Give your team the
security-first path to building quality data products

Yes. Datacoves is an alternative to dbt Cloud, now the dbt Platform. It runs Managed dbt and Managed Airflow in your own cloud, with VS Code in the browser, pre-built CI/CD pipelines, secrets management and governance guardrails. Teams pick it when a SaaS deployment doesn’t fit: private deployment in their own cloud, data residency rules, or a security review that rules hosted tooling out. See the dbt Cloud comparison.

Snowflake, Databricks, BigQuery, Redshift and others. Datacoves works with any warehouse that has a dbt adapter, open source or proprietary.

If you need more time to evaluate, please reach out to us.

Two: multi-tenant SaaS, and Private Deployment inside your own AWS, Azure or GCP account. Private Deployment adds enterprise SSO, AWS Secrets Manager integration and enterprise Git, and keeps your data in infrastructure you control. If neither shape fits what you need, talk to us.

Yes. The SaaS free trial runs for 14 days, and you can enable whichever components you need during it rather than taking a fixed bundle: Managed dbt, Managed Airflow, VS Code in the browser, CI/CD and the rest. If you aren’t sure what fits, get in touch and we’ll help you set it up. If you need Datacoves deployed in your own network instead, there may be a setup charge.

No. Airflow orchestrates the whole pipeline, triggering tools like Azure Data Factory, Fivetran or your own load scripts before transformation, and BI refreshes, reverse ETL or ML steps after it. The VS Code environment runs any Python library, so dbt models and the API scripts that feed them live in one place. dbt is one step in that sequence rather than the boundary of what Datacoves runs.

Start with one team and one pipeline rather than a platform-wide mandate. Put the transformation logic in Git with tests running in CI, prove it on something the business cares about, and let the people who did it make the case to everyone else. Setup friction is what usually stalls this rather than the ideas: days lost to local installs, version drift, environments that differ per developer. A preconfigured environment removes that part and leaves you with the habits to change, which is the work that matters.

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.

It depends which path you’re on. On SaaS the environment exists as soon as you sign up, so teams are developing the same day and shipping pipelines within days. A Private Deployment with a Datacoves Foundation engagement, the usual shape for a large enterprise, typically delivers an MVP in about six weeks of build time, with your engineers writing code in the environment well before that.

Your bill will be pro-rated for the days the user was active on the platform.

Yes. Enable the components your team needs and leave the rest. Most teams run Managed dbt and Managed Airflow with VS Code in the browser, and keep the ingestion and BI tools they already use rather than replacing them. Nothing requires you to adopt the whole stack at once, and pricing is per user with no cap on how many users you add.

No. You’ll get an MVP that uses the standard functionality of dbt, Airflow, your data warehouse, and other existing tools. You can reuse all artifacts (including your governance rules) even if you decide not to move forward with Datacoves.

No. Datacoves uses standard dbt, Airflow, and VS Code functionality. Your code is always yours. If you ever choose to move off Datacoves, you can take your code and run it on another platform.

Yes. Datacoves connects to your existing warehouse, enterprise Git, identity provider, ingestion tools and CI/CD, so the systems that already work keep working. The platform fills the gaps between them instead of replacing them.

Yes, and it doesn’t require your analysts to become software engineers. Version control, testing, CI/CD and orchestration come configured, so the team adopts the workflow rather than building it. Where this fails is a rollout that changes every pipeline at once; where it works, one project goes first and the rest follow it.