terrateam VS dagger

Terrateam: After getting into some really big issues when running Terragrunt with Github Actions, I decided to look for a better CI solution. Terrateam is my CI/CD tool of choice here. Unfortunately as of December 2023, they increased their price from USD $175 to USD $496 monthly. Me being an existing customer I still pay the old amount (thank God!) Alternatively, you can look at solutions like Atlantis or spacelift.

Disclaimer: I am a co-founder of Terrateam (https://terrateam.io/) a competitor to Digger.

For anyone using Digger, are all of the enterprise features in the open source codebase? If you pay for digger, or you just paying for support and hosting? Or do they separate OSS from Community, Pro, and Enterprise plans?

Disclaimer: Co-founder of Terrateam.

For Terrateam[0], we have probably 70% of the enterprise offering but at around 1/10th the price. If there are any features that are deal breaker, feel free to reach out to me and we'll see what we can do. That being said, Spacelift is a much more luxurious piece of software than us. We are very utilitarian, but we have to rationalize that low price-point somehow.

[0] https://terrateam.io

Terrateam is an enterprise Atlantis alternative that lets you plan and apply all within pull requests and very minimal setup. We have a pretty active slack if you have any questions.

> It sure looks like AWS admin credentials are shared with Github, and also available to anything else in the diggerhq/digger action

I am a co-founder of Terrateam[0] which is a Terraform CI/CD as well. At the end of the day, you need to execute something to do these operations and having this component open source is important for auditing purposes. For Terrateam, we lean heavily into GitHub Actions so GitHub is at least managing any secrets and runs. One challenge is users could pin the Action that we publish to a specific version, but we also update it regularly and communicating to customers to update it is a challenge.

[0] https://terrateam.io

There are lots of options for this. I am co-founder of a product called Terrateam which makes it easy to slice up a mono repo for these usecases. We specialize in GitHub installations. But Spacelift, env0, and Scalr exist as options as well.

Many of the CI/CD tools take care of this. Terrateam tracks which plans are still valid after an apply and will force a re-plan. It also ensures that only one apply happens at a time.

We just released our self-hosted version available here: https://github.com/terrateamio/terrateam

I use Terrateam (disclaimer, I'm also a co-founder of it) does PR-level locks (but only on apply, or merge), and does apply serialization, and invalidates outdated plans on apply. You can plan as much as you want in parallel, though, because that does not impact the output. I don't think there are many other options for how to handle this.

The fact that I couldn't point to one page on the docs that shows the tl;dr or the what problem is this solving

https://docs.dagger.io/quickstart/562821/hello just emits "Hello, world!" which is fantastic if you're writing a programming language but less helpful if you're trying to replace a CI/CD pipeline. Then, https://docs.dagger.io/quickstart/292472/arguments doubles down on that fallacy by going whole hog into "if you need printf in your pipline, dagger's got your back". The subsequent pages have a lot of english with little concrete examples of what's being shown.

I summarized my complaint in the linked thread as "less cowsay in the examples" but to be honest there are upteen bazillion GitHub Actions out in the world, not the very least of which your GHA pipelines use some https://github.com/dagger/dagger/blob/v0.10.2/.github/workfl... https://github.com/dagger/dagger/blob/v0.10.2/.github/workfl... so demonstrate to a potential user how they'd run any such pipeline in dagger, locally, or in Jenkins, or whatever by leveraging reusable CI functions that setup go or run trivy

Related to that, I was going to say "try incorporating some of the dagger that builds dagger" but while digging up an example, it seems that dagger doesn't make use of the functions yet <https://github.com/dagger/dagger/tree/v0.10.2/ci#readme> which is made worse by the perpetual reference to them as their internal codename of Zenith. So, even if it's not invoked by CI yet, pointing to a WIP PR or branch or something to give folks who have CI/CD problems in their head something concrete to map into how GHA or GitLabCI or Jenkins or something would go a long way

> GHA has "service containers", but unfortunately the feature is too basic to address real-world use cases: it assumes a container image can just … boot! … and only talk to the code via the network. Real world use cases often require serialized steps between the test & the dependencies, e.g., to create or init database dirs, set up certs, etc.)

My biased recommendation is to write a custom Dagger function, and run it in your GHA workflow. https://dagger.io

If you find me on the Dagger discord, I will gladly write a code snippet summarizing what I have in mind, based on what you explained of your CI stack. We use GHA ourselves and use this pattern to great effect.

Disclaimer: I work there :)

Dagger (https://dagger.io) is a great way to use BuildKit through language SDKs. It's such a better paradigm, I cannot imagine going back.

Dagger is by the same folks that brought us Docker. This is their fresh take on solving the problem of container building and much more. BuildKit can more than build images and Dagger unlocks it for you.

And suddenly, it's almost painfully obvious where all the pain came from. Cloud applications today are simply a patchwork of disconnected pieces. I have a compiler for my infrastructure, another for my functions, another for my containers, another for my CI/CD pipelines. Each one takes its job super seriously, and keeps me safe and happy inside each of these machines, but my application is not running on a single machine anymore, my application is running on the cloud.

That said I've been moving my CI/CD to https://dagger.io/ which has been FANTASTIC. It's code based so you can define all your pipelines in Go, Python, or Javascript and they all run on containers so I can run actions locally without any special setup. Highly recommended.

You are right make is arcane. But it gets the job done. There are new exciting things happening in this area. Check out https://dagger.io.

> but I'm not convinced it's ready to replace Gitlab CI.

The purpose of Dagger it's not to replace your entire CI (Gitlab in your case). As you can see from our website (https://dagger.io/engine), it works and integrates with all the current CI providers. Where Dagger really shines is to help you and your teams move all the artisanal scripts encoded in YAML into actual code and run them in containers through a fluent SDK which can be written in your language of choice. This unlocks a lot of benefits which are detailed in our docs (https://docs.dagger.io/).

> Dagger has one very big downside IMO: It does not have native integration with Gitlab, so you end up having to use Docker-in-Docker and just running dagger as a job in your pipeline.

This is not correct. Dagger doesn't depend on Docker. We're just conveniently using Docker (and other container runtimes) as it's generally available pretty much everywhere by default as a way to bootstrap the Dagger Engine. You can read more about the Dagger architecture here: https://github.com/dagger/dagger/blob/main/core/docs/d7yxc-o...

As you can see from our docs (https://docs.dagger.io/759201/gitlab-google-cloud/#step-5-cr...), we're leveraging the *default* Gitlab CI `docker` service to bootstrap the engine. There's no `docker-in-docker` happening there.

> It clumps all your previously separated steps into a single step in the Gitlab pipeline.

This is also not the case, we should definitely improve our docs to reflect that. You can organize your dagger pipelines in multiple functions and call them in separate Gitlab jobs as you're currently doing. For example, you can do the following:

```.gitlab-ci.yml

Check out https://dagger.io/. Write declarative pipelines in code, reproducibly run anywhere.

Is this similar to Dagger[1] ?

[1] https://dagger.io