Datacoves gives us a managed Airflow on Kubernetes deployment without the operational burden of building and maintaining that infrastructure ourselves. We get full control over DAGs, connections, and variables, while Datacoves handles the underlying Kubernetes orchestration, scaling, and upgrades. The prewired integration between Airflow, dbt, and Snowflake removes a lot of the plumbing work you'd otherwise spend weeks on when standing up a similar stack from scratch. The in-browser VS Code environment is a nice touch, giving developers a consistent, ready-to-go setup (git, dbt, Python virtual environments, SQLFluff) without local machine configuration, and the Airflow UI itself is the standard, familiar interface, so there's no learning curve if you already know Airflow. We've built a fairly complex generic ingestion framework (S3 to Snowflake, incremental loads, SCD2, manifest and data file pairing) on top of it, and the platform has been a solid, stable foundation for that work. It's also flexible enough to support custom tooling on top, like a Streamlit-based troubleshooting frontend and custom email alerting for pipeline failures, without fighting the platform.

On the support side, we have weekly meetings with one of the co-founders, which has been genuinely useful, both for quick troubleshooting and for getting direct product input rather than going through a generic support queue. That level of access is unusual and has made onboarding and ongoing issue resolution noticeably smoother. Review collected by and hosted on G2.com.

One area with room to grow is My Airflow, the developer sandbox instance. It's useful for basic DAG testing, but bringing it closer to feature parity with Teams Airflow would make it a stronger environment for iterating on more complex DAGs before promoting to production. Review collected by and hosted on G2.com.

Datacoves gives us a really smooth managed experience for both Airflow and dbt. I’m a big fan of having dbt development, orchestration, Git, Python, and a VS Code-like workspace all in one place without needing to manage the underlying infrastructure ourselves. Review collected by and hosted on G2.com.

Airflow stability is a concern for me, we've faced some issues but the Datacoves team has always been extremely responsive and ready to help investigate and problem solve. Review collected by and hosted on G2.com.

Datacoves has made managing our data stack way easier. It brings dbt, Airflow, and a VS Code like environment together, so we don't have to deal with a bunch of separate tools or infrastructure.

It's also highly customizable, we recently added the Codex extension seamlessly

The developer experience is really solid, and getting started was pretty smooth. Their support team is also super responsive and helpful, which makes a big difference.

If you're looking to simplify your data workflows without managing everything yourself, it's definitely worth it. Review collected by and hosted on G2.com.

There's not much to dislike, The local Airflow environment ("My Airflow") is helpful for testing during development, but it still has some limitations. For example, triggering DAGs based on dataset events especially when those events need to come from external systems is not fully supported, which makes certain testing scenarios more difficult. Review collected by and hosted on G2.com.

I really like that working in Datacoves feels very familiar if you're used to VSCode. It makes dbt development very easy, and I appreciate the Airflow integration, which is nice. A major highlight is their support team, who are always quick to reply to issues, questions, and feature requests. Their documentation is very helpful for setting up different jobs or dbt configs. The initial setup was very straightforward, supported by detailed guides and quick assistance from the Datacoves team. Review collected by and hosted on G2.com.

The Airflow portion of the setup has been our biggest pain point. We've run into issues with the development area (called 'My Airflow') running out of memory, and the fact that it runs on a different executor than the production version (local file system vs Kubernetes) makes testing certain interactions a bit difficult. Review collected by and hosted on G2.com.

Before Datacoves, we were juggling multiple tools to manage dbt and orchestration — some hosted, some open source — and it created unnecessary complexity, especially as our team scaled and adopted multiple dbt projects. We knew we wanted the flexibility of dbt Core and Airflow, but building and maintaining that ourselves was complex.

Datacoves helped us streamline everything. They provided guidance on how to simplify our project structure, consolidate projects, and set up best practices that would’ve taken us longer to figure out on our own.

Now, every developer on our team has a consistent, ready-to-go workspace with dbt, Python, Git, and Airflow — all accessible through a browser-based VS Code. No local setup struggles, no flaky, and no confusion about where things run.

Their shared and user specific Airflow instances have been especially valuable. With separate dev and team environments, integrated Git syncing, and managed secrets, we’ve gained reliability without giving up flexibility.

Datacoves gave us the best of open source, with none of the overhead. And their team is genuinely helpful, they feel like an extension of our own. Review collected by and hosted on G2.com.

A bit of upfront planning needed to fully take advantage of the flexibility Review collected by and hosted on G2.com.

Their customer support is really open to improvements. I was contacted and I was able to discuss about the Grafana Dashboards that might be available in future releases. Review collected by and hosted on G2.com.

Sometimes some lack of dashboards like a Grafana integration to monitor activity would be useful to avoid over heading the customer support team in case of failures. It would be easier to control if we had a vision of how the Service is running and investigate bottlenecks or attention points. One case we have is when we, as a developer team, overload the Postgres running as background of Airflow. We do not have a clue when it will happen. Review collected by and hosted on G2.com.

Datacoves added copilot and model lineage right in vs code Review collected by and hosted on G2.com.

Provide more options to manage ingestion Review collected by and hosted on G2.com.

The datacoves product threads a really tight window of providing a solution which is relatively easy to get set up (versus implementing these components yourself) while still offering the flexibility to customize and layer your own technical architecture on top of their dbt/airflow/airbyte implementations. The team has been very responsive and supportive on assisting us in the implementation process. Review collected by and hosted on G2.com.

The trade-off of allowing for customization is that certain aspects which are "click button" in competing tool sets (ie. CI/CD for dbt) need to be implemented through your own tooling. Although it is really a benefit because you have tighter control over the processes, it does take some time to get everything implemented. Review collected by and hosted on G2.com.

Datacoves has accelerated our adoption of the modern data stack by providing a one stop shop of the most commonly used tools leveraged across the data and analytics industry.

The Datacoves team has been exceptional in the onboarding, consultation on best practices, customization, and providing troubleshooting assistance to our data engineering community for such a large scale adoption. Review collected by and hosted on G2.com.

The complexity of integrations and introduction of a suite of tools which are new to the organization can cause delays in wider adoption.

Organizations need to ensure the engineering skillset is ready to adopt the technology stack provided by Datacoves and fully understand the capabilities and best practices to realize the maximum value that can derived from the platform. Review collected by and hosted on G2.com.

Datacoves packages together all the tools needed to support data teams of all sizes. This isn't easy to do! If you don't use Datacoves, your options are to buy a disparate set of cloud based solutions, or hire engineering talent who can install it all on prem. Datacoves rises above both these options giving you both the advantages of the cloud and the best of open source.

The Datacoves team is immensely helpful getting everything off the ground in a fraction of the time it took me to install all this software on-prem on a prior project.

Hard to overstate how quickly Datacoves solved my infrastructure needs so I could focus on delivering business value.

Datacoves is also extremely responsive to customer questions, something you don't normally get when leveraging open source software. That's a huge hidden advantage to using their implementation of OSS instead of forging your own path. Review collected by and hosted on G2.com.

Maintaining and updating OSS tools is finnicky and can lead to extra cycles of development time getting software updated. Datacoves does an excellent job walking you through the changes, but if you haven't coded much at all before or are not comfortable with open source packages or GitHub, you will have a bit of a learning curve to overcome. Review collected by and hosted on G2.com.