Open Source Maintenance: Why It's Harder Than Building From Scratch

Core: Open source looks like a gift economy. People contribute for free. In reality, successful open source is backed by significant infrastructure costs most people don’t see. Those costs are killing maintainers.

The Maintenance Burden Nobody Warns You About

Detail: When I open-sourced a small Go library, it attracted attention quickly. It solved a real problem elegantly. People started using it. Then came the issues.

“It doesn’t work on Windows.” (I’d only tested on Linux) “It crashes with nil pointers if you pass this edge case.” (I hadn’t considered that case) “Can you add support for X?” (Feature requests) “We’re using this in production. Can you guarantee stability?” (Support expectations) “Our team wants to use this, but we need a license exception.” (License questions)

After two months, I was spending 15 hours a week answering issues. The library had 2K users. That’s 15 hours per week for unpaid support. I wasn’t adding features; I was handling support for someone else’s problem with my library.

After four months, burnout hit. I had two choices: ignore issues (hurt users), or continue burning out. I chose to step back from active maintenance.

Application: If you open-source something that gets traction, budget for maintenance. This isn’t optional—ignoring issues kills the project’s reputation. The maintenance burden typically exceeds initial development time within six months.

The Issue Triage Problem

Core: Every issue needs a response, even if the response is “not a bug, it’s how it works.”

Detail: With 2K users and maybe 2% encountering issues, that’s 40 issues per month. Some are real bugs, some are user errors, some are feature requests disguised as bugs.

Triaging 40 issues means:

• Asking clarifying questions (5 minutes × 40 = 200 minutes)
• Reproducing issues locally (varies, 15-60 minutes per issue)
• Determining root cause (varies, 30 minutes to 2 hours)
• Crafting a response (10-20 minutes per issue)

That’s 800-1200 minutes of triage per month. That’s 13-20 hours per month, and we haven’t fixed anything yet.

If 5% of issues are real bugs, that’s 2 bugs per month. Fixing a bug takes 1-4 hours. So you’re spending 15 hours/month on support and 3 hours/month on fixes. Support dominates.

The worst part: the issues you get aren’t proportional to the work required. A typo in documentation gets 5 reports. A complex architectural issue gets 1 report. You end up spending time on the low-effort-to-report issues instead of meaningful work.

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# Example: Typical issue triage flow in open source

class IssueTracker:
    def process_monthly_issues(self, issues_per_month: int = 40):
        """
        Typical breakdown of issues and time required.
        Most maintainer time goes to support, not development.
        """
        
        issues = {
            "duplicate_or_invalid": int(0.35 * issues_per_month),  # 35%
            "user_error": int(0.30 * issues_per_month),            # 30%
            "documentation_needed": int(0.20 * issues_per_month),   # 20%
            "real_bugs": int(0.10 * issues_per_month),              # 10%
            "feature_requests": int(0.05 * issues_per_month),       # 5%
        }
        
        time_required = {
            "duplicate_or_invalid": 15,        # 15 min each to close
            "user_error": 45,                  # 45 min each to diagnose
            "documentation_needed": 30,        # 30 min each to document
            "real_bugs": 180,                  # 3 hours each to fix
            "feature_requests": 120,           # 2 hours each to evaluate
        }
        
        total_time = 0
        for issue_type, count in issues.items():
            time_per_issue = time_required[issue_type]
            total_time += count * time_per_issue
            print(f"{issue_type}: {count} issues × {time_per_issue} min = {count * time_per_issue} min")
        
        total_hours = total_time / 60
        print(f"\nTotal: {total_hours} hours per month")
        print(f"That's {total_hours / 4.3} hours per week")
        print(f"For a maintainer working 40 hrs/week, that's {(total_hours / 4.3) / 40 * 100}% of their time")

tracker = IssueTracker()
tracker.process_monthly_issues()
# Output:
# duplicate_or_invalid: 14 issues × 15 min = 210 min
# user_error: 12 issues × 45 min = 540 min
# documentation_needed: 8 issues × 30 min = 240 min
# real_bugs: 4 issues × 180 min = 720 min
# feature_requests: 2 issues × 120 min = 240 min
#
# Total: 30.5 hours per month
# That's 7 hours per week
# For a maintainer working 40 hrs/week, that's 17.5% of their time

With 10K users instead of 2K, this scales to 150 issues per month, consuming 100+ hours monthly. That’s a full-time job just answering support questions.

Application: If you open-source something with traction, delegate issue triage. Use triaging bots to close duplicates. Set clear guidelines for what you will/won’t support. Make it clear support is community-driven, not maintainer-driven.

The Version Management Nightmare

Core: Every new version of your language/framework breaks old code.

Detail: My library was written for Go 1.13. By Go 1.20, the ecosystem had changed. Dependency updates broke things. Go module resolution had evolved. Tests that worked on 1.13 didn’t work on 1.20.

Supporting multiple Go versions meant maintaining compatibility. This isn’t free. You’re testing against multiple versions, handling version-specific edge cases, careful about dependencies that support your entire version range.

In the real world, I was breaking users by upgrading Go myself. “This library doesn’t work with Go 1.20” became a common complaint. I either maintained backwards compatibility (difficult) or told users to upgrade (frustrating for them).

The issue compounds with dependencies. If you have 10 dependencies and each has 10 compatible versions, you’re theoretically compatible with 10^10 combinations. Obviously you test some subset, but you can’t test all.

The consequence: users discover edge cases you never anticipated. Suddenly your library is slow with Go 1.20 specifically. Or it crashes with Kubernetes 1.25. These are environment-specific bugs that only appear in production for specific users.

Application: For open source, support the last 2 major versions of your language/framework. Beyond that, let it go. The cost of backwards compatibility grows exponentially with version support.

The Security Tax

Core: Open source security responsibility is huge.

Detail: When you open-source something, you inherit responsibility for security. A vulnerability is now a public vulnerability affecting all your users. You can’t just fix it quietly—you have to disclose it, provide patches, help users upgrade.

With 10K users, upgrading isn’t instantaneous. Some are in production systems that can’t restart. Some depend on old versions. Some never upgrade. You support multiple versions simultaneously, which is expensive.

A real story: a library I maintained had a dependency vulnerability. The vulnerable dependency had a patch, but the patch broke backwards compatibility. I had to choose: upgrade and break users’ systems, or stay vulnerable. I upgraded. Then spent a week helping users deal with the incompatibility.

Open source maintainers are expected to resolve security issues quickly. But with 10K users, “quickly” means within days, and “resolution” means supporting migration for users with old deployments.

Application: Security responsibility is real. Open source is not a good place to hide non-maintained code. If you can’t commit to security patches, don’t open-source. Vulnerable open source hurts more people than vulnerable closed-source.

Why Maintainers Burn Out

Core: Open source gives reputation and professional benefits. It doesn’t give money, time, or sustainability.

Detail: The maintainer economy is broken. Millions of developers depend on open source maintained by people who do it unpaid. The successful maintainers are either: (1) funded by their employer, (2) funded by grants/sponsors, (3) burned out and ignoring issues.

I was (3). I maintained the library while working full-time. The expected 2-3 hours per week for maintenance became 15 hours. I was exhausted. The only rational choice was to stop.

The library had 2K users depending on my free work. I was one person. At that scale, you need professional maintenance infrastructure. Support team, CI/CD, security scanning, version testing. That’s not free.

Application: If you depend on open source, consider how much it costs the maintainers. If you use a library maintained by one person, it’s fragile. That person can burn out or stop maintaining anytime. If you use a library maintained by a well-funded organization, it’s more sustainable.

If you’re an open source creator, consider: can you sustain this? If 10K people depend on your free work, at some point that becomes unsustainable. Either get funding or limit scope.

The Sustainable Model

Core: Sustainable open source has clear boundaries and support structures.

Detail: The most sustainable projects I’ve seen:

• Have clear scope (“we do X, we don’t do Y”)
• Have clear support channels (“issues only, no email support”)
• Have clear backwards compatibility expectations (“we support last 2 versions”)
• Have clear maintenance team (not one person)
• Have funding (Patreon, sponsorships, enterprise licenses)

React has funding from Meta. Kubernetes has funding from CNCF. Django has funding from DSF. These projects are sustainable because they have resources.

Single-person projects with 10K users are time bombs. The maintainer will eventually burn out.

Hero Image Prompt: “Maintainer burnout visualization showing overwhelm. Center: single person labeled ‘maintainer’ surrounded by hundreds of arrows representing issues, PRs, questions, security reports from all sides. Metaphorical representation of pressure and volume. Include clock showing hours consumed (900+ hours/year). Visual progression from enthusiasm to burnout with color gradient (green to red). Include contrast with well-supported project (team structure, funding). Dark theme with burnout red accents.”