2026 DevOps Survey

Mac Infrastructure in the AI Era

How DevOps leaders are scaling Mac CI/CD as AI reshapes build demand

AI isn't just changing how code gets written. It's changing how much Mac infrastructure it takes to ship it.

Teams building for Apple platforms are running an average of 81 physical Macs for CI/CD and employ around 20 engineers, yet the way they host, provision, and scale that hardware is fragmented, and the ground is shifting fast beneath them.

The accelerant is AI. Nearly nine in ten teams say pull-request and commit volume has risen since adopting AI coding tools, and over 80% say AI adoption has driven up their Mac infrastructure costs. AI is generating more code, more builds, and more pressure on the constrained Mac capacity that teams are already struggling to scale — turning an operational nuisance into a budget-line problem.

We asked 300 US-based DevOps, platform, infrastructure, and SRE engineers and leaders working at mid-market and enterprise organizations how their dev teams are building apps for iOS and macOS.

are using or exploring AI for dev workloads

have seen an increase in pull requests with AI

have seen an increase in infrastructure costs

anticipate Mac infra budget increase

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Today's Mac build landscape



How Mac CI is hosted and deployed



The real challenges of managing Mac infrastructure

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The AI adoption landscape



The AI demand story



How AI is reshaping DevOps



Looking ahead: The next 12 months

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Quick answers from the data

Today's Mac build landscape

The typical respondent runs a meaningful Mac fleet for a compact Apple engineering team.

Apple platform engineers

Teams building for the Apple ecosystem are fairly small: 80% sit between 5 and 50 engineers.

5–25 engineers55%

26–50 engineers25%

Fewer than 516%

More than 504%

Approximately how many software engineers at your organization build for Apple (iOS, macOS, etc.)?

Physical Macs for CI/CD

A slight majority of the teams surveyed run their CI pipelines on 26 or more Macs, with 3% building on 500+ machines.

26+ Macs · 55%

36%

40%

12%

<55–2526–100101–500501+

How many physical Apple Mac machines does your team currently use for CI/CD?

How Mac CI is hosted and deployed

Most teams already use cloud-hosted, managed-service, or hybrid Mac CI, with only 1 in 6 remaining purely on-premises. GitHub Actions is running in nearly a third of dev pipelines, with builds averaging between 20-40 minutes.

Mac CI hosting

Cloud is not the future; it is the present. Most teams (84%) already use cloud-hosted, managed-service, or hybrid Mac CI.

Cloud-hosted30%

Managed service29%

Hybrid25%

On-premises only16%

How is your Mac CI infrastructure currently hosted?

Environment provisioning

A near four-way split means there is no default way to provision a Mac build environment. Every team is solving this from scratch.

30%

27%

26%

17%

Fully managed CI-as-a-service — 30%Hybrid physical / virtual — 27%Dedicated hardware — 26%VMs on shared hardware — 17%

How does your team provision Mac environments for CI/CD?

The Mac dev toolkit

There is a lot of variety in Mac CI. Teams reported using everything from orchestration tools like Orka and Anka to CI-as-a-service platforms like Bitrise and CircleCI. GitHub Actions leads the top five.

GitHub Actions27%

Azure DevOps18%

GitLab CI17%

Xcode Cloud17%

VMware15%

Which tool(s) does your team use to build and manage Mac CI pipelines? (multi-select)

Build times

The majority of teams surveyed wait longer than 20 minutes for a build. Only 8% finish inside ten.

Over 20 min · 60%

32%

39%

18%

Under 10 min10–20 min21–40 min41–60 minOver 60 min

What is your average Mac CI build time, end-to-end (commit to completed run)?

The real challenges of managing Mac infrastructure

Teams aren’t drowning in build queues — only 6% report queues frequently impacting development. The burden is operational: procurement lead times, tool integration, security, and fleet management can all lead to lost developer productivity.

Build capacity vs. demand

Most teams have enough Mac infrastructure to meet demand, and 21% even report excess capacity. The constraint teams feel is not the number of Macs.

41%

30%

21%

Capacity generally meets demand — 41%We occasionally experience build bottlenecks — 30%We consistently have excess capacity — 21%Long build queues frequently impact development — 6%We have challenges managing our capacity — 2%

Which statement best describes your current Mac build capacity?

Infrastructure challenges

While current Mac supply chain issues are evident, there is no single dominant problem — teams struggle with everything from CI/CD tooling to Xcode versioning.

1Hardware procurement & lead times26%

2CI/CD tool integration25%

3Security & compliance23%

4Fleet management & provisioning22%

5Talent / skills gap22%

6Scaling capacity22%

7Environment fragmentation18%

8Cost17%

What are your biggest challenges in managing Mac infrastructure? (multi-select)

Developer productivity

As expected, consistency, reliability, and speed have the greatest impact on productivity.

Environment consistency19%

Test speed17%

Build reliability17%

Availability16%

Build time15%

Provisioning time15%

0%25%

Which metric has the greatest impact on developer productivity?

Time lost to Mac build issues

Two-thirds of teams lose at least an hour of developer time every week to Mac CI issues; nearly 1 in 4 lose six or more.

1 hour or more · 67%

30%

43%

20%

NoneUnder 1 hr1–5 hrs6–10 hrsOver 10 hrs

On average, how many developer hours per week does your team lose to Mac CI issues?

The copilot era is giving way to the agent era. And agents don’t wait politely for build capacity.

The AI adoption landscape

AI adoption is near-universal. Code creation agents (Claude Code, Cursor, Codex, etc.) are now the most-used capability, ahead of code completion assistants. Nearly half of organizations run at least some self-hosted or open-weight models, a workload with direct implications for Apple silicon infrastructure.

Where AI workloads are running

Nearly half run at least some self-hosted or open-weight models. That is a workload with direct implications for Apple silicon capacity.

Primarily hosted AI services (OpenAI, Anthropic, etc.)36%

Primarily self-hosted open-weight or private models24%

A mix of hosted and self-hosted models23%

Still evaluating options17%

Which best describes how your organization runs AI workloads related to software development?

AI capabilities in use

Agents have overtaken completion assistants as the most-used capability — a shift from suggesting code to producing and validating it.

Code creation agents (e.g. Claude Code, Cursor Agent)49%

Code completion assistants (e.g. GitHub Copilot, Tabnine)43%

AI chat used inside the IDE (e.g. Claude, ChatGPT)41%

Which AI capabilities are your development teams currently using? (multi-select)

The AI demand story

Since adopting AI coding tools, 9 in 10 teams have seen pull request and code commit volume increase. Additionally, more than 4 in 5 say AI adoption has increased Mac infrastructure costs. More code means more builds. More builds mean more Macs.

Volume of PRs and commits

90% of teams report more pull requests and commits. Every one of those extra commits needs a Mac to build on.

Increased significantly — 34%Increased moderately — 56%No change — 10%

Since your team adopted AI coding tools, how has the volume of pull requests and code commits changed?

Mac infrastructure costs

83% say AI adoption pushed Mac infrastructure costs up. The demand signal has already become a budget signal.

27%

56%

13%

Increased significantly — 27%Increased somewhat — 56%No change — 13%Decreased — 4%

How has AI adoption affected your Mac infrastructure costs?

How AI is reshaping DevOps

AI is changing the build process itself, not just authoring. Agentic CI/CD is the most prevalent use case. Governance is keeping pace unevenly: every practice polls around 3 in 10, and ownership is split across four different teams.

DevOps process changes

The build process tops the list. That's the part of the lifecycle that consumes Mac capacity, and the part AI is changing most.

32%

31%

29%

28%

20%

Build processQuality & validationTestingTeam skillsDeveloper workflowNew tooling

How is AI adoption changing your build and development process? (multi-select)

AI-driven DevOps use cases

AI isn’t concentrated in one corner of DevOps; it's diffusing everywhere at once.

Agentic CI/CD28%

Infrastructure provisioning27%

Build optimisation26%

Documentation26%

Incident response26%

Code review25%

Test generation25%

Which AI-driven DevOps use cases are you currently using or exploring? (multi-select)

Governance practices

Adoption has outrun the guardrails. The gap between what teams run and what they govern is the real exposure.

Security reviews31%

Formal AI policy30%

Approved tools list30%

Usage monitoring29%

Data governance28%

Sandboxed environments26%

Which governance practices are currently in place for AI at your organization? (multi-select)

AI ownership

AI governance is genuinely shared; four or five teams have a hand in it. Notably, fewer than 1% report having no formal ownership at all.

Engineering35%

IT35%

Platform / DevOps33%

Security29%

Dedicated AI governance team27%

No formal ownership1%

Who is responsible for governing AI usage in your DevOps pipeline? (multi-select)

Looking ahead: The next 12 months

Half the market is evaluating a move from self-hosted to managed/cloud Mac solutions, and a quarter is consolidating tooling. Budgets agree: Mac infrastructure ties developer platforms as the most-expected budget increase — ahead of cloud, security, and AI itself.

Mac infrastructure plans

Only 5% are trying to cut Mac spend. This is not a market looking to spend less; it is a market looking to spend differently.

Moving from self-hosted to a managed/cloud solution50%

Consolidating our Mac infrastructure tooling25%

Scaling up Mac capacity18%

Reducing Mac spend5%

Planning no significant changes2%

What best describes your Mac infrastructure plans for the next 12 months?

Where budgets are growing

Only 19% expect an AI budget increase despite near-universal adoption. For those that are self-hosting, AI spend is being absorbed by infrastructure.

=1Mac infrastructure35%

=1Developer platforms35%

3Cloud infrastructure32%

4Security29%

5Observability20%

6AI19%

7CI/CD tooling16%

Which areas in your organization do you expect will receive a budget increase in the next 12 months? (multi-select)

Quick answers from the data

How can we help?

Running into challenges with your Mac DevOps pipeline or scaling AI with your own Mac fleet? We spend all day on exactly these problems.

Contact sales

About this survey

All figures come from a single survey instrument. Single-select questions total 100%. Multi-select questions can total more than 100% because respondents could choose several options.



300 respondents, surveyed in July 2026



DevOps, platform, infrastructure, and SRE engineers and leaders



Mid-market and enterprise organizations in the U.S.

Have questions or need more details about the survey or the data? Contact us.

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Mac Infrastructure in the AI Era

AI isn't just changing how code gets written. It's changing how much Mac infrastructure it takes to ship it.

Contents

Today's Mac build landscape

How Mac CI is hosted and deployed

The real challenges of managing Mac infrastructure

The AI adoption landscape

The AI demand story

How AI is reshaping DevOps

Looking ahead: The next 12 months

Quick answers from the data

How can we help?

About this survey

Keep reading the DevOps Survey 2026