Managed dbt that lets you focus on data

While Datacoves is not open source, it is built on open components to which we also contribute. There is no vendor lock-in as customers can recreate what we have done, but trust us, this is not simple.

Datacoves is an end-to-end data engineering platform that unifies dbt, Airflow orchestration, Python development, governance, and best practices. It can be deployed in a customer’s environment and provides a secure, managed workspace.

Yes. dbt v2, the engine that grew out of Fusion, runs on Datacoves, and so does the official dbt VS Code extension. We can set it up for any customer who wants it: dbt v2’s license permits managed platforms to distribute it, which Fusion’s Elastic License 2.0 did not, so this stopped being a licensing question at dbt v2’s GA in September 2026. It isn’t the default, so you can stay on dbt v1 (dbt Core) and move when your adapters and packages are ready. The Snowflake, BigQuery, Databricks, Redshift and DuckDB adapters are GA; Spark is in beta and ClickHouse in private beta.

Datacoves users follow standard dbt development practices. New features are created on a branch, they are then pushed to a git server like GitHub where they are tested. Once the change is approved, the branch is merged into the repo’s main branch. The main branch is picked up by Airflow and jobs run as scheduled.

Datacoves runs on Kubernetes, either inside your own cloud account on AWS, Azure or GCP, or as multi-tenant SaaS. A private deployment installs into the account you already have and needs no VPC peering.

DIY gives you flexibility but costs you time, consistency, and institutional knowledge. Teams that go that route spend months wiring together Airflow, dbt, Python environments, secrets management and CI/CD, then more months maintaining it as versions change. Datacoves delivers all of that preconfigured and managed.

Pricing has a few components rather than one. On SaaS you pay for users, for the instances of the tools you run, and for worker minutes. Private Deployment drops the worker minutes, since the compute is yours, so it bills on users and instances. There’s no cap on how many users a team adds on either plan, and both include unlimited projects and environments. See plans for what each includes, or talk to us for a figure.

Yes. Datacoves has helped large organizations manage tens of dbt projects in a single organization, with hundreds of developers, hundreds of Airflow DAGs, and thousands of dbt models.

Managed dbt and Managed Airflow, VS Code in the browser with your own extensions and Python libraries, pre-built CI/CD, secrets management and governance guardrails, all running against your warehouse. You bring your own ingestion and BI tools, or use the ones Datacoves manages. The product pages cover each part in turn.

Datacoves eliminates the complexity of building and maintaining dbt, Airflow, Python, CI/CD, and secrets management. It provides a unified, secure, governed environment.

Every customer gets onboarding, architecture guidance and a shared success channel with our team rather than a ticket queue. Support covers how you use the platform as well as whether it is running: dbt modelling questions, Airflow DAG design, CI/CD setup and version upgrades. Teams that want help building the first version of a platform can start with a Datacoves Foundation engagement, which delivers an MVP in about six weeks of build time and leaves them owning everything it produces.

Analytics engineers, data engineers and platform teams use Datacoves day to day, in the browser-based VS Code environment and in Airflow. The people who buy it are Heads of Data, Directors of Analytics, VPs and CTOs at large enterprises, most often in regulated industries: pharma and life sciences, healthcare, financial services, retail, manufacturing and government. Teams tend to come to us when a SaaS-only tool can’t meet a security or data residency requirement, or when running dbt and Airflow themselves has turned into its own project. See case studies for how teams use it.