Avrea’s Faster Runners for GitHub Actions Reflect How AI Coding Is Changing CI

Before any change to a software project can ship, it has to pass continuous integration (CI), the automated builds and tests that run every time a developer proposes an edit. Engineers wait on those checks many times a day, so their speed sets the pace at which a team can ship.

Now AI coding agents are sending a lot more work through those checks. On weekends, when most human engineers are offline, agents and bots account for about one in five of the in-progress code branches being tested in CI, according to CircleCI’s Q3 2026 State of Software Delivery Pulse Report.

Since September 2025, the number of workflows active on a typical day has grown 40% on these feature branches, compared with 17% on the main codebase, where approved changes end up. Testing activity is rising more than twice as fast as activity on the main codebase, and CI has to absorb the extra work.

But more code does not necessarily mean more progress. A change only counts once it clears its checks and gets merged, so when CI falls behind, much of the speed AI tools add is lost to waiting.

One of the most practical fixes requires no new CI system at all. Teams keep their existing workflows and move them onto faster machines, often by changing a single line of configuration. Avrea is one of the companies built around that idea. Its main product runs CI checks for teams that use GitHub Actions, the CI system built into GitHub, on machines it says are faster than GitHub’s own, and it saves the results of earlier runs so unchanged parts of a project don’t have to be rebuilt and retested.

The company calls itself a “software delivery platform” and has added newer products, including its own CI engine, which is in beta. Several CI vendors have moved in the same direction in 2026, building tools that cover more of the path from code to production, not just the machines that run the checks.

When AI coding agents open dozens of PRs a day, CI becomes the bottleneck

The pressure on CI comes from three places: volume, repetition and recovery. Volume is the first, and the figures above show how quickly it is rising. The repetition compounds the effect. The CircleCI report says the median team needs roughly five total CI cycles per shipped change. If an agent produces a change, runs tests, revises it and sends it back through CI, each additional cycle consumes more compute and adds another wait. A 20-minute GitHub Actions build becomes especially expensive when the same change has to pass through it repeatedly.

Recovery adds another delay. When a check fails on in-progress work, the typical team now takes almost an hour and a half to get it passing again. The same report found that this recovery time rose from about 80 minutes last September to 86 minutes in August. That delay likely affects coding agents’ workflows as well, because many of them rely on builds and tests to confirm their changes work.

To be fair, plenty of CI runs are fast enough, finishing within a few minutes. The trouble is how many times work has to go through CI and how long it takes to fix failures. The problem is most severe on big projects, where a single build can take 20 minutes or more.

Why teams choose third-party GitHub Actions runner providers, and how they compare

For most teams on GitHub Actions, GitHub’s own hosted runners are the default. They need no setup, but they run on general-purpose machines and bill by the minute, so as AI coding agents push more work through CI, teams wait longer and their bills climb with usage. That has opened a crowded market for third-party runner providers, from Blacksmith, Depot, Namespace and Avrea to newer entrants such as WarpBuild, Ubicloud and StarSling.

Whichever third-party GitHub Actions runner provider they choose, teams usually switch for the same reasons: faster machines, lower usage costs, better caching or more visibility into why a job is slow. Managed runner providers let them make that change without maintaining a fleet of self-hosted machines.

Choosing between Blacksmith, Depot and Namespace for GitHub Actions, or newer options such as Avrea, comes down to what a team cares about most. Blacksmith has built a strong developer brand and community around GitHub Actions, with relatively low ARM pricing, test analytics and Docker integrations. Depot has particularly deep Docker build tooling and now has its own CI engine, Depot CI. Namespace emphasizes premium hardware, debugging capabilities and development environments alongside its runners.

Avrea’s emphasis is on combining high-performance runner hardware with several caching layers. According to Avrea, its build-cache integrations cover tools including Gradle, Maven, Nx, Nix, ccache, sccache, Go, Bazel, Tuist, Xcode and Turborepo. Enterprise customers can also apply an egress firewall that allows or blocks destinations by job. The platform is ISO 27001 certified and SOC 2 Type II attested, with SSO and audit logging available. Those features are not all unique to Avrea. Blacksmith also supports build-tool caching, for example. Avrea works only with GitHub Actions, while newer products such as Avrea CI, the company’s own CI engine, remain in beta.

How to speed up GitHub Actions without rewriting your workflows

For most teams, the quickest way to make CI faster is to change where GitHub Actions workflows run, without rewriting them. Avrea uses the same GitHub Actions workflow model, with migration typically reduced to changing a runner label to runs-on: avrea-ubuntu-latest. Its runners cover Linux on x86 and ARM, as well as macOS and Windows.

The gains come from two places: faster CPUs for compiling and Docker builds, and caching that skips work already done. Avrea claims teams can “build 2x faster than on GitHub-hosted runners on hardware alone, and 10x faster with cache hits.” Its caching covers dependencies, Docker layers, build output and code checkouts as well as integrations with individual build tools.

Independent data gives some support to the hardware side of the claim. As of November 11, 2026, RunsOn’s benchmark ranks Avrea the fastest GitHub Actions runner on ARM at each of the 2, 4 and 8 vCPU sizes tested. On x86, it ranks second to Namespace at most sizes. RunsOn is itself a runner competitor, and Depot is not included in the benchmark, so the results might not tell the whole story.

When a job fails, teams need to see why before they can fix it. Avrea exposes resource usage by step, searchable logs and SSH access to a running job, including through its avr command-line tool.

Avrea AI, currently in beta with access by request, takes that further by identifying and proposing fixes for failing tests, slow builds and outdated dependencies, with changes delivered as pull requests for review. For teams handling more machine-generated changes, reducing recovery time can matter as much as reducing the duration of a successful run.

Is it cheaper to self-host GitHub Actions runners or use a third-party provider?

Cost comparisons became less straightforward after GitHub reduced hosted-runner prices by up to 39% last winter.

Self-hosting can still provide inexpensive raw compute and complete control over the environment. The tradeoff is largely operational, as someone has to provision capacity, patch the machines, secure them and make sure enough runners are available when CI demand spikes.

Managed providers sit between those two models. Many offer cheaper alternatives to GitHub-hosted runners without transferring runner operations to the development team.

Avrea’s pricing page claims “67% lower compute cost than GitHub Actions at list prices, with 2x faster builds.” Part of that saving comes from builds finishing faster, so it makes sense that the real figure will vary from team to team.

Avrea uses per-minute usage pricing with no per-seat fee. Linux x86 starts at $0.002 per minute for a 1-vCPU runner, and its pay-as-you-go plan includes $12 in monthly credit. Actual savings therefore depend on runner size, build duration, cache effectiveness and the amount of operational work a team would otherwise spend on self-hosting.

What CI setup works best for teams shipping AI-generated code

Faster machines are now only part of what these companies offer. Depot made its own CI engine, Depot CI, generally available in March 2026. Namespace, which combines runners with development environments, pitches itself as infrastructure for coding agents. Avrea’s newer products sit alongside the runners that remain its core business.

The reasoning is that coding agents touch more of the delivery path than CI alone. They need somewhere to run code, repeated builds benefit from shared caches, and failed runs still need debugging.

For teams that need to make CI faster now, the simplest step is to move existing GitHub Actions workflows onto faster runners with built-in caching, which for most workflows can be a one-line change. The bigger decision comes after that. A single platform spanning development environments, runners and CI can share machines and caches across each step, but it ties a team more closely to one vendor. Mixing specialized tools lets a team pick the fastest runner, cache or build service for each job, but leaves the team to connect them.

Either way, the checks that once ran mostly for human changes now run around the clock for agents as well, and CI will have to keep pace with both. In 2027, it should become clearer whether teams want one platform for all of it, or the best tool for each step.

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