This is usually argued as cost versus convenience, which is the wrong axis. The real question is where your source code and secrets are executed, and who else can reach that machine. For most teams a managed service is the correct answer and the debate should end there. The option people forget is the hybrid: a managed control plane driving runners inside your own network, which answers almost every compliance objection without putting you in the business of operating a CI platform.
Search this question and you will find comparison tables about pricing tiers, concurrency limits and YAML syntax. Those are real differences and they are not the decision. The decision is architectural, and it is this: when your pipeline runs, your entire source tree is checked out onto some machine, your deployment credentials are injected into it, and arbitrary code from your repository executes there. The only question that matters is whose machine that is.
Everything else — price, features, queue times — follows from that choice rather than driving it.
What "managed" actually means
A managed CI service runs two things for you. The control plane schedules work, stores configuration, holds your secrets and shows you logs. The runners are the machines that check out your code and execute the build.
In a fully managed setup both live with the provider. Your repository is cloned onto their infrastructure, your signing keys and cloud credentials are decrypted into that environment, and your test suite — plus every dependency it pulls — runs there. This is not a criticism. It is simply what you are agreeing to, and for the large majority of teams it is a sensible trade: you get a maintained, scalable, well-secured platform and you stop thinking about build infrastructure.
The option most comparisons leave out
The framing as a binary is what makes this question feel harder than it is. Every major platform supports a middle configuration: their control plane, your runners.
You keep the scheduling, the UI, the logs and the upgrade burden with the vendor. You register your own machines — in your data centre, your cloud account, your VPC — and those machines do the actual checkout and execution. Your source tree never lands on hardware you do not control. GitHub Actions calls these self-hosted runners, GitLab has its own runner model, and several platforms are built around this split deliberately.
When self-hosting the runners genuinely earns its keep
Not as a default, and not as a cost play. There are five situations where it is the right call:
Regulated or licensed material in the build
If the build needs a proprietary SDK you are licensed to run only on your own hardware, or test fixtures that are hard to fully synthesise, or an HSM for signing, the build has to happen where those things live. This is the most common legitimate driver in banking work.
Hardware the provider does not offer
Specific GPUs for model tests, particular macOS versions for a mobile pipeline, a device lab, an unusual architecture. Hosted fleets cover the common cases well and the uncommon ones not at all.
Network-reachable dependencies
Integration tests that must reach an internal database, a mainframe, or a service with no public endpoint. You can tunnel from a hosted runner, and that tunnel is now a permanent hole in your perimeter that exists for the convenience of your CI provider.
Data residency as a contractual term
Not a vague preference, but a clause saying code and data stay in a named jurisdiction. Runners in your own region settle it cleanly.
Sustained, predictable, heavy compute
Per-minute pricing is excellent for spiky workloads and poor for a build farm running flat out all day. This is a real cost argument, but it is the last reason on the list rather than the first, and it only applies once your usage is large and steady.
What it costs you, including the part people miss
Running your own runners is not free just because the machines are already paid for.
- Someone owns the upgrade treadmill. Runner versions, base images, toolchains, operating system patches. This is nobody's favourite work and it decays silently.
- Queue management becomes your problem. Autoscaling, capacity at peak, the Monday-morning pile-up.
- Reproducibility erodes. A long-lived runner accumulates state. Builds start passing for reasons nobody can explain, and then failing for the same reasons. Ephemeral runners — a fresh container or VM per job — fix this and cost more.
And the one that matters most: the security of a self-hosted runner is now yours, and a runner is a high-value target. It holds deployment credentials and it executes code from your repository by design. On a shared or long-lived runner, one compromised dependency in one project can reach the secrets of every other project that uses it. Isolation between jobs, short-lived credentials, and a fresh environment per run are not refinements here — they are the baseline. Taking runners in-house and then operating them casually is a worse position than the managed service you left.
How to decide, in four questions
- Does anything in the build have to stay inside your network? Licensed SDKs, non-public services, residency clauses, signing hardware. If no, stop — use the managed service.
- If yes, is it only the execution that must move? Almost always. Then take the hybrid: their control plane, your runners.
- Do you also have a reason the control plane cannot be external? Air-gapped environments and a few regulated contexts genuinely do. Most organisations that believe they do have not checked whether a hybrid satisfies the actual requirement.
- Who owns the runners on an ordinary Tuesday? If there is no name, you are choosing an outage in six months. Answer this before you build anything.
Our default recommendation to clients is the managed service, and we say so even though self-hosted pipeline work is something we are paid to do. It is the right answer for most teams and pretending otherwise would be selling. The hybrid is what we reach for when a bank's build has to touch something that cannot leave the building — which, in that sector, it usually does.
If you are weighing this for a specific pipeline, the useful exercise is listing what the build actually touches — not what the policy document says it touches. That list decides it in about an hour, and it is where we start on DevOps engagements.