Across early-stage startups, I keep seeing the same pattern: engineers set up master and develop branches, formal release cycles, and staging environments. Not because the business needs them, but because that's what they did at their last job. This is the third article in my series on mental models for startups (please check it out, there's a lot of interesting material there). This time, we're looking at release processes. It usually happens when the product is just getting started. The real challenge not stability, it is finding revenue and users. Engineering practices come from habit, not necessity and usually from previous jobs at bigger companies where production failures were expensive and heavy process was the right response. The real challenge not stability, it is finding revenue and users But in a startup, bringing in those processes too early pulls focus away from learning and toward ceremony. It adds friction exactly when speed matters most. This mismatch between what the business actually needs and how the team releases code is where the mental models below come in. Each one offers a different way to think about release decisions, along with practical advice on what actually works when you're pre-revenue and still searching for fit. Who is this for?  This is for founders and engineers who sense their process is slowing them down but aren't sure why. Who is this for? Mental Model 1 or Your Release Process Is a Projection of Fear A release process isn't neutral infrastructure, but it is a mirror of what the team is most afraid of breaking. If the deepest fear is breaking production (regressions, outages, lost trust) you build fortresses: staging environments, approval gates, lengthy reviews, release trains. If the deepest fear is breaking production If the deepest fear is building the wrong thing (wasting time on features nobody wants, missing product-market fit) you build sensors: fast deploys, rapid feedback loops, constant experimentation. If the deepest fear is building the wrong thing Most teams never make this choice consciously. They inherit workflows from past jobs, copy "best practices" from Big Tech blogs, or default to whatever feels professional. The result is a process shaped by someone else's reality and almost always the reality of a mature company with paying customers, compliance needs, and serious consequences for downtime. Release fear has to match your current stage. Every software company lives with two fears that never fully disappear: Execution risk: Shipping bugs, breaking what works, damaging production.
Discovery risk: Building the wrong thing, missing fit, running out of runway. Execution risk: Shipping bugs, breaking what works, damaging production. Execution risk Discovery risk: Building the wrong thing, missing fit, running out of runway. Discovery risk These fears pull in opposite directions, and your release process is the mechanism that chooses between them. Heavy processes like staging gates, approvals, and release trains insure against execution risk. Lightweight processes like trunk-based development, continuous deploy, and feature flags insure against discovery risk. The tradeoff is real, but it's not symmetric. A bug in production is a problem you can fix in an hour. Building the wrong product for six months is a problem you might not recover from. You can't fully hedge both. The right choice almost always follows the company's stage: Pre-revenue, pre-PMF: You're still proving the core hypothesis, so almost nobody outside the team uses the product. The house barely exists yet — insuring it against fire is premature. Fear irrelevance, not instability. Ship often, learn faster than your competitors. Bugs are embarrassing but survivable. Building something nobody wants is fatal.
Post-PMF, with revenue: Real customers depend on you, so downtime means lost revenue, churn, reputational damage. Now execution risk becomes existential. It's time to invest in stability: gates, testing, rollbacks, observability. Pre-revenue, pre-PMF: You're still proving the core hypothesis, so almost nobody outside the team uses the product. The house barely exists yet — insuring it against fire is premature. Fear irrelevance, not instability. Ship often, learn faster than your competitors. Bugs are embarrassing but survivable. Building something nobody wants is fatal. Pre-revenue, pre-PMF Post-PMF, with revenue: Real customers depend on you, so downtime means lost revenue, churn, reputational damage. Now execution risk becomes existential. It's time to invest in stability: gates, testing, rollbacks, observability. Post-PMF, with revenue The fear should evolve as the business does. A process that's perfect at one stage becomes actively harmful at another. Before debating branches, pipelines, or review rules, pause and answer three questions:

What am I actually most afraid of right now: breaking something that works, or building something that doesn't matter?
Do I have paying customers who depend on reliability, or am I still searching for the first ones?
Is this process shaped by my current reality, or am I carrying forward someone else's old fears? Before debating branches, pipelines, or review rules, pause and answer three questions: What am I actually most afraid of right now: breaking something that works, or building something that doesn't matter?
Do I have paying customers who depend on reliability, or am I still searching for the first ones?
Is this process shaped by my current reality, or am I carrying forward someone else's old fears? What am I actually most afraid of right now: breaking something that works, or building something that doesn't matter? Do I have paying customers who depend on reliability, or am I still searching for the first ones? Is this process shaped by my current reality, or am I carrying forward someone else's old fears? Mental Model 2 — Borrowed Maturity Importing release structures from mature companies creates the illusion of progress while silently reducing learning velocity. Process is comfort. When product-market fit is uncertain and the next pivot could be tomorrow, a familiar workflow feels like solid ground. But your release process doesn't just reflect how you ship code. It reflects what your business actually is. Process is comfort. Conway's Law tells us software architecture mirrors organizational structure. There should be a corollary: release process mirrors business certainty. A company that knows what it's building releases differently than one still figuring it out. A company that knows what it's building releases differently than one still figuring it out. I've watched this pattern firsthand. A pre-revenue startup with zero paying users, where the product was still a hypothesis. Instead of testing that hypothesis, the first month went into "proper" infrastructure: develop and main branches, separate staging and prod environments, CI/CD pipelines, Jira and Jira templates. The full enterprise stack, lol. It looked professional but we had two environments for software that zero humans outside the team had touched. A release process for a product that didn't need releases. It needed experiments. Release Processes: A Reality Check Process

How It Works

Startup Fit (pre-PMF)

Why



GitFlow

Work happens on a separate develop branch while main stays stable. Features and fixes are merged in batches, with dedicated release and hotfix branches used to control when changes reach production.

Poor ❌

Batching delays feedback and hides problems.



GitHub Flow

New work is done on short-lived feature branches. Each change goes through a pull request, is reviewed, merged into main, and then deployed continuously.

Good ✅

Reviews add latency to learning.



Trunk Based

Developers commit small changes directly to main or merge very short-lived branches. Unfinished features are hidden with flags and deployments happen frequently.

Good ✅

Fast feedback, break it all faster.



Release Trains

Releases happen on a fixed schedule. Features that are ready ship with the next train, while unfinished work waits for the following one.

Poor ❌

Calendars slow discovery. Process

How It Works

Startup Fit (pre-PMF)

Why



GitFlow

Work happens on a separate develop branch while main stays stable. Features and fixes are merged in batches, with dedicated release and hotfix branches used to control when changes reach production.

Poor ❌

Batching delays feedback and hides problems.



GitHub Flow

New work is done on short-lived feature branches. Each change goes through a pull request, is reviewed, merged into main, and then deployed continuously.

Good ✅

Reviews add latency to learning.



Trunk Based

Developers commit small changes directly to main or merge very short-lived branches. Unfinished features are hidden with flags and deployments happen frequently.

Good ✅

Fast feedback, break it all faster.



Release Trains

Releases happen on a fixed schedule. Features that are ready ship with the next train, while unfinished work waits for the following one.

Poor ❌

Calendars slow discovery. Process

How It Works

Startup Fit (pre-PMF)

Why Process Process How It Works How It Works Startup Fit (pre-PMF) Startup Fit (pre-PMF) Why Why GitFlow

Work happens on a separate develop branch while main stays stable. Features and fixes are merged in batches, with dedicated release and hotfix branches used to control when changes reach production.

Poor ❌

Batching delays feedback and hides problems. GitFlow GitFlow GitFlow Work happens on a separate develop branch while main stays stable. Features and fixes are merged in batches, with dedicated release and hotfix branches used to control when changes reach production. Work happens on a separate develop branch while main stays stable. Features and fixes are merged in batches, with dedicated release and hotfix branches used to control when changes reach production. Poor ❌ Poor ❌ Batching delays feedback and hides problems. Batching delays feedback and hides problems. GitHub Flow

New work is done on short-lived feature branches. Each change goes through a pull request, is reviewed, merged into main, and then deployed continuously.

Good ✅

Reviews add latency to learning. GitHub Flow GitHub Flow GitHub Flow New work is done on short-lived feature branches. Each change goes through a pull request, is reviewed, merged into main, and then deployed continuously. New work is done on short-lived feature branches. Each change goes through a pull request, is reviewed, merged into main, and then deployed continuously. Good ✅ Good ✅ Reviews add latency to learning. Reviews add latency to learning. Trunk Based

Developers commit small changes directly to main or merge very short-lived branches. Unfinished features are hidden with flags and deployments happen frequently.

Good ✅

Fast feedback, break it all faster. Trunk Based Trunk Based Trunk Based Developers commit small changes directly to main or merge very short-lived branches. Unfinished features are hidden with flags and deployments happen frequently. Developers commit small changes directly to main or merge very short-lived branches. Unfinished features are hidden with flags and deployments happen frequently. Good ✅ Good ✅ Fast feedback, break it all faster. Fast feedback, break it all faster. Release Trains

Releases happen on a fixed schedule. Features that are ready ship with the next train, while unfinished work waits for the following one.

Poor ❌

Calendars slow discovery. Release Trains Release Trains Release Trains Releases happen on a fixed schedule. Features that are ready ship with the next train, while unfinished work waits for the following one. Releases happen on a fixed schedule. Features that are ready ship with the next train, while unfinished work waits for the following one. Poor ❌ Poor ❌ Calendars slow discovery. Calendars slow discovery. Startups and mature companies live in different worlds — and each world dictates its own survival rules. Mature companies optimize for coordination risk. Large teams, parallel workstreams, high cost of production failure makes GitFlow and similar processes as a reasonable choice, minimize merge hell and chaos. Mature companies optimize for coordination risk. Startups need to optimize for learning latency. The dominant risk isn't breaking production (you barely have users). It's building something nobody wants so feedback speed determines everything. Startups need to optimize for learning latency. feedback Copying GitFlow (or any heavy process) shifts your bottleneck from discovery to ceremony. You're not reducing risk — you're hiding it behind process. Your competitor with a messy Git history shipped three experiments last week. You're still waiting for the release branch to stabilize. Borrowed maturity creates a false sense of safety.
- Long cycles make pivots expensive
- Big feature branches hide integration problems until late
- Review overhead burns cognitive energy better spent on users
The process looks serious on the outside. Inside, it delays the very learning that determines whether the company lives or dies. Borrowed maturity creates a false sense of safety. false sense of safety - Long cycles make pivots expensive - Big feature branches hide integration problems until late - Review overhead burns cognitive energy better spent on users The process looks serious on the outside. Inside, it delays the very learning that determines whether the company lives or dies. Practicals 1. GitHub Flow is enough. One protected main branch with short-lived feature branches that last hours or days, not weeks. Deploy directly from main to production. No develop branch, no release branches, no hotfix branches. If three engineers need a branching strategy diagram, something is already wrong. 1. GitHub Flow is enough. main main develop 2. Test in production. This sounds reckless but it's not. With feature flags and solid observability, production is the only environment that tells the truth, and staging environments are expensive lies you maintain to feel safe. 2. Test in production. 3. Feature flags are non-negotiable. They let you separate shipping from releasing, which means you can deploy code daily and toggle features on when they're ready. Flags let you ship incomplete work invisibly, A/B test with real users, segment paid versus experimental features, and kill failures instantly. 3. Feature flags are non-negotiable. 4. Preview deployments for every change. Tools like Vercel, Netlify, Render, and Railway generate unique preview URLs for every PR, so stakeholders see real changes instantly without a shared staging environment to maintain and without "works on my machine" excuses. Just a link. 4. Preview deployments for every change. 5. AI-assisted code reviews over waiting for humans. At this stage, unit test coverage is overrated because you don't need 80% coverage, you need fast catches. Tools like GitHub Copilot, Cursor, or dedicated PR review bots flag obvious issues instantly, which is faster and cheaper than blocking on a teammate. 5. AI-assisted code reviews over waiting for humans. 6. Observability over exhaustive testing. Use Sentry for errors and PostHog or Mixpanel for user behavior, with everything alerting directly to Slack so you know when something breaks in seconds rather than when a frustrated user emails. 6. Observability over exhaustive testing. 7. Slack as mission control. Route error alerts, deployment notifications, analytics events, and in-app feedback to dedicated channels, and add a simple feedback widget to your product so users become your real-time QA team. 7. Slack as mission control. **8. Optimize for fast recovery, not perfect prevention.**A 5-minute rollback beats a 2-day QA cycle, so ship often, observe closely, and revert instantly if something breaks. Bottom Line Your release process is a bet on what will kill you first.  Before product-market fit, the danger is building the wrong thing while runway shrinks. Process reflects fear. Know which fear matches your stage.
Borrowed maturity is expensive. That GitFlow setup made sense at your last company. It probably doesn't now.
Reversibility beats stability. Optimize for how fast you can change direction, not how solid the foundation feels. Process reflects fear. Know which fear matches your stage. Process reflects fear. Borrowed maturity is expensive. That GitFlow setup made sense at your last company. It probably doesn't now. Borrowed maturity is expensive. Reversibility beats stability. Optimize for how fast you can change direction, not how solid the foundation feels. Reversibility beats stability. Ship fast. Learn faster.

The writer was physically present in relevant location(s) to this story. The location is also a prevalent aspect of this story be it news or otherwise.

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