# DevSecOps platform Published articles for DevSecOps platform. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to calculate DevOps platform total cost of ownership DevFeed: [How to calculate DevOps platform total cost of ownership]() Original publisher: [Read original article]() Author: GitLab Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [GitLab]() Topics: [CI/CD]() Tags: [ai](), [ci-cd](), [complexity](), [compute](), [cost](), [devops](), [devsecops](), [devsecops-platform](), [drivers](), [how-to](), [infrastructure](), [integration](), [model](), [platform](), [reduce](), [software-delivery](), [tools]() ### AI overview A guide to modeling the total cost of ownership of a DevOps platform, including subscriptions, CI/CD compute, AI usage, infrastructure, tools, and internal labor. ### Source excerpt There's nothing like budget pressure to put your DevOps platform under a microscope. But subscription fees and license costs only tell one part of the story. The total cost of ownership (TCO) for a DevOps platform also includes variable costs like CI/CD compute and AI usage, along with the infrastructure, tools, and employee time required to keep software delivery moving. That wider view matters when you're tasked with defending platform spend or comparing options with a head of finance. Designing a useful TCO model can: Make those costs transparent for stakeholders Shine a light on the reasoning (or lack thereof) behind each cost Identify areas to reduce spend without negatively impacting software delivery What total cost of ownership really includes The core challenge of calculating TCO is that DevOps platforms package and price capabilities differently. For example, one platform may bundle CI/CD or AI capabilities into a per-seat subscription, while another could price usage separately. A third may appear less expensive upfront but require additional tools and ongoing integration work. That's why list prices or pricing tiers alone won't give you a useful comparison. Start with the capabilities and workloads your organization actually needs, then calculate what it takes to support them on each platform. Use the same scope and time period for every option -- often one year -- and define which teams, applications, environments, and delivery stages are included. Separate recurring costs from one-time expenses and external spend from internal labor, so finance can audit the assumptions and forecast future years. A useful TCO model, therefore, answers two questions: What does it cost to meet our requirements today? Which variables will cause that cost to rise or fall as our usage changes? The cost categories that drive your bill Most DevOps platform costs fit into the following categories: Cost categoryWhat it includesMain cost driverPlatform accessPaid seats, role-based ## Scale software delivery pipelines in isolation without owning the runner fleet DevFeed: [Scale software delivery pipelines in isolation without owning the runner fleet]() Original publisher: [Read original article]() Author: Kyurim Rhee Published: 2026-08-25T00:00:00Z Content type: article Language: en Sources: [GitLab]() Topics: [ci](), [GitLab](), [Software as a service](), [Security](), [cloud-infrastructure](), [Provisioning]() Tags: [agentic](), [ci](), [compliance](), [cost](), [developers](), [devsecops-platform](), [infrastructure](), [operations](), [platform](), [product](), [saas](), [scale](), [security](), [software-delivery](), [workflows]() ### AI overview GitLab presents Hosted Runners for GitLab Dedicated, a fully managed CI execution SaaS offering with isolated runner infrastructure. Each job runs in a newly provisioned VM that is deleted after completion, helping enterprises address security, compliance, scalability, reliability, and runner-operations demands without owning the runner fleet. ### Source excerpt Many enterprises choose GitLab Dedicated for a clear reason: a secure and compliant, single-tenant GitLab instance, managed by GitLab. As agentic workflows drive more pipeline volume, full data isolation and runner infrastructure operational burden become a challenge. This leads to the question, "Does owning the runner fleet still make sense?" With GitLab Dedicated, there is no more need to provision, patch, build, and scale your own runner fleet. Hosted Runners for GitLab Dedicated handles the heavy lifting and delivers a seamless experience across the entire software development lifecycle so your team can focus on shipping. Hosted Runners for GitLab Dedicated provides a runner fleet isolated from other customers. To support job-level security, each job runs in a newly provisioned, isolated VM that is deleted after the job completes. We provide a scalable and highly reliable runner fleet to support your bursty CI demands and resiliency needs. Creating and managing runners is self-serviceable through Switchboard (GitLab Dedicated management console). The runner ownership trade-off: Cost vs. user experience Pipeline load can be unpredictable. To account for this, runner infrastructure can be over-provisioned to give developers the best experience and maintain low wait times. But this drives up the infrastructure cost. Conversely, under-provisioning can save on infrastructure cost, but your developers will waste time waiting for their CI jobs to be picked up. Efforts to fine-tune this balance can monopolize your platform team's time. Shift runner operations to GitLab while maintaining control Hosted Runners for GitLab Dedicated is a fully managed CI execution SaaS offering. It's backed by a high-performing and reliable runner infrastructure. Key benefits include: Supports compliance: Full isolation/single-tenant runners with same data residency as GitLab Dedicated. Job level security: "One runner for one job" -- hosted runners run every job in a standalone, short-lived ## When code is abundant DevFeed: [When code is abundant]() Original publisher: [Read original article]() Author: Bill Staples Published: 2026-08-24T00:00:00Z Content type: opinion Language: en Sources: [GitLab]() Topics: [AI Development](), [Vibe coding](), [Software Engineering](), [GitLab](), [sdlc](), [anthropic](), [Concurrency](), [Graphs]() Tags: [ai](), [ai-agents](), [architecture](), [code](), [concurrency](), [devsecops-platform](), [enterprise](), [news](), [policy](), [software-development](), [workflow]() ### AI overview This opinion argues that AI agents and large language models are making code production cheaper and faster, shifting the main challenge for enterprise software development toward context, verification, governance, identity, policy, approval, and audit. It discusses how the software development lifecycle and its underlying architecture must adapt to machine-scale concurrency and automated workflows. ### Source excerpt I returned from the holiday break in January convinced that something fundamental had changed. Large language models had reached the point where they could produce useful code reliably enough, and cheaply enough, to change the economics of software development. Engineers everywhere seemed to be experimenting with the same thing: not just asking an AI assistant for suggestions, but giving agents real work and seeing how far they could take it. I started thinking about what happens if that continues. What changes when producing code is no longer the primary constraint in building software? I wrote those thoughts down in a board memo in January. In May, I published part of that thesis in GitLab's Act 2: the cost and time of producing software was collapsing, machines would increasingly build software under human direction, and the architecture underneath software development would have to change with it. In June, at GitLab Transcend, we showed the first pieces of that architecture: source control rebuilt for machine-scale concurrency, GitLab Orbit as a context graph spanning the software lifecycle, and governance around agent identity, policy, approval and audit. Then, on August 21, Anthropic published The AI-Native SDLC Playbook. It opens with a simple statement: "Code is no longer the bottleneck." I agree. Anthropic's playbook is a practical description of how the development lifecycle changes when agents can move implementation dramatically faster: planning becomes machine-readable, handoffs become automated, verification moves into the loop, and human judgment concentrates at the gates. What interests me is what happens one level beyond the workflow. If code is no longer the primary constraint, what becomes scarce? What architecture does an enterprise need when people, agents and multiple models are all acting across the software lifecycle at machine speed? And where does durable value move when generating the code itself becomes increasingly abundant? Over the pas