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Why Did Starlette Take 8 Years to Reach Version 1.0?

Cowrie
Cowrie
Dev @ Bswen

The Version Number Paradox

When I saw the announcement that Starlette 1.0 RC1 was released, my first reaction was confusion. Starlette has been powering FastAPI for years. It has 10 million daily downloads on PyPI. Thousands of companies depend on it through FastAPI’s ecosystem. How can something this widely used still be at version 0.x?

I checked the release history. Tom Christie created Starlette in June 2018. That’s nearly 8 years in version 0.x territory.

The Reddit comments captured exactly what I was thinking:

“8 years to 1.0 feels right tho. better than rushing a major version and breaking half the ecosystem”

“10 million downloads a day and most people using it through fastapi have never had to think about it. that’s the best thing you can say about infrastructure-level code”

This seems contradictory. Semantic Versioning says 0.x means “initial development” where “anything MAY change at any time.” But Starlette 0.x was clearly production-stable. So which is it? Was Starlette unstable for 8 years, or is Semantic Versioning too rigid?

The answer changed how I think about software maturity.

What Semantic Versioning Actually Says

I went back to the source. The Semantic Versioning 2.0.0 specification states:

“Major version zero (0.y.z) is for initial development. Anything MAY change at any time. The public API SHOULD NOT be considered stable.”

But here’s the nuance I missed: “MAY change” doesn’t mean “WILL break.” It means the maintainers reserve the right to evolve the API. They’re not promising backward compatibility yet, but they’re not obligated to break things either.

The specification also says:

“Version 1.0.0 defines the public API… If you have a stable API on which users have come to depend, you should be 1.0.0.”

This created a paradox in my mind. Starlette had a stable API that millions depended on. Why stay at 0.x?

Then I found the SemVer FAQ, which added more confusion:

“If your software is being used in production, it should probably already be 1.0.0.”

By this logic, Starlette should have been 1.0 years ago. But the Encode team (Tom Christie’s organization) deliberately chose not to. Why?

0.x Doesn’t Mean Unstable

The key insight is that version 0.x doesn’t automatically mean “unstable” or “low-quality.” It means “we’re still learning.”

I looked at other examples in the scientific Python ecosystem. SciPy took 16 years before reaching 1.0.0, despite being the foundation of scientific computing. NumPy also had a long 0.x phase. These weren’t unstable libraries—they were critical infrastructure with massive production usage.

The distinction is:

  • Production-ready: High quality, well-tested, documented, safe to use
  • Version 1.0: We promise backward compatibility forever

Starlette’s README explicitly stated it was “production-ready” while at version 0.x. It had 100% test coverage, 100% type annotation, and minimal dependencies. The quality was there. The version number just reflected the Encode team’s philosophy about API evolution.

The “Boring Infrastructure” Philosophy

The Reddit commenter nailed it:

“10 million downloads a day and most people using it through fastapi have never had to think about it. that’s the best thing you can say about infrastructure-level code”

Infrastructure code should be invisible. When was the last time you thought about the TCP/IP stack? Or your operating system’s kernel allocator? You don’t, because they just work. That’s the goal.

Version 1.0 is a burden, not a milestone. Once you release 1.0, you’re signing a contract: “I will maintain backward compatibility forever.” Every API decision you made is now set in stone. You can’t evolve, refine, or fix design mistakes without major version hell.

For infrastructure libraries, this is terrifying. Breaking changes in Starlette would break FastAPI. Breaking FastAPI would break thousands of companies. The cascade effect is massive.

Tom Christie and the Encode team understood this. By staying in 0.x for 8 years, they gave themselves room to:

  • Fix API design mistakes without breaking the ecosystem
  • Experiment with new patterns without major version bumps
  • Respond to real-world usage before locking down the API

The 8-Year Timeline

Here’s what those 8 years actually looked like:

timeline
    title Starlette's 8-Year Journey to 1.0
    2018 : Starlette 0.1 created by Tom Christie
    2019 : FastAPI released (built on Starlette)
    2020-2023 : Explosive FastAPI adoption
    2024 : 10M daily downloads milestone
    2026 : Starlette 1.0 RC1 released

When FastAPI was released in 2019, it didn’t just use Starlette—it was built on Starlette. Every FastAPI application is a Starlette application. The entire async Python web ecosystem rests on this foundation.

Imagine if Starlette had rushed to 1.0 in 2019 after just one year. Any design mistakes or overlooked edge cases would have become permanent. The Encode team would have been forced to either:

  • Live with bad design decisions forever, or
  • Release breaking changes that cascade through the entire ecosystem

Instead, they waited. They watched how thousands of developers used the library. They learned from real-world production usage. They refined the API based on actual needs, not theoretical best practices.

The Cost of Rushing to 1.0

I’ve seen what happens when projects rush to 1.0. In the JavaScript ecosystem, there’s constant fatigue from frequent breaking changes. Libraries release 1.0, then 2.0, then 3.0 within months. Each update requires migration effort, breaking applications, and lost productivity.

I experienced this personally last year. A dependency I used jumped from 0.x to 1.0, then to 2.0 within six months. Each version broke the API. I spent weeks migrating code instead of building features. The library maintainer was excited about “semantic versioning done right,” but from my perspective, it was a nightmare.

The Reddit commenter’s warning stuck with me:

“better than rushing a major version and breaking half the ecosystem”

Breaking half the ecosystem is worse than waiting 8 years. A stable 0.x version that never breaks is more valuable than a 1.0 version that requires constant migration work.

Production-Ready vs. Version 1.0

The confusion comes from equating “production-ready” with “version 1.0.” They’re not the same thing.

Production-ready means:

  • Comprehensive test coverage (Starlette has 100%)
  • Type annotations (Starlette has 100%)
  • Clear documentation
  • Known bugs and limitations documented
  • Performance characteristics understood
  • Security audit completed

Version 1.0 means:

  • Everything in production-ready, PLUS
  • We promise backward compatibility
  • We won’t break the public API
  • We’re ready to maintain this contract indefinitely

You can be production-ready at 0.1. You can be stable at 0.x. The version number is about the maintainers’ commitment to backward compatibility, not the library’s quality.

I checked PEP 440, the Python version identification standard. It explicitly allows projects to choose semantic versioning aspects but doesn’t require them. The Python ecosystem is pragmatic about this—quality matters more than version numbers.

What This Means for Your Project

After researching this, I realized my own approach to versioning was backwards. I was rushing to 1.0 because I thought it signaled “quality.” But it doesn’t—test coverage, documentation, and stable production deployments signal quality.

Here’s the framework I use now:

flowchart TD
    A[Should I release 1.0?] --> B{Stable API?}
    B -->|No| C[Stay in 0.x]
    B -->|Yes| D{Production use?}
    D -->|No| C
    D -->|Yes| E{Worry about breaks?}
    E -->|No| C
    E -->|Yes| F[Ready for 1.0]

Ask yourself:

  1. Is the API stable? Have you stopped making major changes?
  2. Are users depending on this in production?
  3. Are you worried about breaking changes now?
  4. Have you considered the ecosystem impact?

If the answer is “yes” to all of these, consider 1.0. If any answer is “no,” stay in 0.x. It’s fine.

The checklist I use before 1.0:

  • No planned API changes for the next 12 months
  • At least 6 months of stable production usage
  • Documentation is comprehensive
  • Breaking changes would cause significant ecosystem pain
  • I’m ready to maintain backward compatibility indefinitely

The Real Lesson

Starlette’s 8-year journey to 1.0 isn’t a story of slow development. It’s a story of respecting the ecosystem that depends on your code.

The Encode team could have released 1.0 in 2019 or 2020. They had the quality, the usage, and the stability. But they also had the wisdom to recognize that 1.0 is a contract, not a marketing milestone. By waiting until 2026, they ensured the API they locked down was one they could live with forever.

“8 years to 1.0 feels right”

The Reddit commenter was right. When your code is the foundation for thousands of applications, you don’t rush. You iterate, you learn, you refine, and you only commit to backward compatibility when you’re absolutely certain the API is correct.

Version numbers are for humans, not computers. A 0.x version with 10 million happy users is better than a 1.0 version that breaks things. The best version number is the one your users never have to think about.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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