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Operate First is an open community cloud model that brings operating software into the open-source development process. Projects run workloads in a shared environment; operators and contributors can learn from what happens and feed operational experience back into the software. It has also served as a peer-learning setting for SRE practitioners. The available accounts describe a historical implementation, not a confirmed service you can join today.
What does “operate first” mean?
In a conventional development cycle, operations can be treated as a downstream handoff: developers produce software, then operators deploy and maintain it. Operate First’s premise is to bring deployment, operation, testing, and operational feedback into the development process instead.
That extends open-source collaboration beyond source code. FOSDEM describes the approach as opening operational procedures and data, while the Linux Foundation characterizes the community cloud as a peer-to-peer mentoring environment for running software and learning SRE practices. The question at its center is: “But how do we open-source the operation of software?” FOSDEM’s 2023 session description frames the community as a place where experienced SREs can share production practices and newer practitioners can work with cloud-native projects.
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How the community cloud creates a feedback loop
- Deploy a project. A workload runs in a shared environment rather than remaining only in a development or test setup.
- Observe its behavior. Operators and users can examine operational experience and, where made available, telemetry such as logs and metrics.
- Discuss and change. Contributors use what they learn to inform operational procedures and software changes.
- Feed lessons upstream. Operational knowledge can flow back into the project’s code and practices, connecting running the software with improving it.
Red Hat Research’s explanation of “Ops is the new code” describes real workloads, telemetry, operational feedback, and infrastructure-as-code as parts of this relationship. This describes an approach and its intended learning loop; it is not quantitative proof that a particular deployment becomes more reliable or that software quality improves by a measured amount.
#1 Best Overall
What the historical implementation used
The technology details below come from accounts published in 2021. They are useful as a reference for how the community was described at the time, not as a current inventory.
| Area | What the historical account says | Source and date |
|---|---|---|
| Orchestration and deployment | Kubernetes, with ArgoCD and GitOps practices discussed for deployment and operational changes | Linux Foundation webinar, recorded October 5, 2021 |
| Monitoring | Prometheus was identified among the technologies used | Linux Foundation webinar, recorded October 5, 2021 |
| Cluster and hosting locations | The interview described an OpenShift-based environment with a bare-metal installation at Boston University, a cluster at Hetzner, and clusters in AWS | Open Sourcerers interview, August 30, 2021 |
| Change management and automation | Operational configuration changes were described as pull requests, with bot-assisted deployment; the webinar also discussed onboarding and back-office operations through GitOps and bot automation | Open Sourcerers interview, August 30, 2021; Linux Foundation webinar, recorded October 5, 2021 |
| Operational transparency | The interview said procedures were documented on GitHub and logs and metrics were publicly available at that time | Open Sourcerers interview, August 30, 2021 |
These examples show how an open project can make deployment changes reviewable and operational experience more visible. The accounts do not establish that the same tools, providers, access rules, or clusters remain in use.
Rank #2
What the published scale figures show—and do not show
The OpenInfra Foundation’s 2022 annual report called Operate First an OI Labs-incubated prototype and described its architecture as a blueprint for NERC. Its figures are a dated snapshot rather than current service statistics.
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| Measure reported | 2022 snapshot | Context |
|---|---|---|
| Community | Approximately 250 members | The report also named OKD, OS-Climate, and Apicurio as example projects |
| Projects | 50 | A project count in the 2022 report |
| Infrastructure | Five clusters, 71 nodes, and 1,582 cores | Counts from the report’s infrastructure snapshot |
| Services | 15+ | Backstage and OpenDataHub were named as examples |
Source: OpenInfra Foundation 2022 annual report. These counts do not establish the cloud’s present size, active workload roster, or performance.
Rank #3
What a team can learn from the model
For a team considering a similar community environment, the useful reference is not a particular vendor combination; it is the set of operational choices the model makes visible.
- Hosting and geography: Decide whether the environment is on-premises, hosted, or hybrid, and account for where infrastructure and participants are located.
- Transparency: Define which configuration, operating procedures, logs, and metrics participants can access, and under what rules.
- Change management: Consider whether pull requests, GitOps, and automation can make operational changes reviewable and reproducible.
- Observability and learning: Decide what signals are collected and how operators will turn experience into feedback for maintainers.
- Participation: Establish how workloads are onboarded, who maintains them, and how mentorship and operational responsibilities are shared.
The historical sources provide examples, not a universal deployment prescription. A community needs to make these choices according to its workload, capacity, and access requirements.
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Current service status, infrastructure, project roster, and onboarding availability are not confirmed by the available accounts. The August 2021 interview described a ticket-based onboarding route and pull-request changes to operational configuration, but that is historical guidance, not a current sign-up procedure. Check the project’s current official channels before assuming that tickets, repositories, logs, or metrics remain accessible.
Likewise, the evidence does not quantify improved reliability, software quality, or SRE career outcomes. The community and mentoring benefits are described as goals and experiences, not as measured results in the cited materials.
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