Case Studies

Explore how Craft Cloud powers scalable, on-demand builds with Dagger. Lead Cloud Engineer Jason McCallister shares how Dagger enables programmable, efficient deployments and database backups for Craft CMS projects, streamlining workflows across the cloud.

Learn how OpenMeter transformed its CI/CD pipelines with Dagger, solving common developer challenges like environment inconsistencies and onboarding friction. Márk Sági-Kazár, Head of Open Source at OpenMeter, shares his journey of using Dagger Functions and Dagger Cloud to streamline production pipelines and boost developer productivity.

Learn how software engineer Emmanuel Sibanda resolved the “works on my machine” problem and sped up his CI pipeline using Dagger. By integrating Dagger Functions, Emmanuel improved consistency across environments and reduced development friction for his technical interview prep application.

Discover how Dinesh Majrekar, CTO of Civo, transformed his team’s CI/CD pipelines by migrating to a monorepo and using Dagger to cut build times from 30 to 5 minutes. In this case study, learn how Dagger empowered Civo’s developers to independently manage and test their pipelines using Go, dramatically improving their internal developer experience.

Discover how Augustin Lafanechère and Conor Barber of Airbyte transformed their complex CI system by leveraging Dagger to improve developer experience, reduce costs, and boost performance. In this case study, learn how they streamlined a sprawling web of GitHub Actions, YAML, and shell scripts into efficient, code-driven CI pipelines with Dagger, cutting CI pipeline costs by 75% and achieving a 2-5x speed improvement.

Learn how Kasper Hermansen, Platform Engineer at Lunar, leveraged Dagger to replace unreliable shell scripts with a more reliable, consistent CI/CD process, transforming Lunar’s developer experience. In this case study, discover how Dagger’s Go SDK allowed Kasper’s team to streamline their platform tooling, improve build reliability, and enhance collaboration across the company.

Learn how Sean Clayton transformed his team’s CI pipeline by moving away from complex YAML configurations and adopting Dagger for a faster, more reliable process. In this case study, discover how Dagger improved local testing, reduced build times, and boosted developer confidence by enabling consistent local and remote CI pipelines.​

See how Tony Worm, creator of the Hof code generation framework, improved test consistency and accuracy by using containerized environments orchestrated by Dagger. In this case study, learn how Dagger enabled Tony to simplify Hof’s test suite, eliminate test environment conflicts, and implement matrix testing across multiple Go versions.

Discover how James Qualls, Principal Engineer at Discern, used Dagger to build a sophisticated CI/CD system that deploys ephemeral copies of Discern’s entire application for every pull request. In this case study, learn how Dagger enabled Discern to parallelize builds, reduce build times by 85%, and seamlessly deploy approved builds to production in just minutes.

Discover how Flipt transformed their CI/CD pipeline using Dagger, reducing build times from 10 minutes to 17 seconds on subsequent runs. In this case study, learn how Dagger’s Go SDK enabled Flipt to write native Go tests, streamline containerized integration testing, and leverage caching to achieve a 97% speed improvement in their CI pipeline.​

Discover how Kevin Minehart, a software engineer at Grafana Labs, reduced package build times by 75% using Dagger, cutting down the process from over an hour to just 8 minutes. In this case study, learn how Dagger enabled Kevin’s team to build customized Grafana packages both locally and remotely, improving performance, consistency, and developer productivity.

Learn how Andy Gonzalez, an SRE team leader, used Dagger to cut AWS Lambda deployment times from 1-3 hours down to just 3 minutes, achieving a 93% reduction in processing time. In this case study, discover how Dagger’s parallelized, matrix-based deployment pipeline simplified complex YAML configurations and enabled faster, automated deployments across multiple environments.