Merge Queue: Trunk vs GitHub
Why merge queue optimization matters
Merge queues solve a fundamental scaling problem: when multiple developers are attempting to merge to main simultaneously, the combination of independently-tested changes can break main. Both GitHub and Trunk address this by testing PRs against speculative future states: your changes validated against main plus everything ahead of you in line.
If you're reading this, you've probably already experienced GitHub's native merge queue and run into its limits. This comparison explains where those limits come from and how Trunk's architecture addresses them.
How Trunk and GitHub Compare
Queue Architecture
dynamic parallel lanes or FIFO single lane
FIFO single lane
Parallel Lanes
Yes, parallel lanes via impacted target analysisLearn more
No, all changes line up behind one another
Build concurrency
Yes (configurable)
Yes (configurable)
Per-branch queues
Yes
Yes
Merge Methods
Squash, merge, rebase
Squash, merge, rebase
PR Prioritization
Granular Priority levels, Direct MergeLearn more
"Jump Queue" (admin only, triggers full rebuild)
Flaky Test Handling
Failed PRs held while next PRs continue to test. If they pass, original PR merges automatically. Integrates with Trunk Flaky Tests for detection, quarantine, and ticketing.
Optional setting to prevent immediate PR ejection on failure. When enabled, GitHub waits to see if subsequent builds in its merge group pass before removing a failed PR.
CI Cost Management
Yes, via batching, direct merge, skip redundant bisection tests
No
Slack Integration
Yes, built-in DM and channel notifications
No
API
Full management API (REST): PR operations + queue management/configuration for full automation.Learn more
Limited API surface (GraphQL): PR operations (enqueue/dequeue/status). No queue management or configuration.
Webhooks
PR lifecycle events and Batch events. Includes failure reasons and detailed PR metadata in payloads.Learn more
Minimal webhook focused on triggering CI checks and cleanup notificationsLearn more
Pricing
Per committer
Per committer
Observability
Dashboard, Prometheus metrics endpoint, webhooks/events
webhooks/events only
Features
Queue Architecture
dynamic parallel lanes or FIFO single lane
Parallel Lanes
Yes, parallel lanes via impacted target analysisLearn more
Build concurrency
Yes (configurable)
Per-branch queues
Yes
Merge Methods
Squash, merge, rebase
PR Prioritization
Granular Priority levels, Direct MergeLearn more
Flaky Test Handling
Failed PRs held while next PRs continue to test. If they pass, original PR merges automatically. Integrates with Trunk Flaky Tests for detection, quarantine, and ticketing.
CI Cost Management
Yes, via batching, direct merge, skip redundant bisection tests
Slack Integration
Yes, built-in DM and channel notifications
API
Full management API (REST): PR operations + queue management/configuration for full automation.Learn more
Webhooks
PR lifecycle events and Batch events. Includes failure reasons and detailed PR metadata in payloads.Learn more
Pricing
Per committer
Observability
Dashboard, Prometheus metrics endpoint, webhooks/events
Features
Queue Architecture
FIFO single lane
Parallel Lanes
No, all changes line up behind one another
Build concurrency
Yes (configurable)
Per-branch queues
Yes
Merge Methods
Squash, merge, rebase
PR Prioritization
"Jump Queue" (admin only, triggers full rebuild)
Flaky Test Handling
Optional setting to prevent immediate PR ejection on failure. When enabled, GitHub waits to see if subsequent builds in its merge group pass before removing a failed PR.
CI Cost Management
No
Slack Integration
No
API
Limited API surface (GraphQL): PR operations (enqueue/dequeue/status). No queue management or configuration.
Webhooks
Minimal webhook focused on triggering CI checks and cleanup notificationsLearn more
Pricing
Per committer
Observability
webhooks/events only
Why teams adopt Trunk Merge Queue
Parallel Lanes
Gusto switched to Trunk to unlock Trunk's parallel and target-aware queues so their monorepo wouldn't all be forced through the same single pipeline.
Intellegent Batching
AgencyAnalytics needed batching to avoid doubling CI costs. GitHub's native merge queue didn't support it, and their homegrown solution was difficult to manage.
Flaky Test Handling
Caseware chose Trunk for reliable, lower-ops merges after flaky tests bottlenecked their queue. Nx integration and GitHub Actions made setup and management easy.
Bottom Line
Development & Roadmap
GitHub's merge queue is one feature among thousands on the platform. Trunk publishes a public roadmap focused on merge queue development with regular feature releases.
Throughput & Merge Latency
A single FIFO merge queue forces unrelated changes to contend for the same "lane," so one slow or high-variance CI workload can bottleneck everything behind it; parallel lanes matter because they let independent streams of work validate concurrently (and optionally batch compatible changes), increasing throughput and reducing end-to-end merge latency without lowering test coverage.
Reliability & Flake Handling
GitHub has no flaky test detection, so intermittent test failures eject PRs and trigger queue rebuilds. Trunk quarantines tests that produce inconsistent results for the same commit SHA, allowing valid code to merge while surfacing unreliable tests for repair.
Configuration and observability
GitHub's queue is configured through branch protection UI with limited options. Visibility into queue performance, CI efficiency, and failure patterns is minimal.
Trunk provides configuration-as-code, detailed analytics on queue residence time and batch efficiency, and visibility into the bisection process when failures occur. When your queue backs up at 4pm on release day, you need to know why. Trunk shows you: which PRs are blocking, which tests are flaking, how long each batch took. GitHub gives you a queue position.
