used plan has remained untouched. In this post I’m going to explain why it’s time for an update—and why that’s a good thing for the long-term success of the dbt Community.
Catching up to the value of dbt Cloud
We’re increasing the price of the dbt Cloud Team plan from $50/seat to $100/seat.
The value dbt Cloud provides has grown significantly in the last 3 years. Since January of 2020, when pricing was last updated, we’ve added:
- 35+ new ecosystem integrations
- The Metadata API
- Slim CI
- DAG visualization in the IDE
- Auto-complete in the IDE
- The Model Timing tab
- Environment variables
- Support for GitLab and AzureDevOps
- Merge conflict resolution
- Granular permissions for service tokens
- TriggerRun API Endpoint
- 10x faster scheduler startup speed
- 25x faster IDE startup speed
- The dbt Semantic Layer
As the product has made dramatic improvements, though, it’s been incredibly clear that $50/user/month was no longer the correct price point for the Team plan. We actually get community members reaching out to us concerned that we are under-charging them because they want our business to be successful! This is definitely the first time in my career that I had ever heard feedback like this.
So why haven’t we updated pricing in all this time? Well… because pricing work is hard! And scaling to support a community that’s grown from 50 to 50,000 in 5 years hasn’t left us with a lot of time for much else.
But it’s also because we still had a few things left to prove—to ourselves and to you. We had to prove dbt Cloud could improve collaboration, without sacrificing speed or quality. We had to prove it could help teams grow while continuing to support them through that growth. And we had to prove that this way of working would pay dividends for data teams over a multi-year time horizon—all without creating cost barriers to forward-thinking teams ready to try something new.
With 3,000+ data teams relying on dbt Cloud for production use cases today, dbt Cloud has proven its value. But that doesn’t mean our work is done. We’re on a journey to build a sustainable business around dbt to enable us to invest in and steward the dbt Community over the very long term.
In support of this, as of today all new subscriptions to the dbt Cloud Team plan will be priced at $100 per user per month. Existing dbt Cloud customers will be migrated to new pricing on February 1, 2023.
Right tool for the job
We’re limiting Developer and Team plans to one project (for new customers), with an eight user max on the Team plan (new and current customers).
In the last year, it’s become increasingly clear that the way we package dbt Cloud plans under each tier of service was generating confusion. Community members asked “Which plan is right for me?” while employees asked “When should I direct a customer to which plan?” We had simply never done a good job of making it clear who dbt Cloud’s three different tiers were for and what their intended use case was. It’s time to fix this.
Moving forward, we want to ensure that: 1) each tier serves a very distinct purpose, making it easy for users to choose, and 2) each tier represents a distinct point on the analytics engineering maturity curve.
What we’ve realized over the past few years of working closely with clients as they scale from very modest to very complex dbt deployments is that complexity is directly related to the number of humans participating in the analytics engineering workflow at a given company. Few humans == lower overall complexity. More humans == higher overall complexity.
That may seem obvious, but take it a single step further. There are real milestones in an organization’s analytics engineering journey: from one person to one team to multiple teams. And each milestone represents a real discontinuity from a complexity perspective.
The same is true of software engineering. Imagine a founder CTO, hacking together a project by herself. Often this codebase moves quickly, but isn’t architected to support additional software engineers. Scaling this to a team requires some new practices—better tooling, documentation, clearly-defined interfaces between subsystems. Complexity increases again when you have multiple teams. Each team wants to own their own subsystems and codebases, and these have to be able to evolve independently. You have to step up your software architecture and tooling again in order to support this.
We’re refocusing dbt Cloud’s three tiers to focus on these points along the analytics engineering maturity curve. Here’s how we visualize it:
Let’s go a level deeper. Here’s how we think about dbt Cloud’s three tiers and what changes we’re making to them.
Who the Developer plan is for
Single individuals learning dbt or operating very basic data stacks. We want these users to get the entire dbt Cloud experience, scaled down to the needs of a single user.
What’s included / what’s changing
The Developer tier can be used by a single user. As of today, new developer tier accounts will be limited to a single dbt project.
Note that this tier will be free forever—we care tremendously about giving users the widest and smoothest possible onramp to getting started with the analytics engineering workflow.
Who the Team plan is for
Single teams operating data stacks of modest complexity. We want every small team doing analytics engineering work to grow up on dbt Cloud.
What’s included / what’s changing
As of today, new Team tier accounts can be used by up to eight users—the canonical “two pizza team” popularized in industry lore—for a single dbt project.
Who the Enterprise plans are for
Organizations looking to deepen investment in mission critical work, or drive consistency across teams.
What’s included / what’s changing
Includes all the functionality required for sophisticated implementations of dbt. This announcement does not impact any plan on the Enterprise tier.
Being the commercial maintainer of a popular and influential open source offering is a real responsibility, and it’s one that I take incredibly seriously. I can’t even begin to tell you how many customer and community conversations, how many hours of work went into this change. Creating vs. capturing value is always a balancing act in this role, and it’s baked deeply into our values where we want to land.
I feel strongly that this move will help us continue to build the business that will steward dbt and the dbt Community over the longest-possible time horizon. If you have any questions at all, please don’t hesitate to reach out in dbt Slack—my DMs are always open.
Thanks, as always, for being a part of this journey.
Last modified on: Nov 29, 2023