Trunk-Based Development at Seed-Stage Startups
How founding engineers can set up version control practices that scale with their team's growth.
Someone pushes a first commit, picks a branch name, and sets up the repo. That choice, made in an afternoon, usually outlives the person who made it. The median seed-stage startup has four employees, so whatever one engineer decides about version control on day one becomes the whole company's engineering culture by default.
At seed stage, the founding engineer rarely owns just the code. They own the architecture, the deployment setup, and the early habits that later hires will copy without question. A branching model picked for convenience when there were two people on the team turns into the template the tenth hire inherits, with no one left alive in the company who remembers why it was chosen that way.
Most founding engineers reach for whatever branching model they used at their last job. For a lot of engineers coming from larger companies, that means reaching for GitFlow or some version of long-lived branches out of habit. Nobody stops to ask whether a process built for a much larger engineering org works for a team of two or three people shipping several times a day. That question is the subject of this piece, and the answer is trunk-based development.
Trunk-Based Development: What It Is and What It Is Not
Trunk-based development is a version control practice built around one shared branch, called main or trunk, and developers integrate small changes constantly there instead of working in isolation for weeks at a time. It needs real discipline, and three rules hold no matter how big the team is.
First, every developer integrates to main at least once a day. Second, if feature branches exist at all, they live for hours, not weeks. Third, nobody commits broken code to main. If work isn't finished, it gets hidden behind a feature flag instead of parked on an open branch waiting for the right moment to land.
Trunk-based development doesn't mean you commit whatever, whenever, with no review step. The discipline lives in keeping changes small and frequent, not in skipping scrutiny. It also isn't GitFlow with a different name. GitFlow organizes work around two long-lived branches, main and develop, supported by short-lived release and hotfix branches, and it's built for teams shipping on a fixed schedule. Trunk-based development assumes continuous delivery instead, so every commit to main can go to production.
Most small teams land on a hybrid: short-lived feature branches that close within a day, small pull requests, and automated tests gating every merge. That version keeps the benefits of trunk-based development, fast integration and minimal divergence, but engineers still get a pull request to review before code lands. Trunk-based development is also the foundation continuous integration sits on. CI is the pairing of frequent trunk commits with a fast, automated test suite that runs after every single one, confirming the system still works. Without the first half, the second half has nothing to check against.
The structural conditions of seed stage make TBD the natural fit
Trunk-based development is the correct default the moment a team forms, and the smaller the team, the more obvious that fact becomes. A team of two to four engineers doesn't have a merge conflict problem large enough to justify long-lived branches. Long-lived branches exist so you can coordinate many people working on overlapping code at once. At seed stage, that coordination problem barely exists, so the overhead those branches bring is just waste.
The cost of long-lived branches rises with team size, and the benefit does too, but not at the same rate. At four people, the cost of running GitFlow is low in absolute terms, yet the payoff is close to zero. GitFlow's release and develop branch structure was built so large teams shipping on a fixed calendar could coordinate. Neither condition exists at seed. Merge debt grows fastest when branches stay open longest relative to how quickly the codebase is changing, and seed-stage teams move fast and diverge fast. That's exactly when a long-lived branch turns from a planning tool into a liability. Trunk-based development pushes every change back to main quickly, so it cuts out unnecessary divergence and keeps total engineering overhead low for a small team. The fit comes from the structure of the team, not a stylistic preference.
The honest counterargument deserves a straight answer. Trunk-based development assumes developers are experienced, because if a junior engineer commits half-finished work straight to main and no one is tracking what's being touched, that can destabilize the whole project. That risk is real in a large organization with mixed seniority. It doesn't describe a founding engineering team, which is senior by definition. A founding engineer is the most senior person in the room, writing the code themselves. The practical answer for teams that still want a safety net is the short-lived branch hybrid: branches that close within a day, pull requests kept small, automated tests gating every merge. That setup keeps most of the speed trunk-based development offers while adding a check that committing straight to main skips.
The three dependencies that make TBD work in practice: feature flags, fast CI, and commit discipline
Trunk-based development only holds up with three supporting pieces in place: feature flags, a fast CI pipeline, and commit discipline. Skip any one of them and the rule has no teeth. Teams that try trunk-based development without the supporting infrastructure quietly slide back into branch-based habits within a few weeks.
Feature flags separate deployment from release. Code merges into main continuously, but any feature that isn't ready gets hidden behind a flag until product decides it's time to turn it on. That separation does double duty as an operational safety net: if a released feature starts causing problems, a team can switch it off instantly instead of scrambling to write an emergency hotfix branch. Wayfair moved to feature-flag-driven trunk-based development, built on Unleash's platform, because it needed exactly this. As their platform scaled to handle a very high volume of requests, testing had become a bottleneck, and flags let them keep deploying without that bottleneck slowing everything down. Tink, now part of Visa, used feature flag tooling across its monolithic development framework so it could roll features out flexibly across numerous services and environments and still keep deployment risk contained. Flags carry their own cost, though. Flag logic spreads through a codebase over time, and flags that outlive their purpose turn into clutter nobody wants to touch. Teams running trunk-based development need an actual process for retiring flags once a feature has fully shipped, not just a policy for adding them.
Fast CI is just as load-bearing. GitFlow can tolerate a slow pipeline because its release branches absorb the delay. Trunk-based development can't absorb that same delay, because every commit to main triggers the pipeline directly, and slow feedback breaks the whole rhythm the practice depends on. A pipeline that takes an hour to return results isn't compatible with developers integrating several times a day. The target for most teams running trunk-based development is a pipeline under 10 minutes, fast enough that a developer can wait for the result and move on without losing focus. The emergence of CI/CD platforms built specifically for this problem shows how real the demand has become. A funded product built around this exact problem is a sign that fast pipeline infrastructure for trunk-based development has become its own category, not a side feature of existing tools.
Commit discipline ties the other two together. Every developer integrates to main at least once a day. If feature branches exist at all, they stay open for hours, not weeks. And broken code never lands on main: half-built UI work goes behind a feature flag instead of sitting on an open branch waiting to be finished.
How AI-Assisted Development Raises the Stakes for TBD
AI coding tools let a single engineer produce far more code per day than you could a few years ago, but that extra output doesn't remove instability from the system. Without a fast CI pipeline and real trunk-based discipline already in place, that acceleration just pushes the instability further downstream, where it's harder to see and more expensive to fix.
A founding engineer working in 2026 can build and run a production system that would have needed a much larger team only a few years back. That changes what the branching model needs to hold up under. It has to scale with AI-assisted output, not just the pace of a human typing code by hand. Trunk-based development's requirement for constant small integrations turns out to match AI-assisted code generation well: automated tools and fast test suites handle a stream of small, frequent changes far more efficiently than a human reviewer can handle a pile of large, infrequent ones dumped on them once every two weeks.
Some of the newest AI coding tools are starting to build this discipline directly into how they operate. With Claude Code's Agent Skills feature, teams can write deployment playbooks as instructions the agent then follows on its own. Asked to refactor a service, an agent configured this way can consult a Progressive Delivery skill and make sure feature flags are implemented by default, baking trunk-based habits into the tool itself rather than leaving them up to whoever is prompting it that day.
This fits a broader shift DORA's research has tracked for years: progressive delivery practices like canary releases, feature flags, and observability-driven rollouts are now tied directly to elite software delivery performance. The goal driving these practices is shipping safely, in small reversible steps, with minimal impact on users when something goes wrong. AI raises the volume of code moving through a team's pipeline. Trunk-based development, backed by flags and fast CI, is what keeps that higher volume from turning into higher risk.
What Elite Engineering Organizations at Scale Confirm About TBD
The best-performing engineering organizations run trunk-based development, no matter their size. That consistency shows the practice is the clearest version of a pattern that holds at every scale.
DORA's research has shown for years that elite performers, the teams that hit their reliability targets, are far more likely to have adopted trunk-based development than lower-performing teams. The 2021 DORA Report found this gap directly: elite performers use trunk-based development at significantly higher rates than everyone else, and those same teams report shorter lead times, fewer production incidents, and higher developer satisfaction.
Google runs a single monolithic repository holding billions of lines of code, with tens of thousands of engineers committing to shared trunks through internal tooling like Piper and CitC. That single fact undercuts the claim that trunk-based development only works for small teams. Google's coordination challenge, tens of thousands of engineers touching the same trunk, dwarfs anything four engineers at a seed-stage startup would face, so the model holds at any scale that matters.
What TBD literacy signals in a founding engineer hiring process
Interviewers at seed-stage startups increasingly ask candidates to defend an actual position on branching strategy, CI setup, and deployment philosophy, and not just describe tools they've used before. An engineer who can explain why trunk-based development fits a four-person team, and what feature flags and fast CI are actually for, is showing something more specific than general skill. That fluency shows someone has shipped continuously in a real production system, rather than having only worked inside long release cycles where these questions never came up.
That signal matters more as hiring itself gets more selective at this stage. If a company's deployment philosophy and hiring pace are already clear before a conversation starts, you do better finding founding engineer roles through a curated process than sending applications into a general pool and hoping for a response. The logic is the same one that runs through this whole piece: match on demonstrated practice, not on a resume line that says which framework someone used five years ago.
